python-best-practiceslisted

# Python Best Practices ## Technical Standards - **Python Version**: 3.9+ features - **Code Style**: PEP 8, use black/ruff for formatting - **Type Hints**: typing module for all public APIs - **Testing**: pytest with minimum 80% coverage - **Documentation**: One-line docstring for every public function/class; expand only when non-obvious (Google/NumPy/Sphinx style) - **Error Handling**: Specific exception types, proper error messages - **Dependencies**: uv or poetry - **Virtual Environments**: Always use virtual environments (uv creates them automatically) ## Best Practices - Context managers (with statements) for resource management - Prefer composition over inheritance - Use dataclasses or Pydantic for data structures - Generators for memory efficiency with large datasets - Proper logging (logging module, not print) - async/await for I/O-bound operations when beneficial - No mutable default arguments ## Code Quality Tools - **Linting**: pylint, flake8, or ruff - **Formatting**: black or ruff - **Type Checking**: mypy or pyright - **Testing**: pytest with coverage.py - **Security**: bandit for security checks ## Code Review Checklist - PEP 8 compliance and consistent style - Type hint coverage on public APIs - Docstring presence and accuracy - DRY compliance: duplicated logic, copy-paste patterns - Naming clarity: variables, functions, classes, modules - Context managers for resource management - Exception types are specific, not bare except - Test quality: meaningful as