Chapter 2 Functions

This chapter will explore how to use and create functions in Python. Functions are the primary form of behavior abstraction in computer programming, and are used to structure and generalize code instructions. After considering a function in an abstract sense, it will look at how to use built-in Python functions, how to access additional functions by importing Python modules, and finally how to write your own functions.

2.1 What are Functions?

In a broad sense, a function is a named sequence of instructions (lines of code) that you may want to perform one or more times throughout a program. They provide a way of encapsulating multiple instructions into a single “unit” that can be used in a variety of different contexts. So rather than needing to repeatedly write down all the individual instructions for “make a sandwich” every time you’re hungry, you can define a make_sandwich() function once and then just call (execute) that function when you want to perform those steps.

In addition to grouping instructions, functions in programming languages like Python also follow the mathematical definition of functions, which is a set of operations (instructions!) that are performed on some inputs and lead to some outputs. Functions inputs are called arguments or parameters, and we say that these arguments are passed to a function (like a football). We say that a function then returns an ouput for us to use.

2.2 Python Function Syntax

Python functions are referred to by name (technically they are values like any other variable). As in many programming languages, we call a function by writing the name of the function followed immediately (no space) by parentheses (). Inside the parentheses, we put the arguments (inputs) to the function separated by commas. Thus computer functions look just like mathematical functions, but with names longer than f().

# call the print() function, pass it "Hello world" value as an argument
print("Hello world")  # "Hello world"

# call the str() function, pass it 598 as an argument
str(598)  # "598"

# call the len() function, pass it "python" as an argument
len("python")  # 6 (the word is 6 letters long)

# call the round() function, pass it 3.1415 as the first arg and 2 as the second
# this is an example of a function that takes multiple (ordered) args
round(3.1415, 2)  # 3.14, (3.1415 rounded to 2 decimal places)

# call the min() function, pass it 1, 6/8, AND 4/3 as arguments
# this is another example of a function that takes multiple args
min(1, 6/8, 4/3)  # 0.75, (6/8 is the smallest value)
  • Note: To keep functions and variables distinct, we try to always include empty parentheses () when referring to a function name. This does not mean that the function takes no arguments; it is just a useful shorthand for indicating that something is a function rather than a variable.

Some functions (such as min() or print()) can be passed as many arguments as you wish: min() will find the minimum of all the arguments, and print() will print all the arguments (in order), separated by spaces:

# print() with 3 arguments instead of 1
print("I", "love", "programming")  # "I love programming"

Besides ordered positional arguments, functions may also take keyword arguments, which are arguments for specific function inputs. These are written like variable assignments (using the =, though without spaces around it), but within the function parameter list:

# Use the `sep` keyword argument to specify the separator is '+++'
print("Hello", "World", sep='+++')  # "Hello+++World"

Keyword arguments are always optional (they have “default” values, like how the separator for print() defaults to a single space ' '). The default values are specified in the function documentation (e.g., for print()).

If you call any of these functions interactively (e.g., in an interactive shell or a Jupyter notebook), Python will display the returned value. However, the computer is not able to “read” what is written to the console or an output cell—that’s for humans to view! If we want the computer to be able to use a returned value, we will need to give that value a name so that the computer can refer to it. That is, we need to store the returned value in a variable:

# store min value in smallest_number variable
smallest_number = min(1, 6/8, 4/3, 5+9)

# we can then use the variable as normal, such as mathematical operations
twice_min = smallest_number * 2  # 1.5

# we can use functions directly in expressions (the returned value is anonymous)
number = .5 * round(9.8)  # 5.0

# we can even pass the result of a function as an argument to another!
# watch out for where the parentheses close!
print(min(2.0, round(1.4)))  # prints 1

2.2.1 Object Methods

In Python, all data values are objects, which are groups of data (called attributes) and behaviors—that is, information about the values and the functions that can be applied to that data. For example, a Person object may have a name (e.g., "Ada") and some behavior it can do to that data (e.g., say_name()). Functions that are applied to an object’s data are also known as methods. We say that a method is called on that object.

While we’ll discuss objects in more much detail later, for now you just need to understand that some functions are called on particular values. This is done using dot notation: you write the name of the variable you wish to call the method on (i.e., apply the function to), followed by a period (dot) ., followed by the method name and arguments:

message = "Hello World"

# call the lower() method on the message to make a lowercase version
# Note that the original string does not change (strings are immutable)
lower_message = message.lower()  # "hello world"

# call the replace() method on the message
western_message = message.replace("Hello", "Howdy")  # "Howdy World"

This is a common way of utilizing built-in Python functions.

  • Note that dot notation is also used to access the attributes or properties of an object. So if a Person object has a name attribute, you would refer to that as In this sense, you can think of the dot operator as being like the possessive 's in English: refers so “the_person’s name”, and the_person.say_name() would refer to “the_person’s say_name() action”.

2.3 Built-in Python Functions

As you may have noticed, Python comes with a large number of functions that are built into the language. In the above examples, we used the print() function to print a value to the console, the min() function to find the smallest number among the arguments, and the round() function to round to whole numbers. Similarly, each data type in the language comes with their own set of methods: such as the lower() and replace() methods on strings.

These functions are all described in the official documentation. For example, you can find a list of built-in functions (though most of them will not be useful for us), as well as a list of string methods. To learn more about any individual function as well as what functions are available, look it up in the documentation. You can also use the help() function to look up the details of other functions: help(print) will look up information on the print() function.

Important: “Knowing” how to program in a language is to some extent simply “knowing” what provided functions are available in that language. Thus you should look around and become familiar with these functions… but do not feel that you need to memorize them! It’s enough to simply be aware “oh yeah, there was a function that rounded numbers”, and then be able to look up the name and arguments for that function.

2.3.1 Modules and Libraries

While Python has lots of built-in functions, many of them are not immediately available for use. Instead, these functions are organized into modules, which are collections of related functions and variables. You must specifically load a module into your program in order to access it’s functions; this helps to reduce the amount of memory that the Python interpreter needs to use in order to keep track of all of the functions available.

You load a module (make it available to your program) by using the import keyword, followed by the name of the module. This only needs to be done once per script execution, and so is normally done at the “top” of the script (in a Jupyter notebook, you can include an “importing” code cell, or import the module at the top of the cell in which you first need it):

# load the math module, which contains mathematical functions
import math

# call the math module's sqrt() function to calculate square root
math.sqrt(25)  # 5.0, (square root of 25)

# print out the math modules `pi` variable
print(math.pi)  # 3.141592653589793

Notice that we again use dot notation to call functions of a particular module. Again, think of the . as a possessive 's (“the math module’s sqrt function”).

It is also possible to import only select functions or variables from a module by using the syntax from MODULE import FUNCTION. This will load the specific functions or variables into the global scope, allowing you to call them without specifying the module:

# import the sqrt() function from the math module
from math import sqrt

# call the imported sqrt() function
sqrt(25)  # 5.0

# import multiple values by separating them with commas
from math import sin, cos, pi

sin(pi/2)  # 1.0
cos(pi/2)  # 6.123233995736766e-17; 0 but with precision errors

# import everything from math
# this is useful for module with long or confusing names
from math import *

The collection of built-in modules such as math make up what is called the standard library of Python functions. However, it’s also possible to download and import additional modules written and published by the Python community—what are known as libraries or packages. Because many Python users encounter the same challenges, programmers are able to benefit from the work of other and reuse solutions. This is the amazing thing about the open-source community: people solve problems and then make those solutions available to others.

A large number of additional libraries are included with Anaconda. Anaconda also comes with a command line utility conda that can be used to easily download and install new libraries. For example if you needed to install Jupyter, you could use the command conda install jupyter from the command line.

Python also comes with a command line utility called pip for (“pip installs packages”) that can similarly be used to easily download and install packages. Note that you may need to set up a virtual environment to effectively use pip; thus I recommend you rely on conda instead—though the Anaconda package has everything you will need for this course.

2.4 Writing Functions

Even more common than loading other peoples’ functions is writing your own. Functions are the primary way that we abstract program instructions, acting sort of like “mini programs” inside your larger script. As Downey says:

Their primary purpose is to help us organize programs into chunks that match how we think about the problem.

Any time you want to organize your thinking—or if you have a task that you want to repeat through the script—it’s good practice to write a function to perform that task. This will make your program easier to understand and build, as well as limit repetition and reduce the likelihood of error.

The best way to understand the syntax for defining a function is to look at an example:

# A function named `MakeFullName` that takes two arguments
# and returns the "full name" made from them
def make_full_name(first_name, last_name):
    # Function body: perform tasks in here
    full_name = first_name + " " + last_name

    # Return: what you want the function to output
    return full_name

# Call the make_ful_name function with the values "Alice" and "Kim"
my_name = make_full_name("Alice", "Kim")  # "Alice Kim"

Functions have a couple of pieces to them:

  • def: Functions are defined by using the def keyword, which indicates that you are defining a function rather than a regular variable. This is followed by the name of the function, then a set of parentheses containing the arguments (Note that the function_name(arguments) syntax mirrors how functions are called).

    The line ends with a colon : which starts the function body (see below).

  • Arguments: The values put between the parentheses in the function definition line are variables that will contain the values passed in as arguments. For example, when we call make_full_name("Alice","Kim"), the value of the first argument ("Alice") will be assigned to the first variable (first_name), and the value of the second argument ("Kim") will be assigned to the second variable (last_name).

    Importantly, we could have made the argument names anything we wanted (name_first, given_name, etc.), just as long as we then use that variable name to refer to the argument while inside the function. Moreover, these argument variable names only apply while inside the function (they are scoped to the function). You can think of them like function-specific “nicknames” for the values. The variables first_name, last_name, and full_name only exist within this particular function.

    Note that a function may have no arguments, causing the parentheses to be empty:

    def say_hello():
      print("Hello world!")

    You can give a function keyword arguments by assigning a default value to the argument in the function definition:

    # includes a single keyword argument
    def greet(greeting = "Hello"):
      print(greeting + " world")
    # call by assigning to the arg
    greet(greeting="Hi")  # "Hi world"
    # call without assigning an argument, using the default value
    greet()  # "Hello world"
  • Body: The body of the function is a block of code. The function definition ends with a colon : to indicate that it is followed by a block, which is a list of Python statements (lines of code). Which statements are part of the block are indicated by indentation: statements are indented by 4 spaces to make them part of that particular block. A block can contain as many lines of code as you want—you’ll usually want more than 1 to make a function worthwhile, but if you have more than 20 you might want to break your code up into separate functions. You can use the argument variables in here, create new variables, call other functions… basically any code that you would write outside of a function can be written inside of one as well!

  • Return value: You can specify what output a function produces by using the return keyword, followed by the value that you wish your function to return (output). A return statement will end the current function and return the flow of code execution to where ever this function was called from. Note that even though we returned a variable called full_name, that variable was local to the function and so doesn’t exist outside of it; thus we have to take the returned value and assign it to a new variable (as with my_name = make_full_name("Alice", "Kim")).

    Because the return statement exits the function, it is almost always the last line of code in the function; any statements after a return statement will not be run!

    • A function does not need to return a value (as in the say_hello() example above). In this case, omit the return statement.

We can call (execute) a function we defined the same way we called built-in functions. When we do so, Python will take the arguments we passed in (e.g., "Alice" and "Kim") and assign them to the argument variables. Then the interpreter executes each line of code in the function body one at a time. When it gets to the return statement, it will end the function and return the given value, which can then be assigned to a different variable (outside of the function).

2.4.1 Doc Strings

We create new functions as ways of abstracting behavior, in order to make programs easier to read and understand. Thus it is important to be clear about the purpose and use of any functions you create. This can partially done through effective function and argument names: def calc_rectangle_area(width, height) is pretty self-explanatory, whereas def my_func(a, b, c) isn’t).

Style requirement: Name functions as phrases starting with verbs (because function do things), and variables as nouns.

Nevertheless, in order to make sure that functions are as clear as possible, you should include documentation in the form of a comment that describes in plain English what a function does: its inputs (arguments) once, output (return value), and overall behavior. In Python we document functions by providing a doc string. This is a string literal (written in multi-line triple quotes """) placed immediately below the function declaration:

def to_celsius(degrees_fahrenheit):
    """Converts the given degrees (in Fahrenheit) to degrees in Celsius, and
       returns the result.
    celsius = (degrees_fahrenheit - 32)*(5/9)
    return celsius
  • Note that this string isn’t assigned to a variable; it is treated as a valid statement because it is immediately below the function declaration.

  • You can view the doc string by calling the help() function with your function’s name as the parameter! In fact, when you call help() on any built-in function, what you are viewing is that function’s doc string.

Doc strings should include the following information:

  1. What the function does from the perspective of someone who would call the function. This should be a short (1-2 sentence) summary; don’t describe the individual instructions that are executed, but an abstraction of the code’s purpose.

    • If you mention variables, operations, functions, or control structures, your comment isn’t at a high enough level of abstraction!
  2. Descriptions of the expected arguments. Clarify any ambiguities in type (e.g., a number or a string) and units.

  3. Description of the returned value (if any). Explain the meaning, type (e.g., number or string), etc.

It’s good to think of doc strings as defining a “contract”: you are specifying that “if you give the function this set of inputs, it will perform this behavior and give you this output.”.

For simple methods you can build this information into a single sentence as in the above example. But for more complex functions, you may need to use a more complex format for your doc string. See the Google Style Guide for an example.