Functional Utilities with `functools`

Standard Library

functools Playbook

functools supercharges functions: cache expensive calls, build partials, and preserve metadata for decorators.

Cache resultsCreate partials

Caching with lru_cache and cache

from functools import lru_cache, cache

@lru_cache(maxsize=256)
def get_exchange_rate(code: str) -> float:
    ...

@cache  # Python 3.9+, unbounded — simpler than lru_cache(maxsize=None)
def fibonacci(n: int) -> int:
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

• Use @cache (Python 3.9+) for simple unbounded memoization.
• Use @lru_cache(maxsize=N) when you need to limit memory.
• cache_info() shows hit/miss stats.

Partials & wraps

from functools import partial, wraps

send_sales_email = partial(send_email, template='sales')

def measure(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        ...
    return wrapper

• partial pre-fills arguments for reuse.
• wraps keeps metadata intact when writing decorators.

reduce & total_ordering

• functools.reduce performs cumulative operations; prefer built-ins like sum when possible.
• @functools.total_ordering fills in comparison methods when you implement __eq__ and one ordering method.

Next up in your learning path

Decorators & context managers

Apply wraps and partial in custom decorators and context managers.

Preview

itertools pairing

Compose iterables with caching for streaming workloads.

Companion

Performance pillar

Combine caching with profiling once you reach the optimization cluster.

Future