Creating re-usable code

Last updated on 2023-05-04 | Edit this page

Estimated time: 40 minutes

Overview

Questions

  • What are user defined functions?
  • How can I automate my code for re-use?

Objectives

  • Describe the syntax for a user defined function
  • Create and use simple functions
  • Explain the advantages of using functions

Defining a function

We have already made use of several Python builtin functions like print, list and range.

In addition to the functions provided by Python, you can write your own functions.

Functions are used when a section of code needs to be repeated at various different points in a program. It saves you re-writing it all. In reality you rarely need to repeat the exact same code. Usually there will be some variation in variable values needed. Because of this, when you create a function you are allowed to specify a set of parameters which represent variables in the function.

In our use of the print function, we have provided whatever we want to print, as a parameter. Typically whenever we use the print function, we pass a different parameter value.

The ability to specify parameters make functions very flexible.

PYTHON 

def get_item_count(items_str,sep):
    '''
    This function takes a string with a list of items and the character that they're separated by and returns the number of items
    '''
    items_list = items_str.split(sep)
    num_items = len(items_list)
    return num_items

items_owned = "bicycle;television;solar_panel;table"
print(get_item_count(items_owned,';'))

OUTPUT 

4
Python_Repl

Points to note:

  1. The definition of a function (or procedure) starts with the def keyword and is followed by the name of the function with any parameters used by the function in parentheses.
  2. The definition clause is terminated with a : which causes indentation on the next and subsequent lines. All of these lines form the statements which make up the function. The function ends after the indentation is removed.
  3. Within the function, the parameters behave as variables whose initial values will be those that they were given when the function was called.
  4. functions have a return statement which specifies the value to be returned. This is the value assigned to the variable on the left-hand side of the call to the function. (power in the example above)
  5. You call (run the code) of a function simply by providing its name and values for its parameters the same way you would for any builtin function.
  6. Once the definition of the function has been executed, it becomes part of Python for the current session and can be used anywhere.
  7. Like any other builtin function you can use shift + tab in Jupyter to see the parameters.
  8. At the beginning of the function code we have a multiline comment denoted by the ''' at the beginning and end. This kind of comment is known as a docstring and can be used anywhere in Python code as a documentation aid. It is particularly common, and indeed best practice, to use them to give a brief description of the function at the beginning of a function definition in this way. This is because this description will be displayed along with the parameters when you use the help() function or shift + tab in Jupyter.
  9. The variable x defined within the function only exists within the function, it cannot be used outside in the main program.

In our get_item_count function we have two parameters which must be provided every time the function is used. You need to provide the parameters in the right order or to explicitly name the parameter you are referring to and use the = sign to give it a value.

In many cases of functions we want to provide default values for parameters so the user doesn’t have to. We can do this in the following way

PYTHON 

def get_item_count(items_str,sep=';'):
    '''
    This function takes a string with a list of items and the character that they're separated by and returns the number of items
    '''
    items_list = items_str.split(sep)
    num_items = len(items_list)
    return num_items


print(get_item_count(items_owned))

OUTPUT 

4

The only change we have made is to provide a default value for the sep parameter. Now if the user does not provide a value, then the value of 2 will be used. Because items_str is the first parameter we can specify its value by position. We could however have explicitly named the parameters we were referring to.

PYTHON 

print(get_item_count(items_owned, sep = ','))
print(get_item_count(items_str = items_owned, sep=';'))

OUTPUT 

1
4

Volume of a cube

  1. Write a function definition to calculate the volume of a cuboid. The function will use three parameters h, w and l and return the volume.

  2. Supposing that in addition to the volume I also wanted to calculate the surface area and the sum of all of the edges. Would I (or should I) have three separate functions or could I write a single function to provide all three values together?

  • A function to calculate the volume of a cuboid could be:

PYTHON 

def calculate_vol_cuboid(h, w, len):
    """
    Calculates the volume of a cuboid.
    Takes in h, w, len, that represent height, width, and length of the cube.
    Returns the volume.
    """
    volume = h * w * len
    return volume
  • It depends. As a rule-of-thumb, we want our function to do one thing and one thing only, and to do it well. If we always have to calculate these three pieces of information, the ‘one thing’ could be ‘calculate the volume, surface area, and sum of all edges of a cube’. Our function would look like this:

PYTHON 

# Method 1 - single function
def calculate_cuboid(h, w, len):
    """
    Calculates information about a cuboid defined by the dimensions h(eight), w(idth), and len(gth).

    Returns the volume, surface area, and sum of edges of the cuboid.
    """
    volume = h * w * len
    surface_area = 2 * (h * w + h * len + len * w)
    edges = 4 * (h + w + len)
    return volume, surface_area, edges

It may be better, however, to break down our function into separate ones - one for each piece of information we are calculating. Our functions would look like this:

PYTHON 

# Method 2 - separate functions
def calc_volume_of_cuboid(h, w, len):
    """
    Calculates the volume of a cuboid defined by the dimensions h(eight), w(idth), and len(gth).
    """
    volume = h * w * len
    return volume


def calc_surface_area_of_cuboid(h, w, len):
    """
    Calculates the surface area of a cuboid defined by the dimensions h(eight), w(idth), and len(gth).
    """   
    surface_area = 2 * (h * w + h * len + len * w)
    return surface_area


def calc_sum_of_edges_of_cuboid(h, w, len):
    """
    Calculates the sum of edges of a cuboid defined by the dimensions h(eight), w(idth), and len(gth).
    """   
    sum_of_edges = 4 * (h + w + len)
    return sum_of_edges

We could then rewrite our first solution:

PYTHON 

def calculate_cuboid(h, w, len):
    """
    Calculates information about a cuboid defined by the dimensions h(eight), w(idth), and len(gth).

    Returns the volume, surface area, and sum of edges of the cuboid.
    """
    volume = calc_volume_of_cuboid(h, w, len)
    surface_area = calc_surface_area_of_cuboid(h, w, len)
    edges = calc_sum_of_edges_of_cuboid(h, w, len)

    return volume, surface_area, edges

Using libraries

The functions we have created above only exist for the duration of the session in which they have been defined. If you start a new Jupyter notebook you will have to run the code to define them again.

If all of your code is in a single file or notebook this isn’t really a problem.

There are however many (thousands) of useful functions which other people have written and have made available to all Python users by creating libraries (also referred to as packages or modules) of functions.

You can find out what all of these libraries are and their contents by visiting the main (python.org) site.

We need to go through a 2-step process before we can use them in our own programs.

Step 1. use the pip command from the commandline. pip is installed as part of the Python install and is used to fetch the package from the Internet and install it in your Python configuration.

BASH 

$ pip install <package name>

pip stands for Python install package and is a commandline function. Because we are using the Anaconda distribution of Python, all of the packages that we will be using in this lesson are already installed for us, so we can move straight on to step 2.

Step 2. In your Python code include an import package-name statement. Once this is done, you can use all of the functions contained within the package.

As all of these packages are produced by 3rd parties independently of each other, there is the strong possibility that there may be clashes in function names. To allow for this, when you are calling a function from a package that you have imported, you do so by prefixing the function name with the package name. This can make for long-winded function names so the import statement allows you to specify an alias for the package name which you must then use instead of the package name.

In future episodes, we will be importing the csv, json, pandas, numpy and matplotlib modules. We will describe their use as we use them.

The code that we will use is shown below

PYTHON 

import csv
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

The first two we don’t alias as they have short names. The last three we do. Matplotlib is a very large library broken up into what can be thought of as sub-libraries. As we will only be using the functions contained in the pyplot sub-library we can specify that explicitly when we import. This saves time and space. It does not effect how we call the functions in our code.

The alias we use (specified after the as keyword) is entirely up to us. However those shown here for pandas, numpy and matplotlib are nearly universally adopted conventions used for these popular libraries. If you are searching for code examples for these libraries on the Internet, using these aliases will appear most of the time.

Key Points

  • Functions are used to create re-usable sections of code
  • Using parameters with functions make them more flexible
  • You can use functions written by others by importing the libraries containing them into your code