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目录
匿名函数#xff08;Lambda#xff09;装饰器的使用生成器与迭代器递归函数应用实战案例#xff1a;文件批处理工具
1. 匿名函数#xff08;Lambda#xff09;深入解析 #x1f3af;
1.1 Lambda函数基础与进阶
1.1.1 基本…Python高级函数特性详解
目录
匿名函数Lambda装饰器的使用生成器与迭代器递归函数应用实战案例文件批处理工具
1. 匿名函数Lambda深入解析
1.1 Lambda函数基础与进阶
1.1.1 基本语法与类型注解
from typing import Callable, List, Any, Dict, Optional, Union# 基础lambda函数带类型注解
square: Callable[[int], int] lambda x: x ** 2
greeting: Callable[[str, Optional[str]], str] lambda name, msg你好: f{msg}, {name}!# 带类型注解的复杂lambda函数
process_data: Callable[[Dict[str, Any]], List[Any]] lambda d: [v for k, v in d.items() if isinstance(v, (int, float))]1.1.2 函数式编程应用
# 1. 组合多个lambda函数
compose lambda f, g: lambda x: f(g(x))
double lambda x: x * 2
increment lambda x: x 1# 先加1再乘2
double_after_increment compose(double, increment)
print(double_after_increment(3)) # 输出: 8# 2. 柯里化示例
curry lambda f: lambda x: lambda y: f(x, y)
add lambda x, y: x y
curried_add curry(add)
increment curried_add(1)
print(increment(5)) # 输出: 6# 3. 偏函数应用
from functools import partial
multiply lambda x, y: x * y
double partial(multiply, 2)
print(double(4)) # 输出: 81.2 Lambda函数实战应用
1.2.1 数据处理与转换
# 1. 复杂数据结构处理
users [{name: 张三, age: 25, salary: 8000, department: 技术},{name: 李四, age: 30, salary: 12000, department: 销售},{name: 王五, age: 28, salary: 15000, department: 技术},{name: 赵六, age: 35, salary: 20000, department: 管理}
]# 按多个条件排序先按部门再按薪资降序
sorted_users sorted(users,keylambda u: (u[department], -u[salary])
)# 数据转换和筛选
tech_salaries list(map(lambda u: u[salary],filter(lambda u: u[department] 技术, users)
))# 2. 数据聚合
from collections import defaultdict# 按部门统计平均薪资
dept_avg_salary defaultdict(list)
for user in users:dept_avg_salary[user[department]].append(user[salary])dept_averages {dept: sum(salaries) / len(salaries)for dept, salaries in dept_avg_salary.items()
}1.2.2 事件处理与回调
class EventSystem:def __init__(self):self.handlers: Dict[str, List[Callable]] defaultdict(list)def register(self, event_name: str, handler: Callable) - None:self.handlers[event_name].append(handler)def trigger(self, event_name: str, *args, **kwargs) - None:for handler in self.handlers[event_name]:handler(*args, **kwargs)# 使用lambda作为事件处理器
event_system EventSystem()# 注册多个简单的事件处理器
event_system.register(user_login, lambda user: print(f用户 {user} 登录))
event_system.register(user_login, lambda user: print(f发送欢迎邮件给 {user}))# 注册带条件判断的处理器
event_system.register(payment,lambda amount, user: print(f大额支付警告: {user} 支付了 {amount}元)if amount 10000 else None
)1.3 Lambda函数最佳实践与性能考虑
1.3.1 性能优化技巧
from timeit import timeit
import operator# 1. 使用operator模块替代简单lambda
numbers list(range(1000))# 不推荐
lambda_time timeit(lambda: list(map(lambda x: x 1, numbers)), number1000)# 推荐
operator_time timeit(lambda: list(map(operator.add, numbers, [1]*len(numbers))), number1000)# 2. 缓存计算结果
from functools import lru_cache# 使用缓存装饰器包装lambda
cached_computation lru_cache(maxsize128)(lambda x: sum(i * i for i in range(x)))1.3.2 代码可维护性建议
# ✅ 适合使用Lambda的场景
# 1. 简单的键函数
sorted_items sorted(items, keylambda x: x.priority)# 2. 简单的数据转换
transformed map(lambda x: x.upper(), items)# 3. 简单的过滤条件
filtered filter(lambda x: x 0, numbers)# ❌ 不建议使用Lambda的场景
# 1. 复杂的业务逻辑
# 不推荐
process lambda data: {k: [i * 2 for i in v if i 0]for k, v in data.items()if isinstance(v, list)
}# ✅ 推荐使用常规函数
def process_data(data: Dict[str, List[int]]) - Dict[str, List[int]]:处理数据集合Args:data: 输入数据字典Returns:处理后的数据字典result {}for key, values in data.items():if isinstance(values, list):result[key] [i * 2 for i in values if i 0]return result1.4 实战示例数据分析管道
from typing import List, Dict, Any
from datetime import datetimeclass DataPipeline:数据处理管道def __init__(self):self.transforms: List[Callable] []def add_transform(self, transform: Callable) - DataPipeline:添加转换步骤self.transforms.append(transform)return selfdef process(self, data: List[Dict[str, Any]]) - List[Dict[str, Any]]:执行所有转换result datafor transform in self.transforms:result transform(result)return result# 使用示例
# 1. 创建转换函数
filter_active lambda data: [item for item in dataif item.get(status) active
]calculate_metrics lambda data: [{**item, efficiency: item[output] / item[input] if item[input] else 0}for item in data
]add_timestamp lambda data: [{**item, processed_at: datetime.now().isoformat()}for item in data
]# 2. 构建和使用管道
pipeline DataPipeline()
pipeline.add_transform(filter_active)\.add_transform(calculate_metrics)\.add_transform(add_timestamp)# 3. 处理数据
sample_data [{id: 1, status: active, input: 100, output: 85},{id: 2, status: inactive, input: 90, output: 70},{id: 3, status: active, input: 95, output: 80}
]processed_data pipeline.process(sample_data)2. 装饰器的使用
2.1 基本装饰器
from functools import wraps
import time
from typing import Callable, TypeVar, AnyT TypeVar(T, boundCallable[..., Any])def timing_decorator(func: T) - T:测量函数执行时间的装饰器wraps(func)def wrapper(*args, **kwargs):start_time time.time()result func(*args, **kwargs)end_time time.time()print(f函数 {func.__name__} 执行时间: {end_time - start_time:.4f} 秒)return resultreturn wrappertiming_decorator
def slow_function():time.sleep(1)return 完成2.2 带参数的装饰器
def retry(max_attempts: int 3, delay: float 1.0):创建一个重试装饰器Args:max_attempts: 最大重试次数delay: 重试间隔秒def decorator(func: Callable) - Callable:wraps(func)def wrapper(*args, **kwargs):attempts 0while attempts max_attempts:try:return func(*args, **kwargs)except Exception as e:attempts 1if attempts max_attempts:raise etime.sleep(delay)return Nonereturn wrapperreturn decoratorretry(max_attempts3, delay2.0)
def unstable_network_call():模拟不稳定的网络调用if random.random() 0.7: # 70%的失败率raise ConnectionError(网络连接失败)return 成功2.3 多个装饰器的组合
def log_decorator(func: Callable) - Callable:wraps(func)def wrapper(*args, **kwargs):print(f调用函数: {func.__name__})return func(*args, **kwargs)return wrapperdef validate_inputs(func: Callable) - Callable:wraps(func)def wrapper(*args, **kwargs):if not args and not kwargs:raise ValueError(函数参数不能为空)return func(*args, **kwargs)return wrapperlog_decorator
validate_inputs
timing_decorator
def process_data(data: List[Any]) - List[Any]:处理数据的函数return [item for item in data if item is not None]3. 生成器与迭代器
3.1 生成器函数
from typing import Generator, Iterator, Listdef fibonacci_generator(n: int) - Generator[int, None, None]:生成斐波那契数列的生成器Args:n: 要生成的数字个数Yields:斐波那契数列中的下一个数字a, b 0, 1for _ in range(n):yield aa, b b, a b# 内存效率对比
def get_numbers_list(n: int) - List[int]:返回列表的方式return [i ** 2 for i in range(n)]def get_numbers_generator(n: int) - Generator[int, None, None]:生成器方式for i in range(n):yield i ** 23.2 生成器表达式
# 列表推导式 vs 生成器表达式
numbers [1, 2, 3, 4, 5]# 列表推导式立即计算
squares_list [x ** 2 for x in numbers] # 创建新列表# 生成器表达式惰性计算
squares_gen (x ** 2 for x in numbers) # 创建生成器对象# 条件筛选
even_squares (x ** 2 for x in numbers if x % 2 0)# 多重生成器
matrix ((i, j) for i in range(3) for j in range(3))3.3 自定义迭代器
class DataIterator:自定义数据迭代器def __init__(self, data: List[Any]):self.data dataself.index 0def __iter__(self) - DataIterator:return selfdef __next__(self) - Any:if self.index len(self.data):raise StopIterationvalue self.data[self.index]self.index 1return value# 使用示例
class DataContainer:def __init__(self, data: List[Any]):self.data datadef __iter__(self) - DataIterator:return DataIterator(self.data)4. 递归函数应用
4.1 基本递归模式
from typing import Any, List, Optionaldef factorial(n: int) - int:计算阶乘的递归实现if n 1: # 基本情况return 1return n * factorial(n - 1) # 递归情况def binary_search(arr: List[int], target: int, left: int 0, right: Optional[int] None) - int:递归实现二分查找Returns:目标值的索引如果未找到返回-1if right is None:right len(arr) - 1if left right:return -1mid (left right) // 2if arr[mid] target:return midelif arr[mid] target:return binary_search(arr, target, left, mid - 1)else:return binary_search(arr, target, mid 1, right)4.2 高级递归应用
class TreeNode:def __init__(self, value: Any):self.value valueself.left: Optional[TreeNode] Noneself.right: Optional[TreeNode] Nonedef tree_traversal(root: Optional[TreeNode], method: str inorder) - Generator[Any, None, None]:通用的树遍历生成器Args:root: 树的根节点method: 遍历方法 (preorder, inorder, postorder)if not root:returnif method preorder:yield root.valueyield from tree_traversal(root.left, method)yield from tree_traversal(root.right, method)elif method inorder:yield from tree_traversal(root.left, method)yield root.valueyield from tree_traversal(root.right, method)elif method postorder:yield from tree_traversal(root.left, method)yield from tree_traversal(root.right, method)yield root.value5. 实战案例文件批处理工具 ️
5.1 文件处理工具类
from pathlib import Path
from typing import Generator, List, Dict, Callable
import os
import shutilclass FileBatchProcessor:文件批处理工具def __init__(self, source_dir: str):self.source_dir Path(source_dir)self.processors: Dict[str, Callable] {}def register_processor(self, extension: str, processor: Callable) - None:注册文件处理器self.processors[extension] processordef find_files(self, pattern: str *) - Generator[Path, None, None]:递归查找文件for item in self.source_dir.rglob(pattern):if item.is_file():yield itemdef process_files(self) - Dict[str, List[str]]:处理所有文件results {success: [], error: []}for file_path in self.find_files():try:extension file_path.suffix.lower()if extension in self.processors:self.processors[extension](file_path)results[success].append(str(file_path))except Exception as e:results[error].append(f{file_path}: {str(e)})return results5.2 实际应用示例
# 文件处理器函数
def process_text_file(file_path: Path) - None:处理文本文件with file_path.open(r, encodingutf-8) as f:content f.read()# 处理文本内容processed_content content.upper()# 保存处理后的文件backup_path file_path.with_suffix(file_path.suffix .bak)shutil.copy2(file_path, backup_path)with file_path.open(w, encodingutf-8) as f:f.write(processed_content)def process_image_file(file_path: Path) - None:处理图片文件示例# 这里可以添加图片处理逻辑pass# 使用示例
def main():# 创建处理器实例processor FileBatchProcessor(./documents)# 注册文件处理器processor.register_processor(.txt, process_text_file)processor.register_processor(.md, process_text_file)processor.register_processor(.jpg, process_image_file)processor.register_processor(.png, process_image_file)# 执行批处理results processor.process_files()# 输出处理结果print(f成功处理的文件: {len(results[success])})print(f处理失败的文件: {len(results[error])})if results[error]:print(\n错误详情:)for error in results[error]:print(f- {error})if __name__ __main__:main()本文详细介绍了Python的高级函数特性从匿名函数到装饰器从生成器到递归函数最后通过一个实战案例展示了这些特性的实际应用。
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