Python中functools 模块的使用小结

import functools

def log_call(func):
    @functools.wraps(func)  # 保留 func 的 __name__, __doc__ 等
    def wrapper(*args, **kwargs):
        print(f"调用 {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

@log_call
def greet(name: str) -> str:
    """向某人打招呼"""
    return f"你好, {name}"

print(greet.__name__)  # greet（没有 @wraps 会返回 wrapper）
print(greet.__doc__)   # 向某人打招呼
print(greet.__annotations__)  # {'name': <class 'str'>, 'return': <class 'str'>}

import functools
import time

@functools.lru_cache(maxsize=128)
def fib(n: int) -> int:
    """带缓存的斐波那契数列"""
    if n < 2:
        return n
    return fib(n - 1) + fib(n - 2)

start = time.time()
print(fib(100))  # 354224848179261915075
print(f"耗时: {time.time() - start:.6f}s")  # 瞬间完成

# 查看缓存统计
print(fib.cache_info())  # CacheInfo(hits=98, misses=101, maxsize=128, currsize=101)

import functools

def power(base: float, exp: float) -> float:
    return base ** exp

# 创建平方和立方偏函数
square = functools.partial(power, exp=2)
cube = functools.partial(power, exp=3)

print(square(5))   # 25
print(cube(5))     # 125

# 在排序中固定 key 函数
data = [(1, "z"), (3, "a"), (2, "m")]
sorted(data, key=functools.partial(lambda k, i: k[i], i=1))
# 按第二个元素排序: [(3, 'a'), (2, 'm'), (1, 'z')]

import functools

@functools.singledispatch
def serialize(obj):
    """将对象序列化为字符串"""
    raise TypeError(f"不支持的类型: {type(obj)}")

@serialize.register(int)
def _(obj: int) -> str:
    return f"数字: {obj}"

@serialize.register(str)
def _(obj: str) -> str:
    return f"字符串: '{obj}'"

@serialize.register(list)
def _(obj: list) -> str:
    return f"列表: {[serialize(x) for x in obj]}"

@serialize.register(dict)
def _(obj: dict) -> str:
    return f"字典: {{{', '.join(f'{k!r}: {v}' for k, v in obj.items())}}}"

# 测试
print(serialize(42))         # 数字: 42
print(serialize("hello"))    # 字符串: 'hello'
print(serialize([1, "a"]))   # 列表: ['数字: 1', "字符串: 'a'"]

import functools
import time
from typing import Any

def timer_and_cache(func=None, *, maxsize: int = 128):
    """组合了计时和缓存的装饰器工厂"""
    def decorator(f):
        @functools.wraps(f)
        @functools.lru_cache(maxsize=maxsize)
        def wrapper(*args, **kwargs):
            start = time.perf_counter()
            result = f(*args, **kwargs)
            elapsed = time.perf_counter() - start
            print(f"{f.__name__} 耗时 {elapsed:.6f}s")
            return result
        wrapper.original = f  # 保留原始函数引用
        return wrapper
    return decorator(func) if func else decorator

@timer_and_cache
def compute(n: int) -> int:
    """模拟复杂计算"""
    time.sleep(0.5)
    return n * n

print(compute(10))   # 首次 500ms+
print(compute(10))   # 缓存命中，瞬间返回
print(compute.cache_info())  # CacheInfo(hits=1, misses=1, ...)