Transform Column

An easy Python interview practice problem on DataDriven. Write the Python and run it against test cases, with instant feedback.

Domain
Python
Difficulty
easy
Seniority
L3

Problem

A monitoring export hands you a column of numeric readings. Return a new column holding each reading squared, positioned the same as the reading it came from.

Same data, new shape.

Example 1

Input:

values = [1,2,3]

Output:

[1,4,9]

Example 2

Input:

values = [0]

Output:

[0]

Worked solution and explanation

What this really is

Under the 'square a column' costume this is a plain element-wise map: one transform applied to every reading, whatever the length, with each result landing where its reading sat. Anyone can multiply; what separates candidates is not assuming the shape of the visible examples. Treat the input as a few positive integers and you break on an empty export, on a two-reading export, or on fractional readings, where an int() around the product turns 1.5 squared into 2 instead of 2.25. And negatives need no special case: a number times itself is never negative, so an abs() or sign guard is noise, not a safeguard.

Break down the requirements

Step 1: Iterate through the input list

Walk the input once and square each element. A single pass is all the ordered map needs, and it works the same for zero readings or ten thousand.

Step 2: Collect results in a new list

Accumulate each squared value into a separate list you return, keeping whatever numeric type the reading had. This is also the move that keeps the caller's input intact.

The solution

Element-wise squaring into a fresh list
def square_list(values: list) -> list:
    return [val * val for val in values]

A single-level comprehension: the clear idiom, one pass, a new list.

Common follow-up questions