Python Conventions

Enforce Python coding standards by applying PEP 8 formatting and naming guidelines, and use black, isort, and ruff to structure your code. This ensures your Python project remains clean and maintainable.

The best way to structure a scalable Python project is to follow a recommended layout that separates modules, tests, and configurations. This approach ensures maintainability and robustness as your modern Python application grows.

You should use dataclasses and Pydantic for data modeling and validation in Python. Both are recommended for robust data handling, with Pydantic especially useful for enforcing strict validation rules in your applications.

Set up pytest for unit and integration testing by following established Python testing best practices. This involves configuring your project structure to support modular tests and using dependency injection for more testable code.

Best practices for async patterns and error handling in Python include using custom exceptions and context managers for error management. These strategies guide developers in writing robust, efficient, and modern asynchronous applications.

Yes, the Python conventions guide covers security and logging. It provides a security checklist for secure Python development and guidance on effective logging and internationalization to ensure robust applications.