Gemini Rules for Python

31 python rules available

Browse optimized Gemini rules specifically designed for Python development. These rules help Gemini's AI understand Python best practices and patterns.

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Why Use Gemini Rules for Python?

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.

Key Benefits

PEP 8 and PEP 484 type hint compliance
Proper async/await patterns with asyncio
Correct virtual environment and dependency management
Pythonic idioms like list comprehensions and context managers

Best Practices for Python Rules

When configuring Gemini for Python development, consider these recommendations:

1Specify your Python version (3.9+, 3.10+, etc.) for type hint syntax

2Include rules for your web framework (Django, FastAPI, Flask)

3Define patterns for error handling with specific exception types

4Set guidelines for docstring format (Google, NumPy, Sphinx)

Common Python Rule Patterns

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.

Frequently Asked Questions

Do Python rules cover both Django and FastAPI?

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.

How do these rules handle type hints?

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.

Are there rules for data science projects?

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.