python-patterns

Python framework selection depends on your project context and specific requirements. Decision trees evaluate constraints across API, web, and script contexts to recommend the appropriate framework, ensuring type safety and modular design while avoiding verbatim pattern copying.

Async Python patterns suit I/O-bound tasks, while sync patterns fit CPU-bound workloads. Decision frameworks evaluate your project's specific I/O versus CPU constraints to guide this selection, ensuring clear separation of concerns and maintainable architecture across the application.

Python project structure principles organize large applications by feature or layer to ensure modular design. Applying these structural patterns maintains clear separation of concerns and type safety, scaling effectively from small scripts to complex web apps without copying patterns verbatim.

Python type hints strategy applies Optional, Union, and generics to enforce type safety and validation concepts. This approach integrates with framework selection and project structure decisions to maintain modular design and clear separation of concerns across the codebase.

Applying these Python patterns requires no external dependencies, as the framework focuses on decision-making principles rather than specific libraries. You identify project goals and constraints first, then use decision trees to guide framework, async, typing, and structure selection.