Python Project Template provides a ready-to-use structure for Python projects, integrating best practices for code quality, testing, security, documentation, and CI/CD. It helps developers start new projects quickly with a maintainable and professional foundation.
- 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 (e.g., using venv) 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 install
This installs development, testing, and documentation tools as defined in
pyproject.toml. -
Run the Pipeline
Execute the quality pipeline, which includes linting, type checking, security analysis, 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.