tl;dr
This tutorial will help you gain a solid understanding of the PEP8 style guide for writing clean, professional Python code.
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:
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!
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.
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.
The following are the key recommendations for naming conventions in Python code:
num_samples, learning_rate, model_name, etc.train_model, evaluate_model, get_data, etc.DataLoader, Model, Trainer, etc.NUM_SAMPLES, LEARNING_RATE, MODEL_NAME, etc._num_samples, _learning_rate, _model_name, etc._train_model, _evaluate_model, _get_data, etc.data_loader.py, model.py, trainer.py, etc.dataloader, model, trainer, etc.ValueError, TypeError, ZeroDivisionError, etc.num_samples, learning_rate, model_name, etc.num_samples, learning_rate, model_name, etc.PEP8 style guide recommends the following for indentation in Python code:
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)
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.
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 play an essential role in visually separating different sections of code, making it easier to understand the code's structure and organization.
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
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
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 # ...
result = a + b * (c - d) my_list = [1, 2, 3, 4, 5]
def my_function(a, b, c=None, d=0): pass
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] ``
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.
PEP8 recommends organizing imports into three distinct groups, separated by a blank line. The groups are as follows:
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
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 *
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, )
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
Let's review the PEP8 recommendations for docstrings and comments in Python code.
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:
def my_function(): """This is a concise one-line docstring.""" # Function implementation
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 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:
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 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.
There are several popular linters available for checking PEP8 compliance in Python code. Two widely-used linters are:
pip install flake8
flake8 your_script.py
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 are tools that automatically adjust your code's formatting to adhere to PEP8 guidelines. Two popular formatters are:
pip install black
black your_script.py
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.
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.
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:
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:
pip install pre-commit
.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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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