python-code-style

name python-code-style description Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards. Python Code Style & Documentation Consistent code style and clear documentation make codebases maintainable and collaborative. This skill covers modern Python tooling, naming conventions, and documentation standards. When to Use This Skill Setting up linting and formatting for a new project Writing or reviewing docstrings Establishing team coding standards Configuring ruff, mypy, or pyright Reviewing code for style consistency Creating project documentation Core Concepts 1. Automated Formatting Let tools handle formatting debates. Configure once, enforce automatically. 2. Consistent Naming Follow PEP 8 conventions with meaningful, descriptive names. 3. Documentation as Code Docstrings should be maintained alongside the code they describe. 4. Type Annotations Modern Python code should include type hints for all public APIs. Quick Start # Install modern tooling pip install ruff mypy # Configure in pyproject.toml [tool.ruff] line-length = 120 target-version = "py312" # Adjust based on your project's minimum Python version [tool.mypy] strict = true Fundamental Patterns Pattern 1: Modern Python Tooling Use ruff as an all-in-one linter and formatter. It replaces flake8, isort, and black with a single fast tool. # pyproject.toml [tool.ruff] line-length = 120 target-version = "py312" # Adjust based on your project's minimum Python version [tool.ruff.lint] select = [ "E" , # pycodestyle errors "W" , # pycodestyle warnings "F" , # pyflakes "I" , # isort "B" , # flake8-bugbear "C4" , # flake8-comprehensions "UP" , # pyupgrade "SIM" , # flake8-simplify ] ignore = [ "E501" ] # Line length handled by formatter [tool.ruff.format] quote-style = "double" indent-style = "space" Run with: ruff check --fix . # Lint and auto-fix ruff format . # Format code Pattern 2: Type Checking Configuration Configure strict type checking for production code. # pyproject.toml [tool.mypy] python_version = "3.12" strict = true warn_return_any = true warn_unused_ignores = true disallow_untyped_defs = true disallow_incomplete_defs = true [[tool.mypy.overrides]] module = "tests.*" disallow_untyped_defs = false Alternative: Use pyright for faster checking. [tool.pyright] pythonVersion = "3.12" typeCheckingMode = "strict" Pattern 3: Naming Conventions Follow PEP 8 with emphasis on clarity over brevity. Files and Modules: # Good: Descriptive snake_case user_repository.py order_processing.py http_client.py # Avoid: Abbreviations usr_repo.py ord_proc.py http_cli.py Classes and Functions: # Classes: PascalCase class UserRepository : pass class HTTPClientFactory : # Acronyms stay uppercase pass # Functions and variables: snake_case def get_user_by_email ( email: str ) -> User | None : retry_count = 3 max_connections = 100 Constants: # Module-level constants: SCREAMING_SNAKE_CASE MAX_RETRY_ATTEMPTS = 3 DEFAULT_TIMEOUT_SECONDS = 30 API_BASE_URL = "https://api.example.com" Pattern 4: Import Organization Group imports in a consistent order: standard library, third-party, local. # Standard library import os from collections.abc import Callable from typing import Any # Third-party packages import httpx from pydantic import BaseModel from sqlalchemy import Column # Local imports from myproject.models import User from myproject.services import UserService Use absolute imports exclusively: # Preferred from myproject.utils import retry_decorator # Avoid relative imports from ..utils import retry_decorator Advanced Patterns Pattern 5: Google-Style Docstrings Write docstrings for all public classes, methods, and functions. Simple Function: def get_user ( user_id: str ) -> User: """Retrieve a user by their unique identifier.""" ... Complex Function: def process_batch ( items: list [Item], max_workers: int = 4 , on_progress: Callable [[ int , int ], None ] | None = None , ) -> BatchResult: """Process items concurrently using a worker pool. Processes each item in the batch using the configured number of workers. Progress can be monitored via the optional callback. Args: items: The items to process. Must not be empty. max_workers: Maximum concurrent workers. Defaults to 4. on_progress: Optional callback receiving (completed, total) counts. Returns: BatchResult containing succeeded items and any failures with their associated exceptions. Raises: ValueError: If items is empty. ProcessingError: If the batch cannot be processed. Example: >>> result = process_batch(items, max_workers=8) >>> print(f"Processed {len(result.succeeded)} items") """ ... Class Docstring: class UserService : """Service for managing user operations. Provides methods for creating, retrieving, updating, and deleting users with proper validation and error handling. Attributes: repository: The data access layer for user persistence. logger: Logger instance for operation tracking. Example: >>> service = UserService(repository, logger) >>> user = service.create_user(CreateUserInput(...)) """ def __init__ ( self, repository: UserRepository, logger: Logger ) -> None : """Initialize the user service. Args: repository: Data access layer for users. logger: Logger for tracking operations. """ self .repository = repository self .logger = logger Pattern 6: Line Length and Formatting Set line length to 120 characters for modern displays while maintaining readability. # Good: Readable line breaks def create_user ( email: str , name: str , role: UserRole = UserRole.MEMBER, notify: bool = True , ) -> User: ... # Good: Chain method calls clearly result = ( db.query(User) . filter (User.active == True ) .order_by(User.created_at.desc()) .limit( 10 ) . all () ) # Good: Format long strings error_message = ( f"Failed to process user {user_id} : " f"received status {response.status_code} " f"with body {response.text[: 100 ]} " ) Pattern 7: Project Documentation README Structure: # Project Name Brief description of what the project does. ## Installation \ `\` \`bash pip install myproject \ `\` \` ## Quick Start \ `\` \`python from myproject import Client client = Client(api _key="...") result = client.process(data) \`\`\` ## Configuration Document environment variables and configuration options. ## Development \`\`\`bash pip install -e ".[dev]" pytest \`\`\` CHANGELOG Format (Keep a Changelog): # Changelog ## [Unreleased] ### Added - New feature X ### Changed - Modified behavior of Y ### Fixed - Bug in Z Best Practices Summary Use ruff - Single tool for linting and formatting Enable strict mypy - Catch type errors before runtime 120 character lines - Modern standard for readability Descriptive names - Clarity over brevity Absolute imports - More maintainable than relative Google-style docstrings - Consistent, readable documentation Document public APIs - Every public function needs a docstring Keep docs updated - Treat documentation as code Automate in CI - Run linters on every commit Target Python 3.10+ - For new projects, Python 3.12+ is recommended for modern language features