python-patterns

name python-patterns 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。 origin ECC Python 开发模式 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。 何时激活 编写新的 Python 代码 审查 Python 代码 重构现有的 Python 代码 设计 Python 包/模块 核心原则 1. 可读性很重要 Python 优先考虑可读性。代码应该清晰且易于理解。 # Good: Clear and readable def get_active_users ( users: list [User] ) -> list [User]: """Return only active users from the provided list.""" return [user for user in users if user.is_active] # Bad: Clever but confusing def get_active_users ( u ): return [x for x in u if x.a] 2. 显式优于隐式 避免魔法；清晰说明你的代码在做什么。 # Good: Explicit configuration import logging logging.basicConfig( level=logging.INFO, format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) # Bad: Hidden side effects import some_module some_module.setup() # What does this do? 3. EAFP - 请求宽恕比请求许可更容易 Python 倾向于使用异常处理而非检查条件。 # Good: EAFP style def get_value ( dictionary: dict , key: str , default_value: Any = None ) -> Any : try : return dictionary[key] except KeyError: return default_value # Bad: LBYL (Look Before You Leap) style def get_value ( dictionary: dict , key: str , default_value: Any = None ) -> Any : if key in dictionary: return dictionary[key] else : return default_value 类型提示 基本类型注解 from typing import Optional , List , Dict , Any def process_user ( user_id: str , data: Dict [ str , Any ], active: bool = True ) -> Optional [User]: """Process a user and return the updated User or None.""" if not active: return None return User(user_id, data) 现代类型提示（Python 3.9+） # Python 3.9+ - Use built-in types def process_items ( items: list [ str ] ) -> dict [ str , int ]: return {item: len (item) for item in items} # Python 3.8 and earlier - Use typing module from typing import List , Dict def process_items ( items: List [ str ] ) -> Dict [ str , int ]: return {item: len (item) for item in items} 类型别名和 TypeVar from typing import TypeVar, Union # Type alias for complex types JSON = Union [ dict [ str , Any ], list [ Any ], str , int , float , bool , None ] def parse_json ( data: str ) -> JSON: return json.loads(data) # Generic types T = TypeVar( 'T' ) def first ( items: list [T] ) -> T | None : """Return the first item or None if list is empty.""" return items[ 0 ] if items else None 基于协议的鸭子类型 from typing import Protocol class Renderable ( Protocol ): def render ( self ) -> str : """Render the object to a string.""" def render_all ( items: list [Renderable] ) -> str : """Render all items that implement the Renderable protocol.""" return "\n" .join(item.render() for item in items) 错误处理模式 特定异常处理 # Good: Catch specific exceptions 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: Bare except def load_config ( path: str ) -> Config: try : with open (path) as f: return Config.from_json(f.read()) except : return None # Silent failure! 异常链 def process_data ( data: str ) -> Result: try : parsed = json.loads(data) except json.JSONDecodeError as e: # Chain exceptions to preserve the traceback raise ValueError( f"Failed to parse data: {data} " ) from e 自定义异常层次结构 class AppError ( Exception ): """Base exception for all application errors.""" pass class ValidationError ( AppError ): """Raised when input validation fails.""" pass class NotFoundError ( AppError ): """Raised when a requested resource is not found.""" pass # Usage 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 资源管理 # Good: Using context managers def process_file ( path: str ) -> str : with open (path, 'r' ) as f: return f.read() # Bad: Manual resource management 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 from contextlib import contextmanager @contextmanager def timer ( name: str ): """Context manager to time a block of code.""" start = time.perf_counter() yield elapsed = time.perf_counter() - start print ( f" {name} took {elapsed: .4 f} seconds" ) # Usage with timer( "data processing" ): process_large_dataset() 上下文管理器类 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 # Don't suppress exceptions # Usage with DatabaseTransaction(conn): user = conn.create_user(user_data) conn.create_profile(user. id , profile_data) 推导式和生成器 列表推导式 # Good: List comprehension for simple transformations names = [user.name for user in users if user.is_active] # Bad: Manual loop names = [] for user in users: if user.is_active: names.append(user.name) # Complex comprehensions should be expanded # Bad: Too complex result = [x * 2 for x in items if x > 0 if x % 2 == 0 ] # Good: Use a generator function 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 # Good: Generator for lazy evaluation total = sum (x * x for x in range ( 1_000_000 )) # Bad: Creates large intermediate list total = sum ([x * x for x in range ( 1_000_000 )]) 生成器函数 def read_large_file ( path: str ) -> Iterator[ str ]: """Read a large file line by line.""" with open (path) as f: for line in f: yield line.strip() # Usage for line in read_large_file( "huge.txt" ): process(line) 数据类和命名元组 数据类 from dataclasses import dataclass, field from datetime import datetime @dataclass class User : """User entity with automatic __init__, __repr__, and __eq__.""" id : str name: str email: str created_at: datetime = field(default_factory=datetime.now) is_active: bool = True # Usage user = User( id = "123" , name= "Alice" , email= "alice@example.com" ) 带验证的数据类 @dataclass class User : email: str age: int def __post_init__ ( self ): # Validate email format if "@" not in self .email: raise ValueError( f"Invalid email: {self.email} " ) # Validate age range if self .age < 0 or self .age > 150 : raise ValueError( f"Invalid age: {self.age} " ) 命名元组 from typing import NamedTuple class Point ( NamedTuple ): """Immutable 2D point.""" x: float y: float def distance ( self, other: 'Point' ) -> float : return (( self .x - other.x) ** 2 + ( self .y - other.y) ** 2 ) ** 0.5 # Usage p1 = Point( 0 , 0 ) p2 = Point( 3 , 4 ) print (p1.distance(p2)) # 5.0 装饰器 函数装饰器 import functools import time def timer ( func: Callable ) -> Callable : """Decorator to time function execution.""" @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: .4 f} s" ) return result return wrapper @timer def slow_function (): time.sleep( 1 ) # slow_function() prints: slow_function took 1.0012s 参数化装饰器 def repeat ( times: int ): """Decorator to repeat a function multiple times.""" 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") returns ["Hello, Alice!", "Hello, Alice!", "Hello, Alice!"] 基于类的装饰器 class CountCalls : """Decorator that counts how many times a function is called.""" 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 # Each call to process() prints the call count 并发模式 用于 I/O 密集型任务的线程 import concurrent.futures import threading def fetch_url ( url: str ) -> str : """Fetch a URL (I/O-bound operation).""" import urllib.request with urllib.request.urlopen(url) as response: return response.read().decode() def fetch_all_urls ( urls: list [ str ] ) -> dict [ str , str ]: """Fetch multiple URLs concurrently using threads.""" 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 用于 CPU 密集型任务的多进程 def process_data ( data: list [ int ] ) -> int : """CPU-intensive computation.""" return sum (x ** 2 for x in data) def process_all ( datasets: list [ list [ int ]] ) -> list [ int ]: """Process multiple datasets using multiple processes.""" with concurrent.futures.ProcessPoolExecutor() as executor: results = list (executor. map (process_data, datasets)) return results 用于并发 I/O 的异步/等待 import asyncio async def fetch_async ( url: str ) -> str : """Fetch a URL asynchronously.""" 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 ]: """Fetch multiple URLs concurrently."""