First add a @cache decorator to your module: Python decorators.py import functools # ... def cache(func): """Keep a cache of previous function calls""" @functools.wraps(func) def wrapper_cache(*args, **kwargs): cache_key = args + tuple(kwargs.items()) if cache_key not in ...
def cache_decorator(ttl=60): # ttl指缓存有效时间(秒) def decorator(original_function): cache = {} @functools.wraps(original_function) def wrapper(*args, **kwargs): key = str(args) + str(kwargs) if key not in cache or time.time() - cache[key][0] > ttl: cache[key] = (time....
@lru_cache(maxsize=3)deffibonacci(n):ifn<=1:returnnelse:returnfibonacci(n-1)+fibonacci(n-2)print(fibonacci(5)) 在这个例子中,我们使用functools模块中的lru_cache装饰器,实现了斐波那契数列的缓存功能。这能显著提高递归函数的性能,避免重复计算。 4. 日志记录装饰器 代码语言:javascript 代码运行次数:...
defmemoize(func): cache = {} defwrapper(*args): ifargsincache: returncache[args] result = func(*args) cache[args] = result returnresult returnwrapper @memoize deffibonacci(n): ifn <=1: returnn returnfibonacci(n -1) + fibonacci(n -2) 在递归函数中也可以使用@memoize来优化重复计算。 3...
2、@memoize:缓存结果 在数据科学中，我们经常使用计算成本很高的函数。@memoize装饰器帮助我缓存函数结果，避免了相同输入的冗余计算，显著加快工作流程:def memoize(func): cache = {}def wrapper(*args): if args in cache: return cache[args] result = func(*args) cache[args] = result...
The decorator ignores arguments named 'self' such that it will work across different instances of the same object. from data_cache import pandas_cache from time import sleep from datetime import datetime import pandas as pd class PandasClass: def __init__(self): print(self) @pandas_cache def...
import functoolsimport time# caching up to 12 different results@functools.lru_cache(maxsize=12)defslow_func(x): time.sleep(2) # Simulate long computation return xslow_func(1) # ... waiting for 2 sec before getting resultslow_func(1) # already cached - result returned ...
is_obsolete(cache[key], duration)): print('we got a winner') return cache[key]['value'] # 计算 result = function(*args, **kw) # 保存结果 cache[key] = { 'value': result, 'time': time.time() } return result ...
Learn to use decorators like @functools.lru_cache or @functools.cache to cache functions in Python. Stephen Gruppetta 12 min Tutorial Python Print() Function Learn how you can leverage the capability of a simple Python Print function in various ways with the help of examples. Aditya Sharma 10...
除了第三方库,我们在 Python 标准库上也遇到了一些不顺。例如,functools.lru_cache尽管在typeshed里有类型注解,但由于复杂的原因,它不保留底层函数的签名,所以任何用@functools.lru_cache装饰的函数都会被移除所有类型注解。 例如,Mypy 允许这样做: import functools ...