Code readability

Estimated time: 90 minutes

Overview

Questions

• Why does code readability matter?
• How can I organise my code to be more readable?
• What types of documentation can I include to improve the readability of my code?

Objectives

After completing this episode, participants should be able to:

• Organise code into reusable functions that achieve a singular purpose
• Choose function and variable names that help explain the purpose of the function or variable
• Write informative comments and docstrings to provide more detail about what the code is doing

In this episode, we will introduce the concept of readable code and consider how it can help create reusable scientific software and empower collaboration between researchers.

When someone writes code, they do so based on requirements that are likely to change in the future. Requirements change because software interacts with the real world, which is dynamic. When these requirements change, the developer (who is not necessarily the same person who wrote the original code) must implement the new requirements. They do this by reading the original code to understand the different abstractions, and identify what needs to change. Readable code facilitates the reading and understanding of the abstraction phases and, as a result, facilitates the evolution of the codebase. Readable code saves future developers’ time and effort.

In order to develop readable code, we should ask ourselves: “If I re-read this piece of code in fifteen days or one year, will I be able to understand what I have done and why?” Or even better: “If a new person who just joined the project reads my software, will they be able to understand what I have written here?”

We will now learn about a few software best practices we can follow to help create more readable code. Before we move on with further code modifications, make sure your virtual development environment is active.

Instructor Note

Activate your virtual environment

If it is not already active, make sure to remind learners to activate their virtual environments from the root of the software project directory in command line terminal (e.g. Bash or GitBash):

BASH

$ source venv_spacewalks/bin/activate # Mac or Linux
$ source venv_spacewalks/Scripts/activate # Windows
(venv_spacewalks) $

At this point, the state of the software repository should be as in https://github.com/carpentries-incubator/astronaut-data-analysis-not-so-fair/tree/06-code-readability and the eva_data_analysis.py code should look like as follows:

PYTHON

# https://data.nasa.gov/resource/eva.json (with modifications)
data_f = open('./eva-data.json', 'r')
data_t = open('./eva-data.csv','w')
g_file = './cumulative_eva_graph.png'  

fieldnames = ("EVA #", "Country", "Crew    ", "Vehicle", "Date", "Duration", "Purpose")

data=[]
import json

for i in range(374):
    line=data_f.readline()
    print(line)
    data.append(json.loads(line[1:-1]))
#data.pop(0)
## Comment out this bit if you don't want the spreadsheet
import csv

w=csv.writer(data_t)

import datetime as dt

time = []
date =[]

j=0
for i in data:
    print(data[j])
    # and this bit
    w.writerow(data[j].values())
    if 'duration' in data[j].keys():
        tt=data[j]['duration']
        if tt == '':
            pass
        else:
            t=dt.datetime.strptime(tt,'%H:%M')
            ttt = dt.timedelta(hours=t.hour, minutes=t.minute, seconds=t.second).total_seconds()/(60*60)
            print(t,ttt)
            time.append(ttt)
            if 'date' in data[j].keys():
                date.append(dt.datetime.strptime(data[j]['date'][0:10], '%Y-%m-%d'))
                #date.append(data[j]['date'][0:10])

            else:
                time.pop(0)
    j+=1

t=[0]
for i in time:
    t.append(t[-1]+i)

date,time = zip(*sorted(zip(date, time)))

import matplotlib.pyplot as plt

plt.plot(date,t[1:], 'ko-')
plt.xlabel('Year')
plt.ylabel('Total time spent in space to date (hours)')
plt.tight_layout()
plt.savefig(g_file)
plt.show()

Place import statements at the top

---

Let’s have a look our code again - the first thing we may notice is that our script currently places import statements throughout the code. Conventionally, all import statements are placed at the top of the script so that dependant libraries are clearly visible and not buried inside the code (even though there are standard ways of describing dependencies - e.g. using requirements.txt file). This will help readability (accessibility) and reusability of our code.

Our code after the modification should look like the following.

PYTHON

# https://data.nasa.gov/resource/eva.json (with modifications)
data_f = open('./eva-data.json', 'r')
data_t = open('./eva-data.csv','w')
g_file = './cumulative_eva_graph.png'  

fieldnames = ("EVA #", "Country", "Crew    ", "Vehicle", "Date", "Duration", "Purpose")

data=[]
import json

for i in range(374):
    line=data_f.readline()
    print(line)
    data.append(json.loads(line[1:-1]))
#data.pop(0)
## Comment out this bit if you don't want the spreadsheet
import csv

w=csv.writer(data_t)

import datetime as dt

time = []
date =[]

j=0
for i in data:
    print(data[j])
    # and this bit
    w.writerow(data[j].values())
    if 'duration' in data[j].keys():
        tt=data[j]['duration']
        if tt == '':
            pass
        else:
            t=dt.datetime.strptime(tt,'%H:%M')
            ttt = dt.timedelta(hours=t.hour, minutes=t.minute, seconds=t.second).total_seconds()/(60*60)
            print(t,ttt)
            time.append(ttt)
            if 'date' in data[j].keys():
                date.append(dt.datetime.strptime(data[j]['date'][0:10], '%Y-%m-%d'))
                #date.append(data[j]['date'][0:10])

            else:
                time.pop(0)
    j+=1

t=[0]
for i in time:
    t.append(t[-1]+i)

date,time = zip(*sorted(zip(date, time)))

import matplotlib.pyplot as plt

plt.plot(date,t[1:], 'ko-')
plt.xlabel('Year')
plt.ylabel('Total time spent in space to date (hours)')
plt.tight_layout()
plt.savefig(g_file)
plt.show()

Let’s make sure we commit our changes.

BASH

(venv_spacewalks) $ git add eva_data_analysis.py
(venv_spacewalks) $ git commit -m "Move import statements to the top of the script"

Use meaningful variable names

---

Variables are the most common thing you will assign when coding, and it’s really important that it is clear what each variable means in order to understand what the code is doing. If you return to your code after a long time doing something else, or share your code with a colleague, it should be easy enough to understand what variables are involved in your code from their names. Therefore we need to give them clear names, but we also want to keep them concise so the code stays readable. There are no “hard and fast rules” here, and it’s often a case of using your best judgment.

Some useful tips for naming variables are:

• Short words are better than single character names. For example, if we were creating a variable to store the speed to read a file, s (for ‘speed’) is not descriptive enough but MBReadPerSecondAverageAfterLastFlushToLog is too long to read and prone to misspellings. ReadSpeed (or read_speed) would suffice.
• If you are finding it difficult to come up with a variable name that is both short and descriptive, go with the short version and use an inline comment to describe it further (more on those in the next section). This guidance does not necessarily apply if your variable is a well-known constant in your domain - for example, c represents the speed of light in physics.
• Try to be descriptive where possible and avoid meaningless or funny names like foo, bar, var, thing, etc.

There are also some restrictions to consider when naming variables in Python:

• Only alphanumeric characters and underscores are permitted in variable names.
• You cannot begin your variable names with a numerical character as this will raise a syntax error. Numerical characters can be included in a variable name, just not as the first character. For example, read_speed1 is a valid variable name, but 1read_speed isn’t. (This behaviour may be different for other programming languages.)
• Variable names are case sensitive. So speed_of_light and Speed_Of_Light are not the same.
• Programming languages often have global pre-built functions, such as input, which you may accidentally overwrite if you assign a variable with the same name and no longer be able to access the original input function. In this case, opting for something like input_data would be preferable. Note that this behaviour may be explicitly disallowed in other programming languages but is not in Python.

Give a descriptive name to a variable

Below we have a variable called var being set the value of 9.81. var is not a very descriptive name here as it doesn’t tell us what 9.81 means, yet it is a very common constant in physics! Go online and find out which constant 9.81 relates to and suggest a new name for this variable.

Hint: the units are metres per second squared!

PYTHON

var = 9.81

Show me the solution

Yes, \[9.81 m/s^2 \] is the gravitational force exerted by the Earth. It is often referred to as “little g” to distinguish it from “big G” which is the Gravitational Constant. A more descriptive name for this variable therefore might be:

PYTHON

g_earth = 9.81

Challenge

Let’s apply this to eva_data_analysis.py.

1. Edit the code as follows to use descriptive variable names:

   • Change data_f to input_file
   • Change data_t to output_file
   • Change g_file to graph_file

2. What other variable names in our code would benefit from renaming?

3. Commit your changes to your repository. Remember to use an informative commit message.

Show me the solution

Updated code:

PYTHON

import json
import csv
import datetime as dt
import matplotlib.pyplot as plt

# Data source: https://data.nasa.gov/resource/eva.json (with modifications)
input_file = open('./eva-data.json', 'r')
output_file = open('./eva-data.csv', 'w')
graph_file = './cumulative_eva_graph.png'

fieldnames = ("EVA #", "Country", "Crew    ", "Vehicle", "Date", "Duration", "Purpose")

data=[]

for i in range(374):
    line=input_file.readline()
    print(line)
    data.append(json.loads(line[1:-1]))
#data.pop(0)
## Comment out this bit if you don't want the spreadsheet

w=csv.writer(output_file)

time = []
date =[]

j=0
for i in data:
    print(data[j])
    # and this bit
    w.writerow(data[j].values())
    if 'duration' in data[j].keys():
        tt=data[j]['duration']
        if tt == '':
            pass
        else:
            t=dt.datetime.strptime(tt,'%H:%M')
            ttt = dt.timedelta(hours=t.hour, minutes=t.minute, seconds=t.second).total_seconds()/(60*60)
            print(t,ttt)
            time.append(ttt)
            if 'date' in data[j].keys():
                date.append(dt.datetime.strptime(data[j]['date'][0:10], '%Y-%m-%d'))
                #date.append(data[j]['date'][0:10])

            else:
                time.pop(0)
    j+=1

t=[0]
for i in time:
    t.append(t[-1]+i)

date,time = zip(*sorted(zip(date, time)))

plt.plot(date,t[1:], 'ko-')
plt.xlabel('Year')
plt.ylabel('Total time spent in space to date (hours)')
plt.tight_layout()
plt.savefig(graph_file)
plt.show()

We should also rename variables w, t, ttt to be more descriptive.

Commit changes:

BASH

(venv_spacewalks) $ git add eva_data_analysis.py
(venv_spacewalks) $ git commit -m "Use descriptive variable names"

Use standard libraries

---

Our script currently reads the data line-by-line from the JSON data file and uses custom code to manipulate the data. Variables of interest are stored in lists but there are more suitable data structures (e.g. data frames) to store data in our case. By choosing custom code over standard and well-tested libraries, we are making our code less readable and understandable and more error-prone.

The main functionality of our code can be rewritten as follows using the Pandas library to load and manipulate the data in data frames.

First, we need to install this dependency into our virtual environment (which should be active at this point).

BASH

(venv_spacewalks) $ python -m pip install pandas

The code should now look like:

PYTHON

import matplotlib.pyplot as plt
import pandas as pd

# Data source: https://data.nasa.gov/resource/eva.json (with modifications)
input_file = open('./eva-data.json', 'r')
output_file = open('./eva-data.csv', 'w')
graph_file = './cumulative_eva_graph.png'

eva_df = pd.read_json(input_file, convert_dates=['date'])
eva_df['eva'] = eva_df['eva'].astype(float)
eva_df.dropna(axis=0, inplace=True)
eva_df.sort_values('date', inplace=True)

eva_df.to_csv(output_file, index=False)

eva_df['duration_hours'] = eva_df['duration'].str.split(":").apply(lambda x: int(x[0]) + int(x[1])/60)
eva_df['cumulative_time'] = eva_df['duration_hours'].cumsum()
plt.plot(eva_df['date'], eva_df['cumulative_time'], 'ko-')
plt.xlabel('Year')
plt.ylabel('Total time spent in space to date (hours)')
plt.tight_layout()
plt.savefig(graph_file)
plt.show()

We should replace the existing code in our Python script eva_data_analysis.py with the above code and commit the changes. Remember to use an informative commit message.

BASH

(venv_spacewalks) $ git add eva_data_analysis.py
(venv_spacewalks) $ git commit -m "Refactor code to use standard libraries"

Make sure to capture the changes to your virtual development environment too.

BASH

(venv_spacewalks) $ python -m pip freeze > requirements.txt
(venv_spacewalks) $ git add requirements.txt
(venv_spacewalks) $ git commit -m "Added Pandas library."
(venv_spacewalks) $ git push origin main

Use comments to explain functionality

---

Commenting is a very useful practice to help convey the context of the code. It can be helpful as a reminder for your future self or your collaborators as to why code is written in a certain way, how it is achieving a specific task, or the real-world implications of your code.

There are several ways to add comments to code:

• An inline comment is a comment on the same line as a code statement. Typically, it comes after the code statement and finishes when the line ends and is useful when you want to explain the code line in short. Inline comments in Python should be separated by at least two spaces from the statement; they start with a # followed by a single space, and have no end delimiter.
• A multi-line or block comment can span multiple lines and has a start and end sequence. To comment out a block of code in Python, you can either add a # at the beginning of each line of the block or surround the entire block with three single (''') or double quotes (""").

PYTHON

x = 5  # In Python, inline comments begin with the `#` symbol and a single space.

'''
This is a multiline
comment
in Python.
'''

Here are a few things to keep in mind when commenting your code:

• Focus on the why and the how of your code - avoid using comments to explain what your code does. If your code is too complex for other programmers to understand, consider rewriting it for clarity rather than adding comments to explain it.
• Make sure you are not reiterating something that your code already conveys on its own. Comments should not echo your code.
• Keep comments short and concise. Large blocks of text quickly become unreadable and difficult to maintain.
• Comments that contradict the code are worse than no comments. Always make a priority of keeping comments up-to-date when code changes.

Examples of unhelpful comments

PYTHON

statetax = 1.0625  # Assigns the float 1.0625 to the variable 'statetax'
citytax = 1.01  # Assigns the float 1.01 to the variable 'citytax'
specialtax = 1.01  # Assigns the float 1.01 to the variable 'specialtax'

The comments in this code simply tell us what the code does, which is easy enough to figure out without the inline comments.

Examples of helpful comments

PYTHON

statetax = 1.0625  # State sales tax rate is 6.25% through Jan. 1
citytax = 1.01  # City sales tax rate is 1% through Jan. 1
specialtax = 1.01  # Special sales tax rate is 1% through Jan. 1

In this case, it might not be immediately obvious what each variable represents, so the comments offer helpful, real-world context. The date in the comment also indicates when the code might need to be updated.

Add comments to our code

1. Examine eva_data_analysis.py. Add as many comments as you think is required to help yourself and others understand what that code is doing.
2. Commit your changes to your repository. Remember to use an informative commit message.

Show me the solution

Some good comments may look like the example below.

PYTHON

import matplotlib.pyplot as plt
import pandas as pd


# https://data.nasa.gov/resource/eva.json (with modifications)
input_file = open('./eva-data.json', 'r')
output_file = open('./eva-data.csv', 'w')
graph_file = './cumulative_eva_graph.png'

print("--START--")
print(f'Reading JSON file {input_file}')
# Read the data from a JSON file into a Pandas dataframe
eva_df = pd.read_json(input_file, convert_dates=['date'])
eva_df['eva'] = eva_df['eva'].astype(float)
# Clean the data by removing any incomplete rows and sort by date
eva_df.dropna(axis=0, inplace=True)
eva_df.sort_values('date', inplace=True)

print(f'Saving to CSV file {output_file}')
# Save dataframe to CSV file for later analysis
eva_df.to_csv(output_file, index=False)

print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
# Plot cumulative time spent in space over years
eva_df['duration_hours'] = eva_df['duration'].str.split(":").apply(lambda x: int(x[0]) + int(x[1])/60)
eva_df['cumulative_time'] = eva_df['duration_hours'].cumsum()
plt.plot(eva_df['date'], eva_df['cumulative_time'], 'ko-')
plt.xlabel('Year')
plt.ylabel('Total time spent in space to date (hours)')
plt.tight_layout()
plt.savefig(graph_file)
plt.show()
print("--END--")

Commit changes:

BASH

(venv_spacewalks) $ git add eva_data_analysis.py
(venv_spacewalks) $ git commit -m "Add inline comments to the code"

Separate units of functionality

---

Functions are a fundamental concept in writing software and are one of the core ways you can organise your code to improve its readability. A function is an isolated section of code that performs a single, specific task that can be simple or complex. It can then be called multiple times with different inputs throughout a codebase, but its definition only needs to appear once.

Breaking up code into functions in this manner benefits readability since the smaller sections are easier to read and understand. Since functions can be reused, codebases naturally begin to follow the Don’t Repeat Yourself principle which prevents software from becoming overly long and confusing. The software also becomes easier to maintain because, if the code encapsulated in a function needs to change, it only needs updating in one place instead of many. As we will learn in a future episode, testing code also becomes simpler when code is written in functions. Each function can be individually checked to ensure it is doing what is intended, which improves confidence in the software as a whole.

Callout

Decomposing code into functions helps with reusability of blocks of code and eliminating repetition, but, equally importantly, it helps with code readability and testing.

Looking at our code, you may notice it contains different pieces of functionality:

1. reading the data from a JSON file
2. processing/cleaning the data and preparing it for analysis
3. data analysis and visualising the results
4. converting and saving the data in the CSV format

Let’s refactor our code so that reading the data in JSON format into a dataframe (step 1.) and converting it and saving to the CSV format (step 4.) are extracted into separate functions. Let’s name those functions read_json_to_dataframe and write_dataframe_to_csv respectively. The main part of the script should then be simplified to invoke these new functions, while the functions themselves contain the complexity of each of these two steps.

Our code may look something like the following.

PYTHON

import matplotlib.pyplot as plt
import pandas as pd

def read_json_to_dataframe(input_file):
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'])
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any incomplete rows and sort by date
    eva_df.dropna(axis=0, inplace=True)
    eva_df.sort_values('date', inplace=True)
    return eva_df


def write_dataframe_to_csv(df, output_file):
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False)


# Main code

print("--START--")

input_file = open('./eva-data.json', 'r')
output_file = open('./eva-data.csv', 'w')
graph_file = './cumulative_eva_graph.png'

# Read the data from JSON file
eva_data = read_json_to_dataframe(input_file)

# Convert and export data to CSV file
write_dataframe_to_csv(eva_data, output_file)

print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
# Plot cumulative time spent in space over years
eva_data['duration_hours'] = eva_data['duration'].str.split(":").apply(lambda x: int(x[0]) + int(x[1])/60)
eva_data['cumulative_time'] = eva_data['duration_hours'].cumsum()
plt.plot(eva_data['date'], eva_data['cumulative_time'], 'ko-')
plt.xlabel('Year')
plt.ylabel('Total time spent in space to date (hours)')
plt.tight_layout()
plt.savefig(graph_file)
plt.show()

print("--END--")

We have chosen to create function for reading in and writing out data files since this is a very common task within research software. While these functions do not contain that many lines of code due to using the pandas inbuilt methods that do all the complex data reading, converting and writing operations, it can be useful to package these steps together into reusable functions if you need to read in or write out a lot of similarly structured files and process them in the same way.

Use docstrings to document functions

---

Docstrings are a specific type of documentation that are provided within functions and Python classes. A function docstring should explain what that particular code is doing, what parameters the function needs (inputs) and what form they should take, what the function outputs (you may see words like ‘returns’ or ‘yields’ here), and errors (if any) that might be raised.

Providing these docstrings helps improve code readability since it makes the function code more transparent and aids understanding. Particularly, docstrings that provide information on the input and output of functions makes it easier to reuse them in other parts of the code, without having to read the full function to understand what needs to be provided and what will be returned.

Python docstrings are defined by enclosing the text with 3 double quotes ("""). This text is also indented to the same level as the code defined beneath it, which is 4 whitespaces by convention.

Example of a single-line docstring

PYTHON

def add(x, y):
    """Add two numbers together"""
    return x + y

Example of a multi-line docstring

PYTHON

def divide(x, y):
    """
    Divide number x by number y.

    Args:
        x: A number to be divided.
        y: A number to divide by.

    Returns:
        float: The division of x by y.
        
    Raises:
        ZeroDivisionError: Cannot divide by zero.
    """
    return x / y

Some projects may have their own guidelines on how to write docstrings, such as numpy. If you are contributing code to a wider project or community, try to follow the guidelines and standards they provide for code style.

As your code grows and becomes more complex, the docstrings can form the content of a reference guide allowing developers to quickly look up how to use the APIs, functions, and classes defined in your codebase. Hence, it is common to find tools that will automatically extract docstrings from your code and generate a website where people can learn about your code without downloading/installing and reading the code files - such as MkDocs.

Let’s write a docstring for the function read_json_to_dataframe we introduced in the previous exercise using the Google Style Python Docstrings Convention. Remember, questions we want to answer when writing the docstring include:

• What the function does?
• What kind of inputs does the function take? Are they required or optional? Do they have default values?
• What output will the function produce?
• What exceptions/errors, if any, it can produce?

Our read_json_to_dataframe function fully described by a docstring may look like:

PYTHON

def read_json_to_dataframe(input_file):
    """
    Read the data from a JSON file into a Pandas dataframe.
    Clean the data by removing any incomplete rows and sort by date

    Args:
        input_file_ (str): The path to the JSON file.

    Returns:
         eva_df (pd.DataFrame): The cleaned and sorted data as a dataframe structure
    """
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'])
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any incomplete rows and sort by date
    eva_df.dropna(axis=0, inplace=True)
    eva_df.sort_values('date', inplace=True)
    return eva_df

Writing docstrings

Write a docstring for the function write_dataframe_to_csv we introduced earlier.

Show me the solution

Our write_dataframe_to_csv function fully described by a docstring may look like:

PYTHON

def write_dataframe_to_csv(df, output_file):
"""
Write the dataframe to a CSV file.

    Args:
        df (pd.DataFrame): The input dataframe.
        output_file (str): The path to the output CSV file.

    Returns:
        None
    """
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False)

Finally, our code may look something like the following:

PYTHON

import matplotlib.pyplot as plt
import pandas as pd


def read_json_to_dataframe(input_file):
    """
    Read the data from a JSON file into a Pandas dataframe.
    Clean the data by removing any incomplete rows and sort by date

    Args:
        input_file_ (str): The path to the JSON file.

    Returns:
         eva_df (pd.DataFrame): The cleaned and sorted data as a dataframe structure
    """
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'])
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any incomplete rows and sort by date
    eva_df.dropna(axis=0, inplace=True)
    eva_df.sort_values('date', inplace=True)
    return eva_df


def write_dataframe_to_csv(df, output_file):
    """
    Write the dataframe to a CSV file.

    Args:
        df (pd.DataFrame): The input dataframe.
        output_file (str): The path to the output CSV file.

    Returns:
        None
    """
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False)


# Main code

print("--START--")

input_file = open('./eva-data.json', 'r')
output_file = open('./eva-data.csv', 'w')
graph_file = './cumulative_eva_graph.png'

# Read the data from JSON file
eva_data = read_json_to_dataframe(input_file)

# Convert and export data to CSV file
write_dataframe_to_csv(eva_data, output_file)

print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
# Plot cumulative time spent in space over years
eva_data['duration_hours'] = eva_data['duration'].str.split(":").apply(lambda x: int(x[0]) + int(x[1])/60)
eva_data['cumulative_time'] = eva_data['duration_hours'].cumsum()
plt.plot(eva_data['date'], eva_data['cumulative_time'], 'ko-')
plt.xlabel('Year')
plt.ylabel('Total time spent in space to date (hours)')
plt.tight_layout()
plt.savefig(graph_file)
plt.show()

print("--END--")

Do not forget to commit any uncommitted changes you may have and then push your work to GitHub.

BASH

(venv_spacewalks) $ git add <your_changed_files>
(venv_spacewalks) $ git commit -m "Your commit message"
(venv_spacewalks) $ git push origin main

Further reading

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We recommend the following resources for some additional reading on the topic of this episode:

• 7 tell-tale signs of unreadable code
• ‘Code Readability Matters’ from the Guardian’s engineering blog
• PEP 8 Style Guide for Python
• Coursera: Inline commenting in Python
• Introducing Functions from Introduction to Python
• W3Schools.com Python Functions

Also check the full reference set for the course.

Key Points

• Readable code is easier to understand, maintain, debug and extend (reuse) - saving time and effort.
• Choosing descriptive variable and function names will communicate their purpose more effectively.
• Using comments and docstrings to describe parts of the code will help transmit understanding and context.
• Use libraries or packages for common functionality to avoid duplication.
• Creating functions from the smallest, reusable units of code will make the code more readable and help. compartmentalise which parts of the code are doing what actions and isolate specific code sections for re-use.