python-patterns

Choosing between FastAPI, Django, and Flask depends on your project goals. A decision framework selects FastAPI for API-first designs, Django for full-stack projects, and Flask for flexible scripting, ensuring the right Python web framework fits your architecture.

Use async Python patterns for I/O-bound tasks to handle concurrent operations efficiently, and sync patterns for CPU-bound workloads. This guidance balances task types, preventing event loop blocking and improving application throughput.

The best way to structure a large Python application is following project-structure principles that scale from small to large layouts. This approach organizes modules, packages, and resources systematically to improve maintainability and reliability.

Implement type hints using Optional, Union, and Generics for static analysis, combined with Pydantic for runtime validation. This typing strategy enforces data integrity, catches errors early, and clarifies API contracts across your Python codebase.

Avoid Python anti-patterns like improper error handling and blocking async calls to improve reliability. Following testing, error handling, and structural best practices prevents common architectural flaws and keeps your codebase maintainable.

Yes, this Python patterns guidance works for both scripting and full-stack web development. It provides tailored decision trees, typing strategies, and structural principles that adapt across API design, data processing, and comprehensive web projects.