tl;dr
This tutorial will help you gain a solid understanding of the PEP8 style guide for writing clean, professional Python code.

Overview

Welcome to the tutorial on writing PEP8 compliant Python code. PEP8 is the official style guide for Python, outlining best practices and conventions for formatting your code. Adhering to PEP8 recommendations can make your code more readable, maintainable, and consistent, fostering collaboration and easier code reviews.

In this tutorial, we will cover the following topics:

• Introduction to PEP8: Understand the significance of the PEP8 style guide and why it's considered the gold standard in Python code formatting.
• Key PEP8 Recommendations: Dive into the main tenets of PEP8 such as naming conventions, indentation, line length, whitespace, and more. This section will provide insights into the conventions and their importance in writing clean code.
• Using Linters and Formatters to Enforce PEP8: Learn about tools like flake8, pylint, and black that help in checking and ensuring that your code is PEP8 compliant. This section will be filled with hands-on examples, showcasing how these tools can be integrated into your coding workflow.
• Integrating PEP8 Checks into Development Workflow: Discover the benefits of having automated PEP8 checks as part of your continuous integration process, ensuring code quality from the onset.
• Customizing PEP8 to Fit Team and Project Needs: Understand how you can customize PEP8 rules to better align with your team's coding style and project requirements.
• Balancing PEP8 Recommendations with Practicality: While PEP8 is a great guide, there are times when deviations are necessary. In this section, we'll discuss how to strike a balance between sticking to the style guide and ensuring code readability and efficiency in real-world scenarios.

By the end of this tutorial, you will have a solid understanding of the PEP8 style guide and its recommendations for writing clean, professional Python code. You will also gain practical experience using linters and formatters to enforce PEP8 compliance in your projects, ensuring your code is consistently well-organized and easy to read. Let's get started!

Introduction to PEP8

Python, as a programming language, has gained widespread popularity among data scientists and machine learning engineers due to its simplicity and readability. PEP8, the official style guide for Python code, plays a significant role in maintaining this readability by providing a set of conventions that developers can follow when writing Python code. Adhering to these conventions ensures that the code is consistent, clean, and easy to understand, making it more maintainable and accessible for collaboration.

PEP8 is particularly important for data scientists and ML engineers working in teams, as it helps create a standardized codebase that is easier for all team members to read and understand. A consistent coding style enables efficient collaboration, smooth communication, and reduces the likelihood of misunderstandings and errors, which are essential factors in delivering high-quality projects. PEP8 also helps developers avoid common pitfalls and mistakes, such as using ambiguous variable names or inconsistent indentation, which can lead to bugs and make code difficult to maintain.
Let us now dive into the PEP8 style guide and explore its key recommendations for writing clean, professional Python code.

Key PEP8 recommendations

In this section, we will explore the key recommendations of the PEP8 style guide, which covers various aspects of Python code, including naming conventions, indentation, line length, whitespace, and more.

Naming conventions

The following are the key recommendations for naming conventions in Python code:

• Variable names: Use lowercase letters and underscores to separate words in variable names. For example, num_samples, learning_rate, model_name, etc.
• Function names: Use lowercase letters and underscores to separate words in function names. For example, train_model, evaluate_model, get_data, etc.
• Class names: Use CamelCase to separate words in class names. For example, DataLoader, Model, Trainer, etc.
• Constants: Use all uppercase letters and underscores to separate words in constant names. For example, NUM_SAMPLES, LEARNING_RATE, MODEL_NAME, etc.
• Private variables: Use a single underscore prefix to indicate private variables. For example, _num_samples, _learning_rate, _model_name, etc.
• Private functions: Use a single underscore prefix to indicate private functions. For example, _train_model, _evaluate_model, _get_data, etc.
• modules: Use short, all-lowercase names for modules. Underscores can be used in the module name if it improves readability. data_loader.py, model.py, trainer.py, etc.
• packages: Use short, all-lowercase names, although the use of underscores is discouraged. For example, dataloader, model, trainer, etc.
• exceptions: Use CamelCase for exception names. For example, ValueError, TypeError, ZeroDivisionError, etc.
• arguments: Use lowercase letters and underscores to separate words in argument names. For example, num_samples, learning_rate, model_name, etc.
• keyword arguments: Use lowercase letters and underscores to separate words in keyword argument names. For example, num_samples, learning_rate, model_name, etc.

Indentation

PEP8 style guide recommends the following for indentation in Python code:

• Use 4 spaces per indentation level, not tabs
• Align continuation lines with the opening delimiter, or use a hanging indent with 4-space indentation

The following is an example of correct indentation:

# Correct:

# Aligned with opening delimiter.
foo = long_function_name(var_one, var_two,
                         var_three, var_four)

# Add 4 spaces (an extra level of indentation) to distinguish arguments from the rest.
def long_function_name(
        var_one, var_two, var_three,
        var_four):
    print(var_one)

# Hanging indents should add a level.
foo = long_function_name(
    var_one, var_two,
    var_three, var_four)

The following is an example of wrong indentation:

# Wrong:

# Arguments on first line forbidden when not using vertical alignment.
foo = long_function_name(var_one, var_two,
    var_three, var_four)

# Further indentation required as indentation is not distinguishable.
def long_function_name(
    var_one, var_two, var_three,
    var_four):
    print(var_one)

Line length

According to PEP8, the recommended maximum line length for Python code is 79 characters, including whitespace. This limit is designed to improve code readability by preventing lines from becoming excessively long and difficult to follow. Additionally, it ensures that the code can be easily viewed on various devices and screens without horizontal scrolling.

When a statement is too long to fit within the 79-character limit, you can break it into multiple lines using parentheses, brackets, or braces, or by using the line continuation character (''). Make sure to follow the indentation guidelines discussed earlier for continuation lines.

For comments and docstrings, PEP8 recommends a slightly shorter maximum line length of 72 characters. This allows for proper formatting when generating documentation or displaying the comments and docstrings in various contexts.

Whitespace

Appropriate use of whitespace is vital for code readability, as it visually separates different elements and helps to convey the structure of the code. PEP8 provides several recommendations for using whitespace in Python code. Let us explore them in detail:

Blank lines

Blank lines play an essential role in visually separating different sections of code, making it easier to understand the code's structure and organization.

• Top-level functions and class definitions:

Use two blank lines to separate top-level functions and class definitions. This practice helps to distinguish between different sections of your code and improves overall readability.

class MyClass:
    # Class implementation


def my_function():
    # Function implementation


class AnotherClass:
    # Class implementation

• Method definitions inside a class:

Use one blank line to separate method definitions inside a class. This spacing helps to delineate the individual methods and their boundaries within the class.

class MyClass:

    def method_one(self):
        # Method implementation

    def method_two(self):
        # Method implementation

    def method_three(self):
        # Method implementation

• Grouping related sections of code:

You can use blank lines to group related sections of code within a function or method. However, it is essential not to overuse blank lines, as too many can make your code appear disjointed and less coherent.

def my_function():
    # Section 1: Data preprocessing
    # ...

    # Section 2: Model training
    # ...

    # Section 3: Model evaluation
    # ...

White space in expressions and statements

• Use spaces around operators and after commas to improve readability. For example:

result = a + b * (c - d)
my_list = [1, 2, 3, 4, 5]

• Do not use spaces around the "=" sign when used for keyword arguments or default parameter values:

def my_function(a, b, c=None, d=0):
    pass

• Place a single space before and after assignment operators, comparison operators, and boolean operators:

x = 5
y = x * 2
if x > 0 and y < 10:
    print("Within range")

• Avoid extraneous whitespace in the following situations:

  • Immediately inside parentheses, brackets, or braces:

    # Correct
    my_list = [1, 2, 3]
    
    # Incorrect
    my_list = [ 1, 2, 3 ]

  • Immediately before a comma, semicolon, or colon:

    # Correct
    my_dict = {"key": "value", "another_key": "another_value"}
    
    # Incorrect
    my_dict = {"key" : "value" , "another_key" : "another_value"}

  • Immediately before the open parenthesis that starts the argument list of a function call:

    # Correct
    result = my_function(arg1, arg2)
    
    # Incorrect
    result = my_function (arg1, arg2)

  • Immediately before the open bracket that starts an indexing or slicing operation:

    # Correct
    my_value = my_list[3]
    
    # Incorrect
    my_value = my_list [3]
    ``

Imports

The following are the key recommendations for imports in Python code:

Imports:

In this section, we will discuss the PEP8 recommendations regarding the organization and style of import statements in Python code. Properly organizing imports improves the code's readability and makes it easier to identify dependencies.

Order of imports

PEP8 recommends organizing imports into three distinct groups, separated by a blank line. The groups are as follows:

1. Standard library imports
2. Third-party library imports
3. Local application or library imports

This organization helps to visually separate different types of imports and makes it clear where each imported module or package originates.

# Standard library imports
import os
import sys

# Third-party library imports
import numpy as np
import pandas as pd

# Local application/library imports
import my_module
import another_module

Import style

PEP8 recommends using absolute imports rather than relative imports, as they are usually more readable and less prone to errors. Additionally, it is recommended to use the "import" statement to import an entire module or specific objects from a module, instead of using "from ... import *", which can lead to unclear or conflicting names in the namespace.

# Recommended
import my_module
from my_module import my_function

# Not recommended
from my_module import *

Line length and multiple imports:

When importing multiple objects from a single module, and the line length exceeds the recommended 79 characters, you can break the imports into multiple lines using parentheses and place one import per line.

from my_module import (
    first_function,
    second_function,
    third_function,
)

Alphabetical order:

To further improve the readability of your import statements, you can order them alphabetically within each import group. This practice makes it easier to locate specific imports when scanning the code.

# Standard library imports
import os
import sys

# Third-party library imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Local application/library imports
import my_module
import another_module

Docstrings and Comments

Let's review the PEP8 recommendations for docstrings and comments in Python code.

Docstrings

Docstrings are multi-line strings used to provide documentation for modules, classes, functions, and methods. They are enclosed in triple quotes (either single or double) and should be placed immediately after the definition of the entity they document.

PEP8 recommends following the "docstring conventions" laid out in PEP 257. Some key points from PEP 257 include:

• For a one-line docstring, keep the summary concise and on the same line as the opening triple quotes, followed by the closing triple quotes.

def my_function():
    """This is a concise one-line docstring."""
    # Function implementation

• For a multi-line docstring, start with a one-line summary, followed by a blank line, and then a more detailed description. The closing triple quotes should be placed on a new line.

def my_function():
    """
    This is a summary of the function's purpose.

    This section provides a more detailed description of the function, its
    arguments, return values, and any exceptions it may raise. The description
    can span multiple lines, adhering to the recommended 72-character limit
    for docstrings.
    """
    # Function implementation

Comments

Comments are an essential tool for explaining the purpose, logic, or implementation details of your code. PEP8 provides several recommendations for writing and formatting comments to maximize their usefulness and readability:

• Use inline comments sparingly and ensure they are separated by at least two spaces from the code statement. Start the comment with a '#' followed by a single space.

x = x + 1  # Increment the value of x

• Keep comments up-to-date, as outdated comments can be more confusing than helpful.

• Use complete sentences when writing comments, and ensure they are clear, concise, and relevant to the code they describe.

• For block comments, which describe a section of code, place them before the code they describe and align them with the code. Start each line with a '#' followed by a single space.

# The following section of code calculates the sum
# of all elements in the list and stores the result
# in the variable 'total_sum'
total_sum = 0
for element in my_list:
    total_sum += element

Linters and Formatters to Enforce PEP8

Linters and formatters are useful to check and enforce PEP8 compliance in your Python code. Linters analyze your code for potential errors, bugs, and non-compliant coding practices, while formatters automatically adjust your code's formatting to adhere to PEP8 guidelines.

Linters

There are several popular linters available for checking PEP8 compliance in Python code. Two widely-used linters are:

• Flake8: Flake8 is a popular linter that combines the functionality of PyFlakes, pycodestyle, and McCabe complexity checking. It is easy to configure and can be integrated with various text editors and IDEs. To install and use Flake8, run the following commands:

pip install flake8
flake8 your_script.py

• Pylint: Pylint is another powerful linter that goes beyond PEP8 compliance checks and provides additional insights into code quality, potential bugs, and refactoring opportunities. To install and use Pylint, run the following commands:

pip install pylint
pylint your_script.py

Both linters can be customized to fit your team's preferences and project requirements by modifying their configuration files.

Formatters

Formatters are tools that automatically adjust your code's formatting to adhere to PEP8 guidelines. Two popular formatters are:

• Black: Black is an opinionated code formatter that prioritizes consistency and readability. With minimal configuration options, Black enforces a uniform coding style across your project. To install and use Black, run the following commands:

pip install black
black your_script.py

• Autopep8: Autopep8 is a formatter that focuses specifically on PEP8 compliance. It provides more configuration options than Black, allowing for greater customization. To install and use Autopep8, run the following commands:

pip install autopep8
autopep8 --in-place --aggressive --aggressive your_script.py

By using linters and formatters, you can ensure that your Python code adheres to PEP8 guidelines, improving its readability and maintainability. In the upcoming sections, we will discuss integrating PEP8 checks into your development workflow and continuous integration (CI) pipeline, which will help you maintain a consistent coding style throughout your project.

Integrating PEP8 Checks into Development Workflow

In this section, we will discuss how to integrate PEP8 checks into your development workflow to maintain a consistent coding style and catch issues early in the development process. Integrating PEP8 checks into your workflow will help you and your team ensure that your Python code remains readable and maintainable.

Text editor and IDE integrations

Many text editors and IDEs support PEP8 compliance checking, either natively or through plugins. Integrating PEP8 checks into your preferred text editor or IDE allows you to see and fix issues as you write code. Some popular text editors and IDEs with PEP8 support include:

• Visual Studio Code: You can use extensions like "Python" by Microsoft or "Pylance" to enable PEP8 checking and formatting.
• PyCharm: PyCharm has built-in PEP8 compliance checking and automatic formatting support.
• Sublime Text: Install the "SublimeLinter" and "SublimeLinter-flake8" packages to enable PEP8 checking.

Pre-commit hooks

Pre-commit hooks are scripts that run automatically before each commit, allowing you to check for PEP8 compliance and other issues before your changes are committed to the repository. You can use the "pre-commit" framework to manage pre-commit hooks for PEP8 compliance checking and automatic formatting. To set up pre-commit hooks, follow these steps:

• Install the pre-commit package:

pip install pre-commit

• Create a .pre-commit-config.yaml file in your project's root directory with the following content:

repos:
  - repo: https://github.com/ambv/black
    rev: stable
    hooks:
      - id: black
        language_version: python3.7
  - repo: https://gitlab.com/pycqa/flake8
    rev: 3.9.2
    hooks:
      - id: flake8

• Run pre-commit install to set up the pre-commit hooks.

Now, every time you commit changes to your repository, the pre-commit hooks will check for PEP8 compliance and format your code automatically.

Continuous Integration (CI) Pipeline

Integrating PEP8 checks into your CI pipeline ensures that any code changes submitted by you or your team members meet the required coding standards before they are merged into the main branch. Popular CI services like GitHub Actions, GitLab CI/CD, and Jenkins can be configured to run PEP8 checks on each pull request or merge request. This setup will help you maintain consistent code quality across your project.

By integrating PEP8 checks into your development workflow, you can ensure that your Python code remains readable, maintainable, and adheres to a consistent coding style. This practice will help you and your team catch issues early, streamline collaboration, and improve the overall quality of your project.

Customizing PEP8 to Fit Team and Project Needs

In real-world projects, it's often necessary to adapt PEP8 rules to meet the specific needs of your team and project. By customizing the configuration of linters and formatters, you can enforce a coding style that aligns with your team's preferences and project requirements.

Customizing linter configuration

Both Flake8 and Pylint allow you to customize their configurations to enforce your preferred coding style. To do this, you can create a configuration file in your project's root directory.

• For Flake8, create a .flake8 file with the following example content:

  [flake8]
  max-line-length = 100
  ignore = E203, W503
  

  In this example, we've set the maximum line length to 100 characters and have chosen to ignore specific PEP8 rules (E203 and W503).

• For Pylint, create a pylintrc file with the following example content:

  [MASTER]
  max-line-length = 100
  
  [MESSAGES CONTROL]
  disable = C0301
  

  Similar to the Flake8 configuration, we've set the maximum line length to 100 characters and disabled rule C0301, which corresponds to the line length rule.

Customizing formatter configuration

Both Black and Autopep8 allow you to customize their configurations to format your code according to your preferred style.

• For Black, you can create a pyproject.toml file in your project's root directory with the following example content:

  [tool.black]
  line-length = 100
  

  In this example, we've set the maximum line length to 100 characters.

• For Autopep8, you can pass command-line arguments to customize its behavior, as shown in this example:

  autopep8 --in-place --aggressive --aggressive --max-line-length 100 your_script.py
  

  Here, we've set the maximum line length to 100 characters.

Balancing PEP8 with Practicality

While adhering to the PEP8 style guide is important for maintaining consistent, readable, and maintainable Python code, it's also crucial to balance the strict application of PEP8 rules with practicality and readability in real-world projects. In this section, we will discuss some guidelines for striking this balance.

1. Prioritize readability over strict adherence:

Although PEP8 provides a great set of guidelines for writing readable code, sometimes strict adherence to these rules can actually make the code less readable. In such cases, it's important to prioritize readability over strict PEP8 compliance. For example, you might break the line length limit if it improves readability or if breaking the line would make the code more difficult to understand.

2. Adapt PEP8 rules to your team's preferences and project requirements:

Different teams and projects may have unique requirements and preferences when it comes to coding style. Instead of blindly following PEP8 rules, it's essential to adapt them to fit your team's needs. You can customize the configuration of linters and formatters to enforce a coding style that aligns with your team's preferences and project requirements. For example, you might choose a different maximum line length or modify the rules for naming conventions.

3. Use comments and docstrings effectively:

While PEP8 provides guidelines for the formatting of comments and docstrings, it's also important to focus on their content. Write clear, concise, and informative comments and docstrings that explain the purpose and functionality of your code. This practice will make your code more understandable and maintainable for your team members and future contributors.

4. Use common sense

When in doubt, use common sense and communicate with your team members to determine the best course of action. Discuss any changes or deviations from PEP8 rules with your team to ensure everyone is on the same page and understands the reasoning behind the decision. Also, be open to feedback from your team members and be willing to revise your code to enhance its readability and maintainability.

Summary

In this tutorial, we introduced the PEP8 style guide and discussed its importance for maintaining consistent, readable, and maintainable Python code. We covered key PEP8 recommendations, such as naming conventions, indentation, line length, whitespace, imports, and more. We also discussed using linters and formatters, such as Flake8, Pylint, Black, and Autopep8, to check and enforce PEP8 compliance. Furthermore, we explored integrating PEP8 checks into development workflows, striking a balance between PEP8 recommendations and practicality, and customizing PEP8 rules to fit your team's preferences and project requirements. By following these guidelines, you can ensure that your Python code remains readable and maintainable, ultimately resulting in better collaboration and higher-quality projects.