Functional Power Tools: functools & itertools

16 min 45 XP

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Lesson 5 of 6 · View course roadmap

Step 1

Learn the idea

Two standard-library modules hold the tools that make expert Python so compact:

functools — tools that operate on functions:

@lru_cache — memoize a function in one line; exponential recursions become instant
reduce(f, seq) — fold a sequence into a single value: reduce(lambda a, b: a * b, [1, 2, 3, 4]) → 24
partial(f, x) — pre-fill some arguments, get a new function back

itertools — an iterator algebra:

combinations("ABC", 2) — every unordered pair
chain(a, b) — one stream from many iterables
count(), cycle(), islice() — infinite streams, safely sliced

Everything in itertools is lazy — these tools compose into data pipelines that process millions of items in constant memory.

Where you'll use this

@lru_cache turns expensive API lookups and recursive algorithms into O(1) repeats — it's a one-line performance patch used all over production code. itertools powers scheduling (cycle), pagination (islice), and combinatorics in testing and pricing engines.

Common mistakes

Caching a function with mutable or unhashable arguments — @lru_cache needs hashable args and will TypeError on lists.
Using reduce where sum(), max() or a comprehension is clearer — reduce is for genuinely custom folds, not a badge of honour.
Printing an itertools result and seeing <itertools.combinations object> — iterators must be consumed (list(), a loop) to see their values.

Pro tip

functools.partial shines with callbacks and key functions: sorted(rows, key=partial(score, weights=w)). It's cleaner than a lambda when you're just pinning arguments.

Step 2

Try it yourself

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The lesson example is loaded and ready — press Run, then change something and run it again. Breaking it is part of learning. Want a clean slate? Tap “New blank”.

PYexample.py

⌘ + Enter to run

Output appears here…

Step 3

Pass the challenge +45 XP

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Use functools.reduce to print the product of the numbers 1–5. Then use itertools.combinations on the string "ABC" to print every 2-letter combination joined as a string, one per line.

Target output

120
AB
AC
BC

PYchallenge.py

from functools import reduce
from itertools import combinations

print(reduce(lambda a, b: a * b, range(1, 6)))
for pair in combinations("ABC", 2):
    print("".join(pair))

Run your code to check it…

Step 4

Check your understanding

1. What does @lru_cache do? Limits recursion depth Stores results so repeated calls with the same arguments return instantly Compresses return values Runs the function in parallel

2. Why is itertools memory-efficient? It uses C arrays Everything is lazy — items are produced one at a time on demand It compresses lists It isn't

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