All 15 functools & Functional Programming Tools questions
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What is the functools module used for in Python? How do you use functools.partial to fix keyword arguments for logging or callback functions? How does functools.partial work and when is it useful? What does functools.reduce do, and why is it often discouraged in favor of loops? How does @functools.wraps preserve metadata when writing decorators? How does functools.total_ordering reduce boilerplate for comparison methods? What does functools.reduce's initializer argument do, and why should you usually provide one? What is the practical difference between using functools.reduce and a plain for-loop for accumulation? How does functools.singledispatch enable function overloading based on argument type? What is functools.cached_property and how does it differ from @property combined with manual caching?
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What is the difference between functools.cache and functools.lru_cache?

Beginner

Answer

functools.cache (added in Python 3.9) is a simpler, unbounded version of lru_cache -- equivalent to lru_cache(maxsize=None). Use cache when you want unlimited caching with simpler syntax, and lru_cache when you need to bound memory usage with maxsize or need typed caching.
from functools import cache

@cache
def factorial(n):
    return 1 if n <= 1 else n * factorial(n-1)

print(factorial(10))
Real-world example A configuration loader caches parsed config file contents with @cache since the file rarely changes during a program's run.

Common follow-ups: When would unbounded caching cause memory problems?;How do you clear a cached function's cache?

functools & Functional Programming Tools;Memory Management & Garbage Collection
All 15 functools & Functional Programming Tools questions