python-expert

> **AI-consumed reference.** Optimized for Claude to read during execution. > Human-readable explanation: see [docs/architecture/HIERARCHICAL_PLANNING.md](../../../docs/architecture/HIERARCHICAL_PLANNING.md) > or [docs/getting-started/](../../../docs/getting-started/) depending on topic. # Python Expert — Gotchas & Decisions Use Context7 for FastAPI/Django/Flask docs. ## Key Decisions ```toon decisions[4]{choice,use_when}: FastAPI vs Django vs Flask,"FastAPI: async APIs + auto-docs. Django: full-featured + ORM + admin. Flask: minimal/micro" Pydantic vs dataclass,"Pydantic for validation/serialization (API boundaries). dataclass for internal structs" SQLAlchemy vs Django ORM,"SQLAlchemy: standalone/FastAPI. Django ORM: Django projects only" sync vs async,"async for I/O-bound (HTTP/DB). sync for CPU-bound. Don't mix blocking calls in async" ``` ## Gotchas - Mutable default args: `def f(items=[])` shares list across calls — use `def f(items=None): items = items or []` - `asyncio.run()` creates new event loop — can't nest. Use `await` inside existing async context - FastAPI `Depends()`: new instance per request by default. Use `@lru_cache` for singletons - Django N+1: use `select_related` (FK/OneToOne) and `prefetch_related` (M2M/reverse FK) - `isinstance(x, int)` catches `bool` too — `bool` is subclass of `int`. Check `type(x) is int` if needed - Type hints are NOT enforced at runtime — use Pydantic or `beartype` for runtime validation - `dict.get('key')` returns ...