python
Python engineering guidance for type hints, pydantic validation, httpx usage, pytest conventions, pyproject structure, async patterns, and lightweight dependency injection. Use when building or reviewing Python services and libraries.
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- 2026年4月17日 23:33
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- python
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- Python engineering guidance for type hints, pydantic validation, httpx usage, pytest conventions, pyproject structure, async patterns, and lightweight dependency injection. Use when building or reviewing Python services and libraries.
# Python
Prefer typed, explicit Python with validation at boundaries, modern async I/O, and simple seams over framework-heavy indirection.
## When to Activate
- Add or refactor Python modules
- Choose a model type for request or domain data
- Write an outbound HTTP integration
- Add async handlers or background jobs
- Configure pytest, typing, or packaging
- Replace hidden globals with explicit dependencies
- Review Python code for type and validation drift
## Typing and Modeling
| Pattern | Preferred | Avoid |
| --- | --- | --- |
| Public functions | Full parameter and return annotations | Unannotated signatures |
| Collections | `list[str]`, `dict[str, int]` | `List`, `Dict` unless compatibility requires it |
| Optional values | `str | None` | Implicit `None` returns |
| Need | Use |
| --- | --- |
| External input validation | `pydantic.BaseModel` |
| Internal immutable value object | `@dataclass(frozen=True)` |
| Loosely structured mapping shape | `TypedDict` |
## HTTP and Async
BAD
```python
import requests
response = requests.get(url, timeout=30)
```
GOOD
```python
import httpx
async with httpx.AsyncClient(timeout=10.0) as client:
response = await client.get(url)
```
| Rule | Why |
| --- | --- |
| Use `async` only for real I/O boundaries | Avoid fake async complexity |
| Do not call blocking I/O inside async handlers | Prevent event loop stalls |
| Batch concurrent I/O with `asyncio.TaskGroup` or `gather` where safe | Improve latency deterministically |
## Project Layout and DI
| Section | Purpose |
| --- | --- |
| `[project]` | Metadata and dependencies |
| `[tool.pytest.ini_options]` | Test discovery and markers |
| `[tool.ruff]` or `[tool.black]` | Lint and format configuration |
| `[tool.mypy]` | Type checking configuration |
| Preferred | Avoid |
| --- | --- |
| Pass collaborators through constructors or function parameters | Global singletons hidden inside modules |
| Build objects in a wiring layer | Import-time side effects creating clients |
BAD
```python
def create_user(data):
return User(**data)
```
GOOD
```python
class CreateUserInput(BaseModel):
email: EmailStr
name: str
def create_user(data: CreateUserInput) -> User:
return User(email=data.email, name=data.name)
```
## Checklist
- [ ] Public functions and methods have type hints
- [ ] Boundary inputs are validated with pydantic where appropriate
- [ ] `httpx` is used for outbound HTTP unless a project standard overrides it
- [ ] Async code avoids blocking calls
- [ ] `pyproject.toml` centralizes tool configuration
- [ ] Data models use `BaseModel`, `dataclass`, or `TypedDict` intentionally
- [ ] Dependencies are injected explicitly
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