20 python rules available
Browse optimized Cursor rules specifically designed for Python development. These rules help Cursor's AI understand Python best practices and patterns.
Python's flexibility means AI-generated code can vary widely in style. Rules that specify PEP 8 compliance, type hints (Python 3.9+), and your preferred async patterns help maintain consistency. Whether you're building Django applications, FastAPI services, or data science pipelines, proper rules ensure idiomatic Python code.
When configuring Cursor for Python development, consider these recommendations:
Python rules commonly cover type hints with modern syntax, proper project structure with __init__.py files, and framework-specific patterns. Data science rules often include NumPy/Pandas conventions, while web development rules focus on Django or FastAPI patterns.
Most general Python rules work for any framework, but for best results, look for framework-specific rules. Django rules include model patterns and ORM usage, while FastAPI rules focus on Pydantic models and dependency injection.
Modern Python rules typically expect type hints using the latest syntax (list[str] instead of List[str]). They guide the AI to use proper return types, Optional types, and Union types for comprehensive type coverage.
Yes, look for rules tagged with 'data-science', 'pandas', or 'jupyter'. These include patterns for notebook cell organization, DataFrame operations, and visualization with matplotlib or plotly.
Cursor rules for building high-performance APIs with FastAPI, including async patterns and Pydantic.
Cursor rules for data analysis and manipulation using Pandas, NumPy, and data visualization.
Rules for Python backend development with FastAPI, including async patterns, type hints, and API design.
Best practices for Django web applications
Comprehensive testing strategies using pytest
Async/await patterns for Python applications
Structural typing with Protocol
Create custom context managers
Implement custom descriptors
Manage dependencies with Poetry
Use __slots__ for memory efficiency
Use structural pattern matching
Comprehensive type hinting guidelines for Python code
Organize Python packages for maintainability
Advanced class creation with metaclasses
Fast Python linting with Ruff
Use Pydantic V2 for data validation
Create memory-efficient iterators
Effective use of dataclasses in Python
Build robust APIs with FastAPI
Create your own Cursor rules for Python and share with the community.