---
name: python-patterns
description: Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.
---

# Python 开发模式 (Python Development Patterns)

构建健壮、高效且可维护的应用程序的 Pythonic 惯用模式和最佳实践。

## 激活时机 (When to Activate)

- 编写新的 Python 代码时
- 进行 Python 代码审查（Review）时
- 重构现有的 Python 代码时
- 设计 Python 包（Package）或模块（Module）时

## 核心原则

### 1. 可读性至上 (Readability Counts)

Python 优先考虑可读性。代码应当直观且易于理解。

```python
# Good: 清晰且易读
def get_active_users(users: list[User]) -> list[User]:
    """仅从提供的列表中返回活跃用户。"""
    return [user for user in users if user.is_active]


# Bad: 巧妙但令人困惑
def get_active_users(u):
    return [x for x in u if x.a]
```

### 2. 显式优于隐式 (Explicit is Better Than Implicit)

避免“魔法”行为；清晰地表达代码的功能。

```python
# Good: 显式配置
import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Bad: 隐藏的副作用
import some_module
some_module.setup()  # 这到底做了什么？
```

### 3. EAFP 模式 - 请求宽恕比请求许可更容易 (Easier to Ask Forgiveness Than Permission)

Python 倾向于使用异常处理而非预先检查条件。

```python
# Good: EAFP 风格
def get_value(dictionary: dict, key: str) -> Any:
    try:
        return dictionary[key]
    except KeyError:
        return default_value

# Bad: LBYL (Look Before You Leap，三思而后行) 风格
def get_value(dictionary: dict, key: str) -> Any:
    if key in dictionary:
        return dictionary[key]
    else:
        return default_value
```

## 类型提示 (Type Hints)

### 基础类型注解

```python
from typing import Optional, List, Dict, Any

def process_user(
    user_id: str,
    data: Dict[str, Any],
    active: bool = True
) -> Optional[User]:
    """处理用户并返回更新后的 User 或 None。"""
    if not active:
        return None
    return User(user_id, data)
```

### 现代类型提示 (Python 3.9+)

```python
# Python 3.9+ - 使用内置类型
def process_items(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

# Python 3.8 及更早版本 - 使用 typing 模块
from typing import List, Dict

def process_items(items: List[str]) -> Dict[str, int]:
    return {item: len(item) for item in items}
```

### 类型别名 (Type Aliases) 和 TypeVar

```python
from typing import TypeVar, Union

# 复杂类型的类型别名
JSON = Union[dict[str, Any], list[Any], str, int, float, bool, None]

def parse_json(data: str) -> JSON:
    return json.loads(data)

# 泛型
T = TypeVar('T')

def first(items: list[T]) -> T | None: 
    """返回第一项，如果列表为空则返回 None。"""
    return items[0] if items else None
```

### 基于协议 (Protocol) 的鸭子类型 (Duck Typing)

```python
from typing import Protocol

class Renderable(Protocol):
    def render(self) -> str:
        """将对象渲染为字符串。"""

def render_all(items: list[Renderable]) -> str:
    """渲染所有实现了 Renderable 协议的项目。"""
    return "\n".join(item.render() for item in items)
```

## 异常处理模式 (Error Handling Patterns)

### 特定异常处理

```python
# Good: 捕获特定的异常
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except FileNotFoundError as e:
        raise ConfigError(f"Config file not found: {path}") from e
    except json.JSONDecodeError as e:
        raise ConfigError(f"Invalid JSON in config: {path}") from e

# Bad: 空异常捕获
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except:
        return None  # 静默失败！
```

### 异常链 (Exception Chaining)

```python
def process_data(data: str) -> Result:
    try:
        parsed = json.loads(data)
    except json.JSONDecodeError as e:
        # 使用异常链以保留堆栈跟踪 (traceback)
        raise ValueError(f"Failed to parse data: {data}") from e
```

### 自定义异常层次结构

```python
class AppError(Exception):
    """所有应用程序错误的基类。"""
    pass

class ValidationError(AppError):
    """当输入验证失败时引发。"""
    pass

class NotFoundError(AppError):
    """当请求的资源未找到时引发。"""
    pass

# 使用示例
def get_user(user_id: str) -> User:
    user = db.find_user(user_id)
    if not user:
        raise NotFoundError(f"User not found: {user_id}")
    return user
```

## 上下文管理器 (Context Managers)

### 资源管理

```python
# Good: 使用上下文管理器
def process_file(path: str) -> str:
    with open(path, 'r') as f:
        return f.read()

# Bad: 手动资源管理
def process_file(path: str) -> str:
    f = open(path, 'r')
    try:
        return f.read()
    finally:
        f.close()
```

### 自定义上下文管理器

```python
from contextlib import contextmanager

@contextmanager
def timer(name: str):
    """用于对代码块计时的上下文管理器。"""
    start = time.perf_counter()
    yield
    elapsed = time.perf_counter() - start
    print(f"{name} took {elapsed:.4f} seconds")

# 使用示例
with timer("data processing"):
    process_large_dataset()
```

### 上下文管理器类

```python
class DatabaseTransaction:
    def __init__(self, connection):
        self.connection = connection

    def __enter__(self):
        self.connection.begin_transaction()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is None:
            self.connection.commit()
        else:
            self.connection.rollback()
        return False  # 不要抑制异常

# 使用示例
with DatabaseTransaction(conn):
    user = conn.create_user(user_data)
    conn.create_profile(user.id, profile_data)
```

## 推导式 (Comprehensions) 与生成器 (Generators)

### 列表推导式 (List Comprehensions)

```python
# Good: 用于简单转换的列表推导式
names = [user.name for user in users if user.is_active]

# Bad: 手动循环
names = []
for user in users:
    if user.is_active:
        names.append(user.name)

# 复杂的推导式应当展开
# Bad: 太过复杂
result = [x * 2 for x in items if x > 0 if x % 2 == 0]

# Good: 使用生成器函数
def filter_and_transform(items: Iterable[int]) -> list[int]:
    result = []
    for x in items:
        if x > 0 and x % 2 == 0:
            result.append(x * 2)
    return result
```

### 生成器表达式 (Generator Expressions)

```python
# Good: 用于延迟求值的生成器
total = sum(x * x for x in range(1_000_000))

# Bad: 创建了巨大的中间列表
total = sum([x * x for x in range(1_000_000)])
```

### 生成器函数 (Generator Functions)

```python
def read_large_file(path: str) -> Iterator[str]:
    """逐行读取大文件。"""
    with open(path) as f:
        for line in f:
            yield line.strip()

# 使用示例
for line in read_large_file("huge.txt"):
    process(line)
```

## 数据类 (Data Classes) 与命名元组 (Named Tuples)

### 数据类 (Data Classes)

```python
from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class User:
    """具有自动生成 __init__、__repr__ 和 __eq__ 的用户实体。"""
    id: str
    name: str
    email: str
    created_at: datetime = field(default_factory=datetime.now)
    is_active: bool = True

# 使用示例
user = User(
    id="123",
    name="Alice",
    email="alice@example.com"
)
```

### 带验证的数据类

```python
@dataclass
class User:
    email: str
    age: int

    def __post_init__(self):
        # 验证电子邮件格式
        if "@" not in self.email:
            raise ValueError(f"Invalid email: {self.email}")
        # 验证年龄范围
        if self.age < 0 or self.age > 150:
            raise ValueError(f"Invalid age: {self.age}")
```

### 命名元组 (Named Tuples)

```python
from typing import NamedTuple

class Point(NamedTuple):
    """不可变的二维点。"""
    x: float
    y: float

    def distance(self, other: 'Point') -> float:
        return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5

# 使用示例
p1 = Point(0, 0)
p2 = Point(3, 4)
print(p1.distance(p2))  # 5.0
```

## 装饰器 (Decorators)

### 函数装饰器

```python
import functools
import time

def timer(func: Callable) -> Callable:
    """用于对函数执行进行计时的装饰器。"""
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        elapsed = time.perf_counter() - start
        print(f"{func.__name__} took {elapsed:.4f}s")
        return result
    return wrapper

@timer
def slow_function():
    time.sleep(1)

# slow_function() 输出: slow_function took 1.0012s
```

### 参数化装饰器

```python
def repeat(times: int):
    """用于多次重复执行函数的装饰器。"""
    def decorator(func: Callable) -> Callable:
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            results = []
            for _ in range(times):
                results.append(func(*args, **kwargs))
            return results
        return wrapper
    return decorator

@repeat(times=3)
def greet(name: str) -> str:
    return f"Hello, {name}!"

# greet("Alice") 返回 ["Hello, Alice!", "Hello, Alice!", "Hello, Alice!"]
```

### 基于类的装饰器

```python
class CountCalls:
    """统计函数被调用次数的装饰器。"""
    def __init__(self, func: Callable):
        functools.update_wrapper(self, func)
        self.func = func
        self.count = 0

    def __call__(self, *args, **kwargs):
        self.count += 1
        print(f"{self.func.__name__} has been called {self.count} times")
        return self.func(*args, **kwargs)

@CountCalls
def process():
    pass

# 每次调用 process() 都会打印调用计数
```

## 并发模式 (Concurrency Patterns)

### 线程 (Threading) 处理 I/O 密集型任务

```python
import concurrent.futures
import threading

def fetch_url(url: str) -> str:
    """获取 URL (I/O 密集型操作)。"""
    import urllib.request
    with urllib.request.urlopen(url) as response:
        return response.read().decode()

def fetch_all_urls(urls: list[str]) -> dict[str, str]:
    """使用线程并发地获取多个 URL。"""
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        future_to_url = {executor.submit(fetch_url, url): url for url in urls}
        results = {}
        for future in concurrent.futures.as_completed(future_to_url):
            url = future_to_url[future]
            try:
                results[url] = future.result()
            except Exception as e:
                results[url] = f"Error: {e}"
    return results
```

### 多进程 (Multiprocessing) 处理 CPU 密集型任务

```python
def process_data(data: list[int]) -> int:
    """CPU 密集型计算。"""
    return sum(x ** 2 for x in data)

def process_all(datasets: list[list[int]]) -> list[int]:
    """使用多个进程处理多个数据集。"""
    with concurrent.futures.ProcessPoolExecutor() as executor:
        results = list(executor.map(process_data, datasets))
    return results
```

### Async/Await 处理并发 I/O

```python
import asyncio

async def fetch_async(url: str) -> str:
    """异步获取 URL。"""
    import aiohttp
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

async def fetch_all(urls: list[str]) -> dict[str, str]:
    """并发地获取多个 URL。"""
    tasks = [fetch_async(url) for url in urls]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return dict(zip(urls, results))
```

## 包组织 (Package Organization)

### 标准项目布局

```
myproject/
├── src/
│   └── mypackage/
│       ├── __init__.py
│       ├── main.py
│       ├── api/
│       │   ├── __init__.py
│       │   └── routes.py
│       ├── models/
│       │   ├── __init__.py
│       │   └── user.py
│       └── utils/
│           ├── __init__.py
│           └── helpers.py
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   ├── test_api.py
│   └── test_models.py
├── pyproject.toml
├── README.md
└── .gitignore
```

### 导入规范

```python
# Good: 导入顺序 - 标准库、第三方库、本地库
import os
import sys
from pathlib import Path

import requests
from fastapi import FastAPI

from mypackage.models import User
from mypackage.utils import format_name

# Good: 使用 isort 自动进行导入排序
# pip install isort
```

### 用于包导出的 __init__.py

```python
# mypackage/__init__.py
"""mypackage - 一个 Python 包示例。"""

__version__ = "1.0.0"

# 在包级别导出主要的类/函数
from mypackage.models import User, Post
from mypackage.utils import format_name

__all__ = ["User", "Post", "format_name"]
```

## 内存与性能

### 使用 __slots__ 提高内存效率

```python
# Bad: 常规类使用 __dict__ (占用更多内存)
class Point:
    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y

# Good: __slots__ 减少内存使用
class Point:
    __slots__ = ['x', 'y']

    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y
```

### 用于大数据的生成器

```python
# Bad: 在内存中返回完整列表
def read_lines(path: str) -> list[str]:
    with open(path) as f:
        return [line.strip() for line in f]

# Good: 一次产出一行
def read_lines(path: str) -> Iterator[str]:
    with open(path) as f:
        for line in f:
            yield line.strip()
```

### 避免在循环中进行字符串拼接

```python
# Bad: 由于字符串不可变性，复杂度为 O(n²)
result = ""
for item in items:
    result += str(item)

# Good: 使用 join，复杂度为 O(n)
result = "".join(str(item) for item in items)

# Good: 使用 StringIO 进行构建
from io import StringIO

buffer = StringIO()
for item in items:
    buffer.write(str(item))
result = buffer.getvalue()
```

## Python 工具链集成

### 常用命令

```bash
# 代码格式化
black .
isort .

# 静态检查 (Linting)
ruff check .
pylint mypackage/

# 类型检查
mypy .

# 测试
pytest --cov=mypackage --cov-report=html

# 安全扫描
bandit -r .

# 依赖管理
pip-audit
safety check
```

### pyproject.toml 配置

```toml
[project]
name = "mypackage"
version = "1.0.0"
requires-python = ">=3.9"
dependencies = [
    "requests>=2.31.0",
    "pydantic>=2.0.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.4.0",
    "pytest-cov>=4.1.0",
    "black>=23.0.0",
    "ruff>=0.1.0",
    "mypy>=1.5.0",
]

[tool.black]
line-length = 88
target-version = ['py39']

[tool.ruff]
line-length = 88
select = ["E", "F", "I", "N", "W"]

[tool.mypy]
python_version = "3.9"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "--cov=mypackage --cov-report=term-missing"
```

## 快速参考：Python 惯用法 (Python Idioms)

| 惯用法 | 描述 |
|-------|-------------|
| EAFP | 请求宽恕比请求许可更容易 (Easier to Ask Forgiveness than Permission) |
| 上下文管理器 (Context managers) | 使用 `with` 进行资源管理 |
| 列表推导式 (List comprehensions) | 用于简单转换 |
| 生成器 (Generators) | 用于延迟求值和大型数据集 |
| 类型提示 (Type hints) | 为函数签名添加注解 |
| 数据类 (Dataclasses) | 用于带有自动生成方法的各种数据容器 |
| `__slots__` | 用于内存优化 |
| f-strings | 用于字符串格式化 (Python 3.6+) |
| `pathlib.Path` | 用于路径操作 (Python 3.4+) |
| `enumerate` | 在循环中获取索引-元素对 |

## 应避免的反模式 (Anti-Patterns)

```python
# Bad: 可变默认参数
def append_to(item, items=[]):
    items.append(item)
    return items

# Good: 使用 None 并创建新列表
def append_to(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items

# Bad: 使用 type() 检查类型
if type(obj) == list:
    process(obj)

# Good: 使用 isinstance
if isinstance(obj, list):
    process(obj)

# Bad: 使用 == 与 None 比较
if value == None:
    process()

# Good: 使用 is
if value is None:
    process()

# Bad: from module import *
from os.path import *

# Good: 显式导入
from os.path import join, exists

# Bad: 空异常捕获
try:
    risky_operation()
except:
    pass

# Good: 特定异常
try:
    risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")
```

__记住__：Python 代码应当是易读的、显式的，并遵循最小惊讶原则。如有疑问，请优先考虑清晰度而非巧妙性。

```