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name: python-best-practices description: Python coding best practices. Use when writing or reviewing Python code. Covers type hints, error handling, and common patterns. license: MIT metadata: author: plan-cascade version: "1.0.0"

Python Best Practices

Code Style

Rule	Guideline
Formatter	`ruff format` or `black`
Linter	`ruff check` with strict rules
Type hints	Always for public APIs
Docstrings	Google or NumPy style

Type Hints

python

def process(items: list[str], max_count: int = 10) -> dict[str, int]: ...
def find_user(user_id: int) -> User | None: ...

Error Handling

Rule	Guideline
Specific exceptions	Never bare `except:`
Context managers	Use `with` for resources
Early return	Validate inputs, fail fast

python

# Good
try:
    result = process(data)
except ValidationError as e:
    logger.warning(f"Validation failed: {e}")
    return default

Project Structure

src/package_name/
├── __init__.py
├── core/
└── py.typed
tests/
├── conftest.py
└── test_*.py
pyproject.toml

Common Patterns

Pattern	Usage
`@dataclass`	Data containers
`@property`	Computed attributes
Generators	Memory-efficient iteration
`functools.cache`	Memoization

Anti-Patterns

Avoid	Use Instead
Mutable default args	`None` default, create inside
`import *`	Explicit imports
Global state	Dependency injection

python

# Bad: def add(item, items=[]): ...
# Good:
def add(item, items=None):
    items = items or []
    items.append(item)
    return items

Tools

Tool	Purpose
`pytest`	Testing
`ruff`	Linting + formatting
`mypy`	Type checking
`uv`	Dependencies

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