python-patterns

Python Patterns

Python development principles and decision-making for 2025. Learn to THINK, not memorize patterns.

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⚠️ How to Use This Skill

This skill teaches decision-making principles, not fixed code to copy.

• ASK user for framework preference when unclear
• Choose async vs sync based on CONTEXT
• Don't default to same framework every time

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1. Framework Selection (2025)

Decision Tree

What are you building?
│
├── API-first / Microservices
│   └── FastAPI (async, modern, fast)
│
├── Full-stack web / CMS / Admin
│   └── Django (batteries-included)
│
├── Simple / Script / Learning
│   └── Flask (minimal, flexible)
│
├── AI/ML API serving
│   └── FastAPI (Pydantic, async, uvicorn)
│
└── Background workers
    └── Celery + any framework

Comparison Principles

Factor	FastAPI	Django	Flask
Best for	APIs, microservices	Full-stack, CMS	Simple, learning
Async	Native	Django 5.0+	Via extensions
Admin	Manual	Built-in	Via extensions
ORM	Choose your own	Django ORM	Choose your own
Learning curve	Low	Medium	Low

Selection Questions to Ask:

1. Is this API-only or full-stack?
2. Need admin interface?
3. Team familiar with async?
4. Existing infrastructure?

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2. Async vs Sync Decision

When to Use Async

async def is better when:
├── I/O-bound operations (database, HTTP, file)
├── Many concurrent connections
├── Real-time features
├── Microservices communication
└── FastAPI/Starlette/Django ASGI

def (sync) is better when:
├── CPU-bound operations
├── Simple scripts
├── Legacy codebase
├── Team unfamiliar with async
└── Blocking libraries (no async version)

The Golden Rule

I/O-bound → async (waiting for external)
CPU-bound → sync + multiprocessing (computing)

Don't:
├── Mix sync and async carelessly
├── Use sync libraries in async code
└── Force async for CPU work

Async Library Selection