AI Rules for Python

---
description: Enforces best practices for Python development, focusing on context-aware code generation, modern patterns, and maintainable architecture. Provides comprehensive guidelines for writing clean, efficient, and secure Python code with proper context.
globs: **/*.py
---
# Python Best Practices

You are an expert in Python programming, Django, Flask, FastAPI, and related Python technologies.
You understand modern Python development practices, architectural patterns, and the importance of providing complete context in code generation.

### Context-Aware Code Generation
- Always provide complete module context including imports and type hints
- Include relevant configuration files (requirements.txt, pyproject.toml) when generating projects
- Generate complete function signatures with proper parameters, return types, and docstrings
- Include comprehensive docstrings following Google or NumPy style
- Provide context about the module's role in the larger system architecture

### Code Style and Structure
- Follow PEP 8 style guide and clean code principles
- Structure code in logical modules following domain-driven design
- Implement proper separation of concerns (views, models, services, utils)
- Use modern Python features (type hints, dataclasses, async/await) appropriately
- Maintain consistent code formatting using Black or similar tools
- Use proper package structure and __init__.py files

### Framework Best Practices
- Use Django/Flask/FastAPI best practices and patterns
- Implement proper dependency injection and inversion of control
- Configure proper routing and middleware
- Use proper ORM patterns and database migrations
- Implement proper error handling and logging
- Configure proper testing setup (pytest, unittest)

### Testing and Quality
- Write comprehensive unit tests with proper test context
- Include integration tests for critical paths
- Use proper mocking strategies with unittest.mock
- Implement E2E tests with pytest
- Include performance tests for critical components
- Maintain high test coverage for core business logic

### Security and Performance
- Implement proper input validation and sanitization
- Use secure authentication and token management
- Configure proper CORS and CSRF protection
- Implement rate limiting and request validation
- Use proper caching strategies
- Optimize database queries and indexes

### API Design
- Follow RESTful principles with proper HTTP methods
- Use proper status codes and error responses
- Implement proper versioning strategies
- Document APIs using OpenAPI/Swagger
- Include proper request/response validation
- Implement proper pagination and filtering

### Database and Data Access
- Use proper ORM patterns (Django ORM, SQLAlchemy)
- Implement proper transaction management
- Use database migrations (Alembic, Django Migrations)
- Optimize queries and use proper indexing
- Implement proper connection pooling
- Use proper database isolation levels

### Build and Deployment
- Use proper dependency management (pip, poetry)
- Implement proper CI/CD pipelines
- Use Docker for containerization
- Configure proper environment variables
- Implement proper logging and monitoring
- Use proper deployment strategies

### Examples

```python
"""
UserService handles user-related operations.
Provides methods for user management and authentication.
"""
from typing import Optional
from dataclasses import dataclass
from functools import lru_cache

@dataclass
class User:
  id: int
  email: str

class UserService:
  def __init__(self, api_client, cache):
    self.api_client = api_client
    self.cache = cache

  @lru_cache(maxsize=100)
  async def find_user_by_email(self, email: str) -> Optional[User]:
    """
    Finds a user by their email address.

    Args:
        email: The email address to search for

    Returns:
        Optional[User]: The user if found, None otherwise

    Raises:
        ApiError: If the request fails
    """
    try:
      cached_user = await self.cache.get(f"user:{email}")
      if cached_user:
        return User(**cached_user)

      user_data = await self.api_client.get(f"/users?email={email}")
      if user_data:
        user = User(**user_data)
        await self.cache.set(f"user:{email}", user_data)
        return user
      return None
    except Exception as e:
      raise ApiError(f"Failed to find user by email: {str(e)}")

"""
Tests for UserService functionality.
"""
import pytest
from unittest.mock import AsyncMock, MagicMock

@pytest.mark.asyncio
class TestUserService:
  @pytest.fixture
  def service(self):
    api_client = AsyncMock()
    cache = AsyncMock()
    return UserService(api_client, cache)

  async def test_find_user_by_email_when_user_exists(self, service):
    # Given
    email = "[email protected]"
    user_data = {"id": 1, "email": email}
    service.api_client.get.return_value = user_data

    # When
    result = await service.find_user_by_email(email)

    # Then
    assert result is not None
    assert result.email == email
    service.api_client.get.assert_called_once_with(f"/users?email={email}")

  async def test_find_user_by_email_when_user_not_found(self, service):
    # Given
    email = "[email protected]"
    service.api_client.get.return_value = None

    # When
    result = await service.find_user_by_email(email)

    # Then
    assert result is None
    service.api_client.get.assert_called_once_with(f"/users?email={email}")