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* docs(zh-CN): sync Chinese docs with latest upstream changes * update --------- Co-authored-by: neo <neo.dowithless@gmail.com>
751 lines
16 KiB
Markdown
751 lines
16 KiB
Markdown
---
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name: python-patterns
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description: Pythonic 惯用法、PEP 8 标准、类型提示以及构建稳健、高效且可维护的 Python 应用程序的最佳实践。
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origin: ECC
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---
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# Python 开发模式
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用于构建健壮、高效和可维护应用程序的惯用 Python 模式与最佳实践。
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## 何时激活
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* 编写新的 Python 代码
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* 审查 Python 代码
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* 重构现有的 Python 代码
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* 设计 Python 包/模块
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## 核心原则
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### 1. 可读性很重要
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Python 优先考虑可读性。代码应该清晰且易于理解。
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```python
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# Good: Clear and readable
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def get_active_users(users: list[User]) -> list[User]:
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"""Return only active users from the provided list."""
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return [user for user in users if user.is_active]
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# Bad: Clever but confusing
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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
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避免魔法；清晰说明你的代码在做什么。
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```python
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# Good: Explicit configuration
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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: Hidden side effects
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import some_module
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some_module.setup() # What does this do?
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```
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### 3. EAFP - 请求宽恕比请求许可更容易
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Python 倾向于使用异常处理而非检查条件。
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```python
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# Good: EAFP style
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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) style
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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
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### 基本类型注解
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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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"""Process a user and return the updated User or 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+ - Use built-in types
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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 and earlier - Use typing module
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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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### 类型别名和 TypeVar
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```python
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from typing import TypeVar, Union
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# Type alias for complex types
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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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# Generic types
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T = TypeVar('T')
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def first(items: list[T]) -> T | None:
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"""Return the first item or None if list is empty."""
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return items[0] if items else None
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```
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### 基于协议的鸭子类型
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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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"""Render the object to a string."""
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def render_all(items: list[Renderable]) -> str:
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"""Render all items that implement the Renderable protocol."""
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return "\n".join(item.render() for item in items)
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```
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## 错误处理模式
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### 特定异常处理
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```python
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# Good: Catch specific exceptions
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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: Bare except
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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 # Silent failure!
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```
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### 异常链
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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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# Chain exceptions to preserve the 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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"""Base exception for all application errors."""
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pass
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class ValidationError(AppError):
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"""Raised when input validation fails."""
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pass
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class NotFoundError(AppError):
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"""Raised when a requested resource is not found."""
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pass
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# Usage
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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
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### 资源管理
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```python
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# Good: Using context managers
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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: Manual resource management
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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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"""Context manager to time a block of code."""
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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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# Usage
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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 # Don't suppress exceptions
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# Usage
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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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## 推导式和生成器
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### 列表推导式
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```python
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# Good: List comprehension for simple transformations
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names = [user.name for user in users if user.is_active]
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# Bad: Manual loop
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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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# Complex comprehensions should be expanded
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# Bad: Too complex
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result = [x * 2 for x in items if x > 0 if x % 2 == 0]
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# Good: Use a generator function
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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
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```python
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# Good: Generator for lazy evaluation
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total = sum(x * x for x in range(1_000_000))
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# Bad: Creates large intermediate list
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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
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```python
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def read_large_file(path: str) -> Iterator[str]:
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"""Read a large file line by line."""
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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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# Usage
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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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## 数据类和命名元组
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### 数据类
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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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"""User entity with automatic __init__, __repr__, and __eq__."""
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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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# Usage
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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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# Validate email format
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if "@" not in self.email:
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raise ValueError(f"Invalid email: {self.email}")
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# Validate age range
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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
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```python
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from typing import NamedTuple
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class Point(NamedTuple):
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"""Immutable 2D point."""
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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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# Usage
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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
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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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"""Decorator to time function execution."""
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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() prints: 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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"""Decorator to repeat a function multiple times."""
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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") returns ["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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"""Decorator that counts how many times a function is called."""
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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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# Each call to process() prints the call count
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```
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## 并发模式
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### 用于 I/O 密集型任务的线程
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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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"""Fetch a URL (I/O-bound operation)."""
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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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"""Fetch multiple URLs concurrently using threads."""
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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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### 用于 CPU 密集型任务的多进程
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```python
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def process_data(data: list[int]) -> int:
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"""CPU-intensive computation."""
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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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"""Process multiple datasets using multiple processes."""
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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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### 用于并发 I/O 的异步/等待
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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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"""Fetch a URL asynchronously."""
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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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"""Fetch multiple URLs concurrently."""
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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
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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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### 导入约定
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```python
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# Good: Import order - stdlib, third-party, local
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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: Use isort for automatic import sorting
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# pip install isort
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```
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### **init**.py 用于包导出
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```python
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# mypackage/__init__.py
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"""mypackage - A sample Python package."""
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__version__ = "1.0.0"
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# Export main classes/functions at package level
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from mypackage.models import User, Post
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from mypackage.utils import format_name
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__all__ = ["User", "Post", "format_name"]
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```
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## 内存和性能
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### 使用 **slots** 提高内存效率
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```python
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# Bad: Regular class uses __dict__ (more memory)
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class Point:
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def __init__(self, x: float, y: float):
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self.x = x
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self.y = y
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# Good: __slots__ reduces memory usage
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class Point:
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__slots__ = ['x', 'y']
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def __init__(self, x: float, y: float):
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self.x = x
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self.y = y
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```
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### 生成器用于大数据
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```python
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# Bad: Returns full list in memory
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def read_lines(path: str) -> list[str]:
|
||
with open(path) as f:
|
||
return [line.strip() for line in f]
|
||
|
||
# Good: Yields lines one at a time
|
||
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: O(n²) due to string immutability
|
||
result = ""
|
||
for item in items:
|
||
result += str(item)
|
||
|
||
# Good: O(n) using join
|
||
result = "".join(str(item) for item in items)
|
||
|
||
# Good: Using StringIO for building
|
||
from io import StringIO
|
||
|
||
buffer = StringIO()
|
||
for item in items:
|
||
buffer.write(str(item))
|
||
result = buffer.getvalue()
|
||
```
|
||
|
||
## Python 工具集成
|
||
|
||
### 基本命令
|
||
|
||
```bash
|
||
# Code formatting
|
||
black .
|
||
isort .
|
||
|
||
# Linting
|
||
ruff check .
|
||
pylint mypackage/
|
||
|
||
# Type checking
|
||
mypy .
|
||
|
||
# Testing
|
||
pytest --cov=mypackage --cov-report=html
|
||
|
||
# Security scanning
|
||
bandit -r .
|
||
|
||
# Dependency management
|
||
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
|
||
|
||
| 惯用法 | 描述 |
|
||
|-------|-------------|
|
||
| EAFP | 请求宽恕比请求许可更容易 |
|
||
| 上下文管理器 | 使用 `with` 进行资源管理 |
|
||
| 列表推导式 | 用于简单的转换 |
|
||
| 生成器 | 用于惰性求值和大数据集 |
|
||
| 类型提示 | 注解函数签名 |
|
||
| 数据类 | 用于具有自动生成方法的数据容器 |
|
||
| `__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` | 用于循环中的索引-元素对 |
|
||
|
||
## 要避免的反模式
|
||
|
||
```python
|
||
# Bad: Mutable default arguments
|
||
def append_to(item, items=[]):
|
||
items.append(item)
|
||
return items
|
||
|
||
# Good: Use None and create new list
|
||
def append_to(item, items=None):
|
||
if items is None:
|
||
items = []
|
||
items.append(item)
|
||
return items
|
||
|
||
# Bad: Checking type with type()
|
||
if type(obj) == list:
|
||
process(obj)
|
||
|
||
# Good: Use isinstance
|
||
if isinstance(obj, list):
|
||
process(obj)
|
||
|
||
# Bad: Comparing to None with ==
|
||
if value == None:
|
||
process()
|
||
|
||
# Good: Use is
|
||
if value is None:
|
||
process()
|
||
|
||
# Bad: from module import *
|
||
from os.path import *
|
||
|
||
# Good: Explicit imports
|
||
from os.path import join, exists
|
||
|
||
# Bad: Bare except
|
||
try:
|
||
risky_operation()
|
||
except:
|
||
pass
|
||
|
||
# Good: Specific exception
|
||
try:
|
||
risky_operation()
|
||
except SpecificError as e:
|
||
logger.error(f"Operation failed: {e}")
|
||
```
|
||
|
||
**记住**：Python 代码应该具有可读性、显式性，并遵循最小意外原则。如有疑问，优先考虑清晰性而非巧妙性。
|