Higher-Order Functions & functools
Compose clean functional pipelines and use functools.lru_cache for memoization.
Learning objectives
- Use functools.reduce for custom accumulators
- Cache expensive recursive calls with @lru_cache
Lesson material
Memoization with lru_cache
@lru_cache caches function return values based on input arguments.
Example code
from functools import lru_cache
@lru_cache(maxsize=None)
def fib_fast(n):
if n < 2:
return n
return fib_fast(n - 1) + fib_fast(n - 2)
print("Fibonacci(35):", fib_fast(35))Practice exercise: Reduce Accumulator
Use `functools.reduce` and lambda to compute the product of list `[1, 2, 3, 4, 5]`. Print the product.
Test yourself with the Module 17: Functional Programming Concepts quiz →