python-patterns

Choosing between FastAPI and Django depends on your project needs, as FastAPI suits asynchronous I/O-bound APIs while Django offers comprehensive features for larger applications. This Skill provides a decision tree to compare framework capabilities, guiding you toward the most suitable and maintainable architecture for your specific requirements.

Use asynchronous programming in Python for I/O-bound tasks to maximize concurrency, while synchronous code suffices for CPU-bound operations. This Skill provides clear criteria to evaluate your workloads, helping you decide when asyncio delivers performance benefits versus adding unnecessary complexity to your application architecture.

The best way to manage type hinting with Pydantic is to apply strict validation schemas at your application boundaries. This Skill recommends best practices for integrating Pydantic models, ensuring data integrity and maintainable code without over-complicating internal logic with redundant type checks.

To structure a maintainable Python project, organize modules by separating business logic from infrastructure and API layers. This Skill offers organizational templates tailored for varying project sizes, ensuring your codebase remains scalable and easy to navigate as the application grows.

Flask can handle background tasks, but frameworks like FastAPI offer native asynchronous support for more efficient I/O-bound task management. This Skill evaluates your error handling and background processing needs to determine if Flask meets your concurrency requirements or if a migration is necessary.