itertools in Data Science: Product, Combinations, Permutations

Intermediate Python itertools
Created by Pavel · 12.03.2026 at 07:54 UTC · 2 completed

The itertools module provides iterator tools for combinatorial generation.

Typical Data Science usage:
- product: hyperparameter grids,
- combinations: unordered feature subsets,
- permutations: order-sensitive pipeline variants,
- islice: inspect only first N generated candidates.

Example:

from itertools import product

grid = list(product([3, 5], [0.01, 0.1], ['l1', 'l2']))

Edge cases and cautions:
- search spaces can grow very quickly,
- avoid converting huge iterators to lists prematurely,
- use lazy iteration and filters when possible.

University approvals: 0
Tasks
Question 1

You need all (depth, learning_rate) pairs from two independent lists. Which iterator is correct?

Hint

This is a Cartesian product.

Question 2

Code task: implement build_grid(depths, lrs) returning all (depth, lr) pairs.

Submission format: submit a full function definition def build_grid(...): ....

Hint

Use itertools.product and convert to list.

Starter code is prefilled; replace TODO blocks with your solution.
2 test cases will be used for grading
Run checks runtime behavior only. Final correctness is evaluated when you submit.
Question 3

Why use islice with permutations in large searches?

Hint

Avoid generating everything when not needed.

Question 4

Code task: implement first_n_permutations(items, n) using permutations and islice.

Submission format: submit a full function definition def first_n_permutations(...): ....

Hint

Wrap islice(permutations(items), n) with list(...).

Starter code is prefilled; replace TODO blocks with your solution.
2 test cases will be used for grading
Run checks runtime behavior only. Final correctness is evaluated when you submit.
Card Info
  • Topic: Python itertools
  • Difficulty: Intermediate
  • Completed: 2 users
Creator
Pavel
Pavel