cachetools

This module provides various memoizing collections and decorators, including variants of the Python Standard Library’s @lru_cache function decorator.

from cachetools import cached, LRUCache, TTLCache

# speed up calculating Fibonacci numbers with dynamic programming
@cached(cache={})
def fib(n):
    return n if n < 2 else fib(n - 1) + fib(n - 2)

# cache least recently used Python Enhancement Proposals
@cached(cache=LRUCache(maxsize=32))
def get_pep(num):
    from urllib.request import urlopen
    url = 'https://peps.python.org/pep-%04d/' % num
    with urlopen(url) as s:
        return s.read()

# cache PyOWM weather data for no longer than ten minutes
from pyowm.owm import OWM
owm = OWM('your-free-api-key')

@cached(cache=TTLCache(maxsize=1024, ttl=600))
def get_weather(place):
    return owm.weather_manager().weather_at_place(place).weather

For the purpose of this module, a cache is a mutable mapping of a fixed maximum size. When the cache is full, i.e. by adding another item the cache would exceed its maximum size, the cache must choose which item(s) to discard based on a suitable cache algorithm.

This module provides multiple cache classes based on different cache algorithms, as well as decorators for easily memoizing function and method calls.

pip install cachetools