Python Best Practices

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 them (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 assertions, edge cases, error paths, proper mocking
• Code brevity: flag code that can be expressed in fewer lines without losing clarity

Use PY-NNN prefix for all findings.