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name: python-patterns description: Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Use when making Python architecture decisions, choosing frameworks, or structuring Python projects. tags: [python, fastapi, django, patterns]

Python Patterns

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

When to Use

Use this skill when making Python architecture decisions, choosing frameworks, designing async patterns, or structuring Python projects.

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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

Need	Async Library
HTTP client	httpx
PostgreSQL	asyncpg
Redis	aioredis / redis-py async
File I/O	aiofiles
Database ORM	SQLAlchemy 2.0 async, Tortoise

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3. Type Hints Strategy

When to Type

Always type:
+-- Function parameters
+-- Return types
+-- Class attributes
+-- Public APIs

Can skip:
+-- Local variables (let inference work)
+-- One-off scripts
+-- Tests (usually)

Common Type Patterns

# These are patterns, understand them:

# Optional -> might be None
from typing import Optional
def find_user(id: int) -> Optional[User]: ...

# Union -> one of multiple types
def process(data: str | dict) -> None: ...

# Generic collections
def get_items() -> list[Item]: ...
def get_mapping() -> dict[str, int]: ...

# Callable
from typing import Callable
def apply(fn: Callable[[int], str]) -> str: ...

Pydantic for Validation

When to use Pydantic:
+-- API request/response models
+-- Configuration/settings
+-- Data validation
+-- Serialization

Benefits:
+-- Runtime validation
+-- Auto-generated JSON schema
+-- Works with FastAPI natively
+-- Clear error messages

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4. Project Structure Principles

Structure Selection

Small project / Script:
+-- main.py
+-- utils.py
+-- requirements.txt

Medium API:
+-- app/
|   +-- __init__.py
|   +-- main.py
|   +-- models/
|   +-- routes/
|   +-- services/
|   +-- schemas/
+-- tests/
+-- pyproject.toml

Large application:
+-- src/
|   +-- myapp/
|       +-- core/
|       +-- api/
|       +-- services/
|       +-- models/
|       +-- ...
+-- tests/
+-- pyproject.toml

FastAPI Structure Principles

Organize by feature or layer:

By layer:
+-- routes/ (API endpoints)
+-- services/ (business logic)
+-- models/ (database models)
+-- schemas/ (Pydantic models)
+-- dependencies/ (shared deps)

By feature:
+-- users/
|   +-- routes.py
|   +-- service.py
|   +-- schemas.py
+-- products/
    +-- ...

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5. Django Principles (2025)

Django Async (Django 5.0+)

Django supports async:
+-- Async views
+-- Async middleware
+-- Async ORM (limited)
+-- ASGI deployment

When to use async in Django:
+-- External API calls
+-- WebSocket (Channels)
+-- High-concurrency views
+-- Background task triggering

Django Best Practices

Model design:
+-- Fat models, thin views
+-- Use managers for common queries
+-- Abstract base classes for shared fields

Views:
+-- Class-based for complex CRUD
+-- Function-based for simple endpoints
+-- Use viewsets with DRF

Queries:
+-- select_related() for FKs
+-- prefetch_related() for M2M
+-- Avoid N+1 queries
+-- Use .only() for specific fields

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6. FastAPI Principles

async def vs def in FastAPI

Use async def when:
+-- Using async database drivers
+-- Making async HTTP calls
+-- I/O-bound operations
+-- Want to handle concurrency

Use def when:
+-- Blocking operations
+-- Sync database drivers
+-- CPU-bound work
+-- FastAPI runs in threadpool automatically

Dependency Injection

Use dependencies for:
+-- Database sessions
+-- Current user / Auth
+-- Configuration
+-- Shared resources

Benefits:
+-- Testability (mock dependencies)
+-- Clean separation
+-- Automatic cleanup (yield)

Pydantic v2 Integration

# FastAPI + Pydantic are tightly integrated:

# Request validation
@app.post("/users")
async def create(user: UserCreate) -> UserResponse:
    # user is already validated
    ...

# Response serialization
# Return type becomes response schema

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7. Background Tasks

Selection Guide

Solution	Best For
BackgroundTasks	Simple, in-process tasks
Celery	Distributed, complex workflows
ARQ	Async, Redis-based
RQ	Simple Redis queue
Dramatiq	Actor-based, simpler than Celery

When to Use Each

FastAPI BackgroundTasks:
+-- Quick operations
+-- No persistence needed
+-- Fire-and-forget
+-- Same process

Celery/ARQ:
+-- Long-running tasks
+-- Need retry logic
+-- Distributed workers
+-- Persistent queue
+-- Complex workflows

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8. Error Handling Principles

Exception Strategy

In FastAPI:
+-- Create custom exception classes
+-- Register exception handlers
+-- Return consistent error format
+-- Log without exposing internals

Pattern:
+-- Raise domain exceptions in services
+-- Catch and transform in handlers
+-- Client gets clean error response

Error Response Philosophy

Include:
+-- Error code (programmatic)
+-- Message (human readable)
+-- Details (field-level when applicable)
+-- NOT stack traces (security)

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9. Testing Principles

Testing Strategy

Type	Purpose	Tools
Unit	Business logic	pytest
Integration	API endpoints	pytest + httpx/TestClient
E2E	Full workflows	pytest + DB

Async Testing

# Use pytest-asyncio for async tests

import pytest
from httpx import AsyncClient

@pytest.mark.asyncio
async def test_endpoint():
    async with AsyncClient(app=app, base_url="http://test") as client:
        response = await client.get("/users")
        assert response.status_code == 200

Fixtures Strategy

Common fixtures:
+-- db_session -> Database connection
+-- client -> Test client
+-- authenticated_user -> User with token
+-- sample_data -> Test data setup

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10. Decision Checklist

Before implementing:

• Asked user about framework preference?
• Chosen framework for THIS context? (not just default)
• Decided async vs sync?
• Planned type hint strategy?
• Defined project structure?
• Planned error handling?
• Considered background tasks?

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11. Anti-Patterns to Avoid

DON'T:

• Default to Django for simple APIs (FastAPI may be better)
• Use sync libraries in async code
• Skip type hints for public APIs
• Put business logic in routes/views
• Ignore N+1 queries
• Mix async and sync carelessly

DO:

• Choose framework based on context
• Ask about async requirements
• Use Pydantic for validation
• Separate concerns (routes -> services -> repos)
• Test critical paths

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Remember: Python patterns are about decision-making for YOUR specific context. Don't copy code--think about what serves your application best.

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