All 15 functools & Functional Programming Tools questions
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What is the difference between functools.cache and functools.lru_cache? 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 functools module used for in Python?

Beginner

Answer

functools provides higher-order functions that act on or return other functions, such as tools for caching (lru_cache, cache), partial application (partial), reducing sequences (reduce), and customizing comparisons (cmp_to_key, total_ordering). It centralizes common functional-programming utilities so you don't reimplement them.
import functools

@functools.lru_cache(maxsize=None)
def fib(n):
    return n if n < 2 else fib(n-1) + fib(n-2)

print(fib(30))  # cached, fast
Real-world example A pricing service caches expensive tax-rate lookups per region using lru_cache to avoid repeated database hits.

Common follow-ups: What's the difference between functools.cache and lru_cache?;When would you avoid caching a function?

Decorators;Closures
All 15 functools & Functional Programming Tools questions