functools_module

functools

Higher-order function utilities — caching, partial application, reduction, and decorator helpers.

Files

File	Description
example.py	lru_cache, partial, reduce, pipelines

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Descriptive Example

Scenario

Memoize Fibonacci to turn exponential recursion into linear time.

import functools

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

print(fibonacci(100))           # instant
print(fibonacci.cache_info())   # hits, misses, size

Without cache: O(2^n). With cache: O(n) — each subproblem computed once.

Partial application

def multiply(a, b, c):
    return a * b * c

double_first = functools.partial(multiply, 2)
print(double_first(5, 3))   # 2 * 5 * 3 = 30

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Interview Q&A

Q1: What does @lru_cache do?
A: Least Recently Used cache — stores function results keyed by arguments. Repeated calls with same args return cached result instantly.

Q2: When should you NOT use lru_cache?
A: When arguments are unhashable (lists, dicts), function has side effects, or return values are mutated by callers.

Q3: What is functools.partial?
A: Pre-fills some arguments of a function, returning a new callable with remaining args. Useful for callbacks and configuration.

Q4: What is functools.wraps?
A: Copies __name__, __doc__, etc. from wrapped function to wrapper. Essential for decorators (see decorator).

Q5: What is functools.reduce?
A: Applies a binary function cumulatively: reduce(add, [1,2,3]) → 6. Moved from builtins to functools in Python 3.

Q6: lru_cache vs manual dict cache?
A: lru_cache handles eviction (maxsize), thread safety (with lock), and cache statistics. Manual dict is fine for simple cases.

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Run

python3 example.py