python-dev（Python DEV 测试）

Python Development Rules

Overview

Python development guidance focused on code quality, error handling, testing, and environment management. Apply when working with Python code or Jupyter notebooks.

When to Use This Skill

Use this skill when:

• Writing new Python code or modifying existing Python files
• Creating or updating Jupyter notebooks
• Setting up Python development environments
• Writing or updating tests
• Reviewing Python code for quality and best practices

Code Quality

Principles

• DRY (Don't Repeat Yourself): Avoid code duplication
• Composition over inheritance: Prefer composition patterns
• Pure functions when possible: Functions without side effects
• Simple solutions over clever ones: Prioritize readability and maintainability
• Design for common use cases first: Solve the primary problem before edge cases

Style & Documentation

• Type hints required: All functions must include type annotations
• snake_case naming: Use snake_case for variables, functions, and modules
• Google-style docstrings: Document functions, classes, and modules using Google-style docstrings
• Keep functions small: Single responsibility principle - one function, one purpose
• Preserve existing comments: Maintain and update existing code comments

Example

def calculate_total(items: list[dict[str, float]], tax_rate: float = 0.08) -> float:
    """Calculate total cost including tax.

    Args:
        items: List of items with 'price' key
        tax_rate: Tax rate as decimal (default 0.08)

    Returns:
        Total cost including tax

    Raises:
        ValueError: If tax_rate is negative or items list is empty
    """
    if not items:
        raise ValueError("Items list cannot be empty")
    if tax_rate < 0:
        raise ValueError("Tax rate cannot be negative")

    subtotal = sum(item['price'] for item in items)
    return subtotal * (1 + tax_rate)

Error Handling & Efficiency

Error Handling

• Specific exception types: Catch specific exceptions, not bare except
• Validate inputs early: Check inputs at function entry
• No bare except: Always specify exception types

Efficiency Patterns

• f-strings: Use f-strings for string formatting
• Comprehensions: Prefer list/dict/set comprehensions over loops when appropriate
• Context managers: Use with statements for resource management

Example

def process_file(file_path: str) -> list[str]:
    """Process file and return lines.

    Args:
        file_path: Path to file

    Returns:
        List of non-empty lines

    Raises:
        FileNotFoundError: If file doesn't exist
        PermissionError: If file cannot be read
    """
    if not file_path:
        raise ValueError("File path cannot be empty")

    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            return [line.strip() for line in f if line.strip()]
    except FileNotFoundError:
        raise FileNotFoundError(f"File not found: {file_path}")
    except PermissionError:
        raise PermissionError(f"Permission denied: {file_path}")

Testing (Critical)

Framework & Structure

• pytest only: Use pytest exclusively (no unittest)
• Test location: All tests in ./tests/ directory
• Test package: Include __init__.py in tests directory
• TDD approach: Write/update tests for all new/modified code
• All tests must pass: Ensure all tests pass before task completion

Test Structure Example

project/
├── src/
│   └── my_module.py
└── tests/
    ├── __init__.py
    └── test_my_module.py

Example Test

# tests/test_calculations.py
import pytest
from src.calculations import calculate_total

def test_calculate_total_basic():
    """Test basic total calculation."""
    items = [{'price': 10.0}, {'price': 20.0}]
    result = calculate_total(items, tax_rate=0.1)
    assert result == 33.0

def test_calculate_total_empty_list():
    """Test error handling for empty list."""
    with pytest.raises(ValueError, match="Items list cannot be empty"):
        calculate_total([])

def test_calculate_total_negative_tax():
    """Test error handling for negative tax rate."""
    items = [{'price': 10.0}]
    with pytest.raises(ValueError, match="Tax rate cannot be negative"):
        calculate_total(items, tax_rate=-0.1)

Environment Management

Dependency Management

• Use uv exclusively: All packaging, environment, and script execution via uv
• No pip/venv/conda: Do not use pip, python3 -m venv, or conda — uv handles all of this
• pyproject.toml is the source of truth: Define all dependencies in pyproject.toml (not requirements.txt)

Environment Setup Example

# Install dependencies from pyproject.toml
uv sync

# Install with optional dev dependencies
uv sync --extra dev

# Run a script (no activation needed)
uv run python script.py

# Run pytest
uv run pytest

# Add a new dependency
uv add requests

# Remove a dependency
uv remove requests

Running Python Code

• Use uv run to execute scripts — no manual venv activation needed
• Use uv run <tool> for dev tools (pytest, ruff, etc.)
• Dependencies are defined in pyproject.toml (not requirements.txt)

Linting & Formatting (Ruff)

• Ruff: Use Ruff for linting AND formatting (replaces flake8, black, isort)

# Lint code
uv run ruff check .

# Lint and auto-fix
uv run ruff check --fix .

# Format code
uv run ruff format .

# Check formatting without changes
uv run ruff format --check .

Type Checking (Pyright)

# Check types
uv run pyright

Note: Use pyright for type checking — do not use mypy.

Best Practices Summary

1. Code Quality: DRY, composition, pure functions, simple solutions
2. Style: Type hints, snake_case, Google docstrings, small functions
3. Errors: Specific exceptions, early validation, no bare except
4. Efficiency: f-strings, comprehensions, context managers
5. Testing: pytest only, TDD, tests in ./tests/, all must pass
6. Environment: Use uv exclusively for dependencies and execution, Ruff for linting/formatting, Pyright for type checking