Streamline previous content
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"\n",
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"At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be re-using the same parts inside core Python every time we use them.\n",
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"\n",
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"This chapter shows how Python offers language constructs that let us **define** our own functions that we can then **call** just like the built-in ones."
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"This chapter shows how Python offers language constructs that let us **define** our own functions that we may then **call** just like the built-in ones."
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}
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"source": [
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"So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** can be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
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"So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
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"\n",
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"A function's **name** must be chosen according to the same naming rules as for ordinary variables. In fact, Python manages function names just like variables. In this book, we further adopt the convention of ending function names with parentheses \"`()`\" in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are not actually part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
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"\n",
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"Functions may define an arbitrary number of **parameters** as inputs that can then be referenced within the indented **code block**: They are simply listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
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"Functions may define an arbitrary number of **parameters** as inputs that are then referenced within the indented **code block**: They are simply listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
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"\n",
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"The code block is often also called a function's **body** while the first line with the `def` in it is the **header** and must end with a colon.\n",
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"\n",
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@ -92,7 +92,7 @@
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}
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"source": [
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"Once defined, a function can be referenced just like any other variable by its name (i.e., *without* the parenthesis). Its value might seem awkward at first: It consists of the location where we defined the function (i.e., `__main__`, which is Python's way of saying \"in this notebook\") and the signature."
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"Once defined, a function may be referenced just like any other variable by its name (i.e., *without* the parenthesis). Its value might seem awkward at first: It consists of the location where we defined the function (i.e., `__main__`, which is Python's way of saying \"in this notebook\") and the signature."
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"source": [
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"We can **call** (i.e., \"execute\") a function with the **call operator** `()` as often as we wish. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
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"We **call** (i.e., \"execute\") a function with the **call operator** `()` as often as we wish. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
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@ -863,7 +863,7 @@
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"\n",
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"Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
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"\n",
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"While this is not a problem for Python as we have observed, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well and we have to be prepared to encounter shadowing variables."
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"While this is not a problem for Python, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well and we have to be prepared to encounter shadowing variables."
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"source": [
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"We can cast certain objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. This actually creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects who continue to exist in memory unchanged."
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"We may cast certain objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. This actually creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects who continue to exist in memory unchanged."
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"source": [
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"Not all conversions are valid and *runtime* errors can occur as the `ValueError` shows."
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"Not all conversions are valid and *runtime* errors may occur as the `ValueError` shows."
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"source": [
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"We can also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in function."
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"We may also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in function."
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"source": [
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"As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we can pass in the arguments by position."
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"As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we pass in the arguments by position."
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"source": [
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"However, now the function call is a bit harder to comprehend as we need to always remember what the `2` means. This becomes even harder the more parameters we specify.\n",
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"\n",
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"Luckily, we can also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the exact same thing."
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"Luckily, we may also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the exact same thing."
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"source": [
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"Now we can call the function either with or without the `scalar` argument.\n",
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"Now we call the function either with or without the `scalar` argument.\n",
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"\n",
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"If `scalar` is passed in, this can be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is easier to comprehend in a large program?"
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"If `scalar` is passed in, this may be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is easier to comprehend in a large program?"
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"source": [
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"Now we can call `add_three()` as if we defined it with the `def` statement to begin with."
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"Now we call `add_three()` as if we defined it with the `def` statement to begin with."
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"\n",
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"Let's see what we can do with the `math` module.\n",
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"\n",
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"The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function can also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of ... names. As we cannot know at this point in time if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
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"The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function may also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of ... names. As we cannot know at this point in time if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
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"source": [
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"Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we can pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
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"Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
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"\n",
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"So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We can execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
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"So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
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"\n",
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"So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
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}
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"source": [
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"If we only need one particular function from a module, we can also use the alternative `from ... import ...` syntax.\n",
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"If we only need one particular function from a module, we may also use the alternative `from ... import ...` syntax.\n",
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"\n",
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"This does *not* create a module object but only makes a variable in our current location point to an object defined inside a module directly."
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}
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"source": [
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"Besides the usual dunder-style attributes, the [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function lists some attributes in an upper case naming convention and many others starting with a single underscore \"\\_\". To understand the former, we have to wait until Chapter 10 while the latter are explained further below."
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"Besides the usual dunder-style attributes, the [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function lists some attributes in an upper case naming convention and many others starting with a single underscore `_`. To understand the former, we have to wait until Chapter 10 while the latter are explained further below."
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"execution_count": 75,
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{
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"<bound method Random.choice of <random.Random object at 0x56111ba1cba8>>"
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"<bound method Random.choice of <random.Random object at 0x56063d20aba8>>"
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]
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"execution_count": 76,
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{
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"data": {
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"2"
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"7"
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"execution_count": 78,
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}
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"source": [
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"In order to re-produce the same random numbers in a simulation each time we run it, we can set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we will get the exact same random numbers again. This becomes very important, for example, when we employ certain machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **re-producable** results.\n",
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"In order to re-produce the same random numbers in a simulation each time we run it, we set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we will get the exact same random numbers again. This becomes very important, for example, when we employ certain machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **re-producable** results.\n",
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"\n",
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"The [random](https://docs.python.org/3/library/random.html) module provides the [random.seed()](https://docs.python.org/3/library/random.html#random.seed) function to do that."
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}
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"source": [
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"As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone can download and install it for free using command line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is actually quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
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"As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone may download and install it for free using command line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is actually quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
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"\n",
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"The difference between the [standard library](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and a lot of such software is actually also relied on by many businesses and researchers.\n",
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"\n",
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}
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"source": [
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"`np` can be used in the same way as `math` or `random` above."
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"`np` is used in the same way as `math` or `random` above."
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]
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}
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"source": [
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"[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we can now [scalar-multiply](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
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"[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we may now [scalar-multiply](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
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"\n",
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"[numpy](http://www.numpy.org/)'s functions are implemented in highly optimized C code and therefore fast, especially when it comes to big data."
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"source": [
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"Disregarding the dunder-style attributes, `mod` defines the five attributes `_default_scalar`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file.\n",
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"\n",
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"An important convention when working with imported code is to *disregard* any attributes starting with an underscore \"\\_\". These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
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"An important convention when working with imported code is to *disregard* any attributes starting with an underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
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"\n",
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"In contrast, the three remaining **public** attributes are the functions `average()`, `average_evens()`, and `average_odds()` that we may use after the import."
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}
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"source": [
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"We can use the imported `mod.average_evens()` just like `average_evens()` defined above. The advantage we get from **modularization** with *.py* files is that we can now easily re-use functions across different Jupyter notebooks without re-defining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
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"We use the imported `mod.average_evens()` just like `average_evens()` defined above. The advantage we get from **modularization** with *.py* files is that we can now easily re-use functions across different Jupyter notebooks without re-defining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
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}
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"source": [
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"Packages are a generalization of modules and we will look at one in Chapter 10 in detail. You can, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
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"Packages are a generalization of modules and we will look at one in Chapter 10 in detail. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
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"\n",
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"As a further references on modules, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
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"- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a code base), and\n",
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"- eliminate redundancies by allowing **re-use of code**.\n",
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"\n",
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"Functions are **defined** once with the `def` statement. Then, they can be **called** many times with the call operator `()`.\n",
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"Functions are **defined** once with the `def` statement. Then, they may be **called** many times with the call operator `()`.\n",
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"\n",
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"They may process **parameterized** inputs, **passed** in as **arguments**, and output a **return value**.\n",
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"\n",
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"Arguments can be passed in by **position** or **keyword**. Some functions may even require **keyword-only** arguments.\n",
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"Arguments may be passed in by **position** or **keyword**. Some functions may even require **keyword-only** arguments.\n",
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"\n",
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"**Lambda expressions** create anonymous functions.\n",
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"\n",
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