Appendix D — Python Development Workflow Checklist

This checklist provides a practical reference for setting up and maintaining Python projects of different scales. Choose the practices that match your project’s complexity and team size.

Development Stage	Simple/Beginner Project	Intermediate/Large Project
Project Setup
Create project structure	✅ Basic directory with code and tests	✅ Full src layout with package under src/
Initialize version control	✅ git init and basic .gitignore	✅ Advanced .gitignore with branch strategies
Add essential files	✅ README.md	✅ README.md, LICENSE, CONTRIBUTING.md
Environment Setup
Create virtual environment	✅ python -m venv .venv	✅ uv venv or containerized environment
Track dependencies	✅ pip freeze > requirements.txt	✅ requirements.in with pip-compile or uv pip compile
Install dependencies	✅ pip install -r requirements.txt	✅ pip-sync or uv pip sync
Code Quality
Formatting	✅ Basic PEP 8 adherence	✅ Automated with Ruff (ruff format)
Linting	❌ or basic Flake8	✅ Ruff with multiple rule sets enabled
Type checking	❌ or basic annotations	✅ mypy with increasing strictness
Security scanning	❌	✅ Bandit
Dead code detection	❌	✅ Vulture
Testing
Unit tests	✅ Basic pytest	✅ Comprehensive pytest with fixtures
Test coverage	❌ or basic	✅ pytest-cov with coverage targets
Mocking	❌	✅ pytest-mock for external dependencies
Integration tests	❌	✅ For component interactions
Functional tests	❌	✅ For key user workflows
Documentation
Code documentation	✅ Basic docstrings	✅ Comprehensive docstrings (Google/NumPy style)
API documentation	✅ Generated with pydoc	✅ MkDocs + mkdocstrings
User guides	✅ Basic README usage examples	✅ Comprehensive MkDocs site with tutorials
Version Control Practices
Commit frequency	✅ Regular commits	✅ Atomic, focused commits
Commit messages	✅ Basic descriptive messages	✅ Structured commit messages with context
Branching	❌ or basic feature branches	✅ Git-flow or trunk-based with feature branches
Code reviews	❌	✅ Pull/Merge requests with review guidelines
Automation
Local automation	❌	✅ pre-commit hooks
CI pipeline	❌ or basic	✅ GitHub Actions with matrix testing
CD pipeline	❌	✅ Automated deployments/releases
Packaging & Distribution
Package configuration	✅ Basic pyproject.toml	✅ Comprehensive configuration with extras
Build system	✅ Basic setuptools	✅ Modern build with PEP 517 support
Release process	✅ Manual versioning	✅ Semantic versioning with automation
Publication	❌ or manual PyPI upload	✅ Automated PyPI deployment via CI
Maintenance
Dependency updates	✅ Manual updates	✅ Scheduled updates with dependabot
Security monitoring	❌	✅ Vulnerability scanning
Performance profiling	❌	✅ Regular profiling and benchmarking
User feedback channels	❌	✅ Issue templates and contribution guidelines

D.1 Project Progression Path

For projects that start simple but grow in complexity, follow this progression:

1. Start with the essentials:
   • Project structure and version control
   • Virtual environment
   • Basic testing
   • Clear README
2. Add code quality tools incrementally:
   • First add Ruff for formatting and basic linting
   • Then add mypy for critical modules
   • Finally add security scanning
3. Enhance testing as complexity increases:
   • Add coverage reporting
   • Implement mocking for external dependencies
   • Add integration tests for component interactions
4. Improve documentation with growth:
   • Start with good docstrings from day one
   • Transition to MkDocs when README becomes insufficient
   • Generate API documentation from docstrings
5. Automate processes as repetition increases:
   • Add pre-commit hooks for local checks
   • Implement CI for testing across environments
   • Add CD when deployment becomes routine

Remember: Don’t overengineer! Choose the practices that add value to your specific project and team. It’s better to implement a few practices well than to poorly implement many.