Python Project Template provides a ready-to-use structure for Python projects, integrating best practices for code quality, testing and more. It helps developers start new projects quickly with a maintainable and professional foundation.
Warning: This template is configured for Linux x86_64 systems. For other platforms, you may need to adjust the
environmentsandrequired-environmentssettings inpyproject.toml.
- Linting & Type Checking: Ruff and Mypy for clean, consistent code.
- Security Scanning: Bandit detects potential vulnerabilities.
- Code Complexity Analysis: Complexipy identifies complex functions and modules.
- Testing Suite: Reliable unit testing with Pytest.
- Auto Documentation: MkDocs + GitHub Pages for automated docs.
- CI/CD: GitHub Actions automates linting, testing, and documentation deployment.
And more.
Before starting, ensure that you have required Python installed and a virtual environment
set up. It is recommended to create an isolated environment to manage dependencies
cleanly. Additionally, ensure that uv is installed
in your environment to handle grouped dependency installations.
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Generate Your Project
Use Cookiecutter to create a new project from the template:
cookiecutter https://github.com/danibcorr/python-project-template.git
Follow the prompts to configure project metadata, package name, and other options.
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Install Dependencies
Activate your virtual environment and install all dependencies using the included
Makefile:make setup
This installs development, testing, and documentation tools as defined in
pyproject.toml. -
Run the Pipeline
Execute the quality pipeline, which includes linting, type checking, complexity checks, and test execution:
make pipeline
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Run the Full Workflow (Optional)
To perform a complete setup including dependency installation, full quality checks, and local documentation preview:
make all
This ensures that the project environment is fully prepared for development and validation.