Black is an uncompromising Python code formatter designed to save developers time and mental energy by automating the task of code formatting. By using Black, developers give up control over the finer details of formatting, in exchange for speed, consistency, and freedom from worrying about style guide issues like PEP 8 compliance. Black ensures that code looks the same across different projects, making it easier for teams to focus on the functionality of their code rather than its appearance.
The tool is designed to improve the coding workflow by generating minimal diffs, which makes reviewing code much faster and more efficient. Black’s formatting style is consistent and predictable, helping developers get accustomed to a uniform look across all projects, reducing the cognitive load when switching between codebases. It also integrates seamlessly into many Python environments and tools, supporting both small and large projects alike, and even allows configuration via a pyproject.toml file for custom exclusions.
Installation and usage of Black are straightforward, requiring Python 3.9+, with an option to format Jupyter Notebooks. It’s widely adopted by well-known organizations like Facebook, Dropbox, and Tesla, as well as open-source projects like Django and Pytest. The tool has garnered praise from prominent developers for boosting productivity by eliminating manual formatting, letting them focus on more critical tasks.
Visit the website: https://github.com/psf/black
Related Technologies:
Related Resources
Check what other resources can help you on the Python developer's path.
uv is a lightning-fast Python package and project manager, written in Rust, that replaces tools like pip and poetry while offering 10-100x speed improvements. It supports multiple Python versions and advanced project management, providing a flexible, high-performance solution for developers.
Jupyter Notebook is an open-source web application that enables users to create and share documents containing live code, equations, and visualizations, supporting over 40 programming languages. Its advanced version, JupyterLab, provides a more flexible environment, making both tools ideal for data science, machine learning, and scientific computing tasks across various industries and research fields.
Flask's documentation provides a comprehensive guide for building web applications, starting from installation and quick setup to more advanced topics like blueprints, testing, and deployment. It covers key components such as routing, templates, and session management, while offering detailed API references and guidance on extending Flask with community-maintained extensions.
Add a Python-related resource!
Do you have a useful resource for other Python developers? List them here!