When developing ReadmeGenie, I aimed to ensure consistent code quality with an automated setup for linting and formatting. After considering several tools, I selected Ruff as the linter and Black as the code formatter. Although Ruff can also handle both linting and formatting, I decided to set up Black as a separate formatter to gain experience with the configuration of both tools. Below, I’ll share why I chose these tools, how I configured them for my project, the challenges I faced, and the lessons I learned along the way.
1. Tool Selection
Why Ruff?
Ruff is a fast linter for Python that supports various linting rules from other linters (like Flake8 and Pyflakes) and offers significant performance improvements. It’s highly customizable, which allowed me to specify a mix of rules while ensuring compatibility with Black for formatting. Ruff’s design for speed and extensibility is ideal for projects that prioritize efficiency without sacrificing quality.
- Ruff Documentation: https://github.com/charliermarsh/ruff
Why Black?
Black is a Python formatter that strictly enforces one formatting style, helping reduce discussions and inconsistencies over code styling. While Ruff offers basic formatting capabilities, Black’s dedicated approach provides a few advantages:
- Consistency: Black enforces a strict, standard style that minimizes debates over code formatting.
Broad Adoption: Black is widely used, making it easier to integrate into most development workflows, especially in collaborative projects.
Black Documentation: https://github.com/psf/black
2. Project Setup
To ensure that Ruff and Black worked seamlessly in ReadmeGenie, I configured them in both pyproject.toml and
.pre-commit-config.yaml, allowing developers to automatically format and lint code when making commits.
Configuration for Ruff and Black in pyproject.toml
This setup ensures Ruff is used solely for linting and Black for formatting:
# pyproject.toml # Set up black as formatter [tool.black] line-length = 88 target-version = ["py311"] # Set up ruff as linter only [tool.ruff] # Exclude directories that don’t need linting (e.g., virtual environments) exclude = [ "venv/", "__pycache__/" ] fix = true # Enable specific linting rules select = ["F", "E", "W", "B", "I", "S"] # Example codes: F=flake8, E=errors, W=warnings, B=bugbear, I=import, S=safety # Exclude Black-compatible rules to avoid conflicts with Black's formatting. ignore = ["E501", "E203", "E231"] # Exclude Black-incompatible style issues
- ignore: Black handles specific styling, so we excluded these rules in Ruff.
- fix: Enables Ruff to fix issues where possible, leaving formatting to Black.
Adding Pre-commit Hook for Ruff and Black
Using pre-commit hooks, I configured .pre-commit-config.yaml to enforce linting and formatting on every commit:
# .pre-commit-config.yaml repos: - repo: https://github.com/psf/black rev: 23.1.0 hooks: - id: black - repo: https://github.com/charliermarsh/ruff-pre-commit rev: v0.7.1 hooks: - id: ruff
3. Running Ruff and Black from the Command Line
With the above setup, you can use the following commands:
- Run Ruff:
# pyproject.toml # Set up black as formatter [tool.black] line-length = 88 target-version = ["py311"] # Set up ruff as linter only [tool.ruff] # Exclude directories that don’t need linting (e.g., virtual environments) exclude = [ "venv/", "__pycache__/" ] fix = true # Enable specific linting rules select = ["F", "E", "W", "B", "I", "S"] # Example codes: F=flake8, E=errors, W=warnings, B=bugbear, I=import, S=safety # Exclude Black-compatible rules to avoid conflicts with Black's formatting. ignore = ["E501", "E203", "E231"] # Exclude Black-incompatible style issues
- Run Black:
# .pre-commit-config.yaml repos: - repo: https://github.com/psf/black rev: 23.1.0 hooks: - id: black - repo: https://github.com/charliermarsh/ruff-pre-commit rev: v0.7.1 hooks: - id: ruff
These commands apply fixes to all Python files, ensuring consistent styling and quality checks.
4. VS Code Integration
To automate Ruff and Black on save, I added the following configuration in .vscode/settings.json:
ruff check . --fix
This setup makes Black the default formatter and Ruff the only active linter in VS Code, allowing both to run
automatically upon saving.
5. Findings and Fixes
Once configured, Ruff and Black identified several issues:
- Line Length (E501): Ruff initially flagged long lines, which Black auto-formatted.
- Unused Imports and Variables: Ruff caught several unused imports and variables.
- Indentation and Styling Consistency: Black applied consistent spacing and indentation, enhancing readability.
6. Challenges
One notable challenge was understanding that some styles are incompatible between Ruff and Black. For example:
- Line Length (E501): Ruff initially flagged long lines exceeding 88 characters, which Black handles by wrapping lines. To prevent conflicts, I added E501 to Ruff’s ignore list. Despite this, Ruff sometimes flagged E501 errors if Black didn’t apply the expected breakpoints. These discrepancies underscored the importance of adjusting each tool’s configuration and understanding where they may overlap.
7. Lessons Learned
Using Ruff and Black together has been a great way to improve code quality. Here’s what I learned:
- Consistency: Black’s opinionated style reduces ambiguity in code styling.
- Automation: Pre-commit hooks save time and ensure consistent formatting.
- Editor Integration: Configuring Ruff and Black to run on save within VS Code streamlined development.
- Compatibility: Learning how to resolve conflicts like E501 taught me about tool configurations and helped fine-tune project workflows.
The above is the detailed content of Setting Up Tools for Code Quality. For more information, please follow other related articles on the PHP Chinese website!

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 C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

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

Hot Article

Hot Tools

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Dreamweaver Mac version
Visual web development tools

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

SublimeText3 Mac version
God-level code editing software (SublimeText3)

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment