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752 lines
17 KiB
Markdown
752 lines
17 KiB
Markdown
---
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name: python-patterns
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description: Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.
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---
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# Python 开发模式 (Python Development Patterns)
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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。
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## 激活时机 (When to Activate)
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- 编写新的 Python 代码时
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- 进行 Python 代码审查（Review）时
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- 重构现有的 Python 代码时
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- 设计 Python 包（Package）或模块（Module）时
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## 核心原则
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### 1. 可读性至上 (Readability Counts)
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Python 优先考虑可读性。代码应当直观且易于理解。
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```python
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# Good: 清晰且易读
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def get_active_users(users: list[User]) -> list[User]:
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"""仅从提供的列表中返回活跃用户。"""
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return [user for user in users if user.is_active]
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# Bad: 巧妙但令人困惑
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def get_active_users(u):
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return [x for x in u if x.a]
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```
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### 2. 显式优于隐式 (Explicit is Better Than Implicit)
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避免“魔法”行为；清晰地表达代码的功能。
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```python
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# Good: 显式配置
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import logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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# Bad: 隐藏的副作用
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import some_module
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some_module.setup() # 这到底做了什么？
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```
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### 3. EAFP 模式 - 请求宽恕比请求许可更容易 (Easier to Ask Forgiveness Than Permission)
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Python 倾向于使用异常处理而非预先检查条件。
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```python
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# Good: EAFP 风格
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def get_value(dictionary: dict, key: str) -> Any:
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try:
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return dictionary[key]
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except KeyError:
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return default_value
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# Bad: LBYL (Look Before You Leap，三思而后行) 风格
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def get_value(dictionary: dict, key: str) -> Any:
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if key in dictionary:
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return dictionary[key]
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else:
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return default_value
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```
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## 类型提示 (Type Hints)
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### 基础类型注解
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```python
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from typing import Optional, List, Dict, Any
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def process_user(
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user_id: str,
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data: Dict[str, Any],
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active: bool = True
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) -> Optional[User]:
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"""处理用户并返回更新后的 User 或 None。"""
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if not active:
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return None
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return User(user_id, data)
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```
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### 现代类型提示 (Python 3.9+)
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```python
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# Python 3.9+ - 使用内置类型
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def process_items(items: list[str]) -> dict[str, int]:
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return {item: len(item) for item in items}
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# Python 3.8 及更早版本 - 使用 typing 模块
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from typing import List, Dict
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def process_items(items: List[str]) -> Dict[str, int]:
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return {item: len(item) for item in items}
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```
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### 类型别名 (Type Aliases) 和 TypeVar
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```python
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from typing import TypeVar, Union
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# 复杂类型的类型别名
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JSON = Union[dict[str, Any], list[Any], str, int, float, bool, None]
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def parse_json(data: str) -> JSON:
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return json.loads(data)
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# 泛型
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T = TypeVar('T')
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def first(items: list[T]) -> T | None:
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"""返回第一项，如果列表为空则返回 None。"""
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return items[0] if items else None
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```
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### 基于协议 (Protocol) 的鸭子类型 (Duck Typing)
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```python
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from typing import Protocol
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class Renderable(Protocol):
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def render(self) -> str:
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"""将对象渲染为字符串。"""
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def render_all(items: list[Renderable]) -> str:
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"""渲染所有实现了 Renderable 协议的项目。"""
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return "\n".join(item.render() for item in items)
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```
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## 异常处理模式 (Error Handling Patterns)
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### 特定异常处理
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```python
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# Good: 捕获特定的异常
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def load_config(path: str) -> Config:
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try:
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with open(path) as f:
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return Config.from_json(f.read())
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except FileNotFoundError as e:
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raise ConfigError(f"Config file not found: {path}") from e
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except json.JSONDecodeError as e:
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raise ConfigError(f"Invalid JSON in config: {path}") from e
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# Bad: 空异常捕获
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def load_config(path: str) -> Config:
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try:
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with open(path) as f:
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return Config.from_json(f.read())
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except:
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return None # 静默失败！
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```
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### 异常链 (Exception Chaining)
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```python
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def process_data(data: str) -> Result:
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try:
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parsed = json.loads(data)
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except json.JSONDecodeError as e:
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# 使用异常链以保留堆栈跟踪 (traceback)
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raise ValueError(f"Failed to parse data: {data}") from e
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```
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### 自定义异常层次结构
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```python
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class AppError(Exception):
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"""所有应用程序错误的基类。"""
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pass
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class ValidationError(AppError):
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"""当输入验证失败时引发。"""
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pass
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class NotFoundError(AppError):
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"""当请求的资源未找到时引发。"""
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pass
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# 使用示例
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def get_user(user_id: str) -> User:
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user = db.find_user(user_id)
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if not user:
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raise NotFoundError(f"User not found: {user_id}")
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return user
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```
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## 上下文管理器 (Context Managers)
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### 资源管理
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```python
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# Good: 使用上下文管理器
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def process_file(path: str) -> str:
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with open(path, 'r') as f:
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return f.read()
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# Bad: 手动资源管理
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def process_file(path: str) -> str:
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f = open(path, 'r')
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try:
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return f.read()
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finally:
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f.close()
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```
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### 自定义上下文管理器
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```python
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from contextlib import contextmanager
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@contextmanager
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def timer(name: str):
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"""用于对代码块计时的上下文管理器。"""
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start = time.perf_counter()
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yield
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elapsed = time.perf_counter() - start
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print(f"{name} took {elapsed:.4f} seconds")
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# 使用示例
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with timer("data processing"):
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process_large_dataset()
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```
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### 上下文管理器类
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```python
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class DatabaseTransaction:
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def __init__(self, connection):
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self.connection = connection
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def __enter__(self):
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self.connection.begin_transaction()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if exc_type is None:
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self.connection.commit()
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else:
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self.connection.rollback()
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return False # 不要抑制异常
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# 使用示例
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with DatabaseTransaction(conn):
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user = conn.create_user(user_data)
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conn.create_profile(user.id, profile_data)
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```
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## 推导式 (Comprehensions) 与生成器 (Generators)
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### 列表推导式 (List Comprehensions)
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```python
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# Good: 用于简单转换的列表推导式
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names = [user.name for user in users if user.is_active]
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# Bad: 手动循环
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names = []
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for user in users:
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if user.is_active:
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names.append(user.name)
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# 复杂的推导式应当展开
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# Bad: 太过复杂
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result = [x * 2 for x in items if x > 0 if x % 2 == 0]
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# Good: 使用生成器函数
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def filter_and_transform(items: Iterable[int]) -> list[int]:
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result = []
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for x in items:
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if x > 0 and x % 2 == 0:
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result.append(x * 2)
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return result
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```
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### 生成器表达式 (Generator Expressions)
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```python
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# Good: 用于延迟求值的生成器
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total = sum(x * x for x in range(1_000_000))
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# Bad: 创建了巨大的中间列表
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total = sum([x * x for x in range(1_000_000)])
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```
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### 生成器函数 (Generator Functions)
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```python
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def read_large_file(path: str) -> Iterator[str]:
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"""逐行读取大文件。"""
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with open(path) as f:
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for line in f:
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yield line.strip()
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# 使用示例
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for line in read_large_file("huge.txt"):
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process(line)
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```
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## 数据类 (Data Classes) 与命名元组 (Named Tuples)
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### 数据类 (Data Classes)
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```python
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from dataclasses import dataclass, field
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from datetime import datetime
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@dataclass
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class User:
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"""具有自动生成 __init__、__repr__ 和 __eq__ 的用户实体。"""
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id: str
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name: str
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email: str
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created_at: datetime = field(default_factory=datetime.now)
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is_active: bool = True
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# 使用示例
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user = User(
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id="123",
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name="Alice",
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email="alice@example.com"
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)
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```
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### 带验证的数据类
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```python
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@dataclass
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class User:
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email: str
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age: int
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def __post_init__(self):
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# 验证电子邮件格式
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if "@" not in self.email:
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raise ValueError(f"Invalid email: {self.email}")
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# 验证年龄范围
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if self.age < 0 or self.age > 150:
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raise ValueError(f"Invalid age: {self.age}")
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```
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### 命名元组 (Named Tuples)
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```python
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from typing import NamedTuple
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class Point(NamedTuple):
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"""不可变的二维点。"""
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x: float
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y: float
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def distance(self, other: 'Point') -> float:
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return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5
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# 使用示例
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p1 = Point(0, 0)
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p2 = Point(3, 4)
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print(p1.distance(p2)) # 5.0
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```
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## 装饰器 (Decorators)
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### 函数装饰器
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```python
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import functools
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import time
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def timer(func: Callable) -> Callable:
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"""用于对函数执行进行计时的装饰器。"""
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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start = time.perf_counter()
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result = func(*args, **kwargs)
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elapsed = time.perf_counter() - start
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print(f"{func.__name__} took {elapsed:.4f}s")
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return result
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return wrapper
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@timer
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def slow_function():
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time.sleep(1)
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# slow_function() 输出: slow_function took 1.0012s
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```
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### 参数化装饰器
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```python
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def repeat(times: int):
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"""用于多次重复执行函数的装饰器。"""
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def decorator(func: Callable) -> Callable:
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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results = []
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for _ in range(times):
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results.append(func(*args, **kwargs))
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return results
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return wrapper
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return decorator
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@repeat(times=3)
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def greet(name: str) -> str:
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return f"Hello, {name}!"
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# greet("Alice") 返回 ["Hello, Alice!", "Hello, Alice!", "Hello, Alice!"]
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```
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### 基于类的装饰器
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```python
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class CountCalls:
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"""统计函数被调用次数的装饰器。"""
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def __init__(self, func: Callable):
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functools.update_wrapper(self, func)
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self.func = func
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self.count = 0
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def __call__(self, *args, **kwargs):
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self.count += 1
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print(f"{self.func.__name__} has been called {self.count} times")
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return self.func(*args, **kwargs)
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@CountCalls
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def process():
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pass
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# 每次调用 process() 都会打印调用计数
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```
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## 并发模式 (Concurrency Patterns)
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### 线程 (Threading) 处理 I/O 密集型任务
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```python
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import concurrent.futures
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import threading
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def fetch_url(url: str) -> str:
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"""获取 URL (I/O 密集型操作)。"""
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import urllib.request
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with urllib.request.urlopen(url) as response:
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return response.read().decode()
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def fetch_all_urls(urls: list[str]) -> dict[str, str]:
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"""使用线程并发地获取多个 URL。"""
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with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
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future_to_url = {executor.submit(fetch_url, url): url for url in urls}
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results = {}
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for future in concurrent.futures.as_completed(future_to_url):
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url = future_to_url[future]
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try:
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results[url] = future.result()
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except Exception as e:
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results[url] = f"Error: {e}"
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return results
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```
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### 多进程 (Multiprocessing) 处理 CPU 密集型任务
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```python
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def process_data(data: list[int]) -> int:
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"""CPU 密集型计算。"""
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return sum(x ** 2 for x in data)
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def process_all(datasets: list[list[int]]) -> list[int]:
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"""使用多个进程处理多个数据集。"""
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with concurrent.futures.ProcessPoolExecutor() as executor:
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results = list(executor.map(process_data, datasets))
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return results
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```
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### Async/Await 处理并发 I/O
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```python
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import asyncio
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async def fetch_async(url: str) -> str:
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"""异步获取 URL。"""
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import aiohttp
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as response:
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return await response.text()
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async def fetch_all(urls: list[str]) -> dict[str, str]:
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"""并发地获取多个 URL。"""
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tasks = [fetch_async(url) for url in urls]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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return dict(zip(urls, results))
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```
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## 包组织 (Package Organization)
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### 标准项目布局
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```
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myproject/
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├── src/
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│ └── mypackage/
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│ ├── __init__.py
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│ ├── main.py
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│ ├── api/
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│ │ ├── __init__.py
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│ │ └── routes.py
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│ ├── models/
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│ │ ├── __init__.py
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│ │ └── user.py
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│ └── utils/
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│ ├── __init__.py
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│ └── helpers.py
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├── tests/
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│ ├── __init__.py
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│ ├── conftest.py
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│ ├── test_api.py
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│ └── test_models.py
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├── pyproject.toml
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├── README.md
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└── .gitignore
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```
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### 导入规范
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```python
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# Good: 导入顺序 - 标准库、第三方库、本地库
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import os
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import sys
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from pathlib import Path
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import requests
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from fastapi import FastAPI
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from mypackage.models import User
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from mypackage.utils import format_name
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# Good: 使用 isort 自动进行导入排序
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# pip install isort
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```
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### 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。
|
||
|
||
```
|