python-patterns

# Python 开发模式 用于构建稳健、高效且可维护应用的惯用 Python 模式与最佳实践。 ## 何时启用 - 编写新的 Python 代码时 - 审查 Python 代码时 - 重构现有 Python 代码时 - 设计 Python 包/模块时 ## 核心原则 ### 1. 可读性至关重要 Python 优先考虑可读性。代码应当直观且易于理解。 ```python # Good: 清晰且可读性强 def get_active_users(users: list[User]) -> list[User]: """返回提供列表中的活跃用户。""" 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. 明示优于暗示 避免“黑魔法”，确保代码意图明确。 ```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() # 这具体做了什么？ ``` ### 3. EAFP - 寻求原谅比请求许可更容易 Python 更倾向于异常处理，而非前置条件检查（It's Easier to Ask for Forgiveness than Permission）。 ```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) 风格，即“三思而后行” 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} ``` ### 类型别名与 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) # 泛型类型 T = TypeVar('T') def first(items: list[T]) -> T | None: """返回第一个项目，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 ```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) ``` ## 错误处理模式 ### 处理特定异常 ```python # Good: 捕获特定异常 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"未找到配置文件: {path}") from e except json.JSONDecodeError as e: raise ConfigError(f"配置文件中的 JSON 无效: {path}") from e # Bad: 宽泛的 except 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 ```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 raise ValueError(f"解析数据失败: {data}") from e ``` ### 自定义异常层次结构 ```python class AppError(Exception): """所有应用错误的基类。""" pass class ValidationError(AppError): """当输入验证失败时抛出。""" pass class NotFoundError(AppError): """当请求的资源未找到时抛出。""" pass # 使用示例 def get_user(user_id: str) -> User: user = db.find_user(user_id) if not user: raise NotFoundError(f"未找到用户: {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: 手动管理资源 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): """用于测量代码块执行时间的上下文管理器。""" start = time.perf_counter() yield elapsed = time.perf_counter() - start print(f"{name} 耗时 {elapsed:.4f} 秒") # 使用示例 with timer("数据处理"): 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) ``` ## 推导式与生成器 ### 列表推导式 ```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) # 复杂的推导式应当拆分展开 # Bad: 过于复杂 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 ```python # Good: 用于惰性求值的生成器 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 ```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）与具名元组（Named Tuples） ### 数据类 ```python from dataclasses import dataclass, field from datetime import datetime @dataclass class User: """带有自动生成的 __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): # 验证邮箱格式 if "@" not in self.email: raise ValueError(f"无效邮箱: {self.email}") # 验证年龄范围 if self.age < 0 or self.age > 150: raise ValueError(f"无效年龄: {self.age}") ``` ### 具名元组 ```python from typing import NamedTuple class Point(NamedTuple): """不可变的 2D 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: """测量函数执行时间的装饰器。""" @functools.wraps(func) def wrapper(*args, **kwargs): start = time.perf_counter() result = func(*args, **kwargs) elapsed = time.perf_counter() - start print(f"{func.__name__} 耗时 {elapsed:.4f}s") return result return wrapper @timer def slow_function(): time.sleep(1) # slow_function() 会打印: slow_function took 1.0012s ``` ### 带参数的装饰器 ```python def repeat(times: int): """将函数重复执行多次的装饰器。""" 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__} 已被调用 {self.count} 次") 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 ### 用于 I/O 密集型任务的线程 ```python import concurrent.futures import threading def fetch_url(url: str) -> str: """抓取 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]: """使用线程并发抓取多个 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"错误: {e}" return results ``` ### 用于 CPU 密集型任务的多进程 ```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]: """使用多进程处理多个数据集。""" with concurrent.futures.ProcessPoolExecutor() as executor: results = list(executor.map(process_data, datasets)) return results ``` ### 用于并发 I/O 的 Async/Await ```python import asyncio async def fetch_async(url: str) -> str: """异步抓取 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]: """并发抓取多个 URL。""" tasks = [fetch_async(url) for url in urls] results = await asyncio.gather(*tasks, return_exceptions=True) return dict(zip(urls, results)) ``` ## 包结构 ### 标准项目布局 ```