Home Backend Development Python Tutorial Formatting and Linting Your Python Codes with GitHub Actions

Formatting and Linting Your Python Codes with GitHub Actions

Sep 10, 2024 am 10:52 AM

Formatting and Linting Your Python Codes with GitHub Actions

In the ever-evolving landscape of software development, maintaining code quality and consistency is crucial. One of the most effective ways to ensure that your codebase remains clean and adheres to best practices is by automating formatting and linting processes. In this blog post, we’ll walk through setting up a GitHub Actions workflow designed to automate code formatting and linting for Python projects. We'll explore the configuration and the steps involved, and how it can save you time and reduce errors in your code.

Introduction to GitHub Actions

GitHub Actions is a powerful tool that allows you to automate workflows directly within your GitHub repository. From running tests to deploying applications, GitHub Actions can handle various tasks based on events like pushes, pull requests, and more. In this example, we’ll focus on automating code formatting and linting using GitHub Actions.

The Workflow Breakdown

Here’s a detailed look at the GitHub Actions workflow for formatting and linting Python code:

name: Format and Lint

on:
  push:
    branches:
      - master
  pull_request:
    branches:
      - master

jobs:
  format-and-lint:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.9'  # Specify the Python version to use

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install black isort autopep8

      - name: Run Black
        run: black .

      - name: Run isort
        run: isort .

      - name: Run autopep8
        run: autopep8 --in-place --recursive .

      - name: Commit changes if any
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          # Check for changes
          git diff --exit-code || {
            echo "Changes detected. Committing changes..."

            # Configure Git user
            git config --global user.name "github-actions"
            git config --global user.email "actions@github.com"

            # Stage all changes
            git add .

            # Commit changes
            git commit -m "Apply code formatting and linting fixes"

            # Push changes
            git push origin HEAD
          }
Copy after login

Workflow Components Explained

  1. Trigger Events:
   on:
     push:
       branches:
         - master
     pull_request:
       branches:
         - master
Copy after login

The workflow is triggered on pushes and pull requests to the master branch. This ensures that every change to the main branch or pull request is automatically formatted and linted.

  1. Job Configuration:
   jobs:
     format-and-lint:
       runs-on: ubuntu-latest
Copy after login

The job runs on the latest version of Ubuntu. This is the environment where your formatting and linting will occur.

  1. Checkout Code:
   - name: Checkout code
     uses: actions/checkout@v3
Copy after login

This step checks out your repository code, allowing subsequent steps to access and modify it.

  1. Set Up Python:
   - name: Set up Python
     uses: actions/setup-python@v4
     with:
       python-version: '3.9'
Copy after login

This step sets up Python 3.9 in the workflow environment. Adjust this to match the Python version used in your project.

  1. Install Dependencies:
   - name: Install dependencies
     run: |
       python -m pip install --upgrade pip
       pip install black isort autopep8
Copy after login

Here, essential Python packages for formatting and linting—black, isort, and autopep8—are installed.

  1. Run Formatters:
   - name: Run Black
     run: black .

   - name: Run isort
     run: isort .

   - name: Run autopep8
     run: autopep8 --in-place --recursive .
Copy after login

These steps apply code formatting using black, isort for import sorting, and autopep8 for additional formatting adjustments.

  1. Commit Changes:
   - name: Commit changes if any
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     run: |
       git diff --exit-code || {
         echo "Changes detected. Committing changes..."

         git config --global user.name "github-actions"
         git config --global user.email "actions@github.com"

         git add .
         git commit -m "Apply code formatting and linting fixes"
         git push origin HEAD
       }
Copy after login

If formatting or linting changes are made, this step commits and pushes them back to the repository. It uses a GitHub token for authentication and configures Git with a generic user for commits.

Benefits of This Workflow

  1. Consistency: Ensures that code follows consistent formatting rules, improving readability and maintainability.
  2. Automation: Automates the formatting and linting process, reducing manual intervention and potential errors.
  3. Integration: Seamlessly integrates with your GitHub repository, running checks automatically on code changes.

Conclusion

Implementing a GitHub Actions workflow for formatting and linting is a smart way to maintain code quality and consistency across your projects. By automating these processes, you can focus more on writing code and less on formatting issues. The workflow provided here serves as a solid foundation, but you can customize it further based on your project's specific needs. Start integrating this workflow into your repositories today and experience the benefits of automated code quality management!

The above is the detailed content of Formatting and Linting Your Python Codes with GitHub Actions. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1664
14
PHP Tutorial
1266
29
C# Tutorial
1239
24
Python vs. C  : Applications and Use Cases Compared Python vs. C : Applications and Use Cases Compared Apr 12, 2025 am 12:01 AM

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

The 2-Hour Python Plan: A Realistic Approach The 2-Hour Python Plan: A Realistic Approach Apr 11, 2025 am 12:04 AM

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python: Games, GUIs, and More Python: Games, GUIs, and More Apr 13, 2025 am 12:14 AM

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

How Much Python Can You Learn in 2 Hours? How Much Python Can You Learn in 2 Hours? Apr 09, 2025 pm 04:33 PM

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and Time: Making the Most of Your Study Time Python and Time: Making the Most of Your Study Time Apr 14, 2025 am 12:02 AM

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python: Exploring Its Primary Applications Python: Exploring Its Primary Applications Apr 10, 2025 am 09:41 AM

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

See all articles