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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​

from functools import lru_cache

@lru_cache(maxsize=256)
def get_exchange_rate(code: str) -> float:
    ...
  • Perfect for I/O-bound operations or expensive calculations.
  • 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

Extend this knowledge in the advanced iteration cluster (coming soon).

Preview

itertools pairing

Compose iterables with caching for streaming workloads.

Companion

Performance pillar

Combine caching with profiling once you reach the optimization cluster.

Future