Technical Interview Problems: Lists

A collection of common interview problems that use the Python List, and strategies for how to construct optimal solutions.

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Technical Interview Problems in Python: Lists

Lesson 1 of 1

1
Introduction
Lists are a fundamental data type in Python. Projects make extensive use of lists to store data in sequential order. You should expect at least one technical interview question that requires wo…
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2
List Rotation: Slice
For our first problem, we would like to “rotate” a list, or move elements forward in a list by a number of spaces, k. Elements at the greatest index will “wrap around” to the beginning of the …
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3
List Rotation: Indices
Optimizing a solution means reducing the memory required (space complexity), or reducing the number of instructions the computer must execute (time complexity). Sometimes this means entirely reth…
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4
Rotation Point: Linear Search
We’ll continue our theme of list rotation by flipping the problem: given a sorted list rotated k times, return the index where the “unrotated” list would begin. rotated_list = [‘c’, ‘d’, ‘e’, …
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5
Rotation Point: Binary Search
Our last problem had a sorted dataset as the input. Sure, it was rotated, but it started out sorted. A sorted list gives us ways to leverage that ordering. After all, someone went through a lo…
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6
Remove Duplicates: Naive
Duplicate values? Who needs them! Given a list of values with duplicates, return a list holding the same values in the same order with all duplicates removed. duplicates = [‘a’, ‘a’, ‘x’, ‘x’…
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7
Remove Duplicates: Optimized
For the last problem our suggested solutions had O(N) time and/or O(N) space complexity. We can’t improve the time complexity. We have to visit each value to determine whether or not it is a dupli…
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8
Max list sub-sum: Naive
Our next problem calculates sums within a list. Given a list of integers, return the maximum sum possible from a contiguous sub-list. A sub-list is an uninterrupted portion of the list (up to…
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9
Max List Sub-Sum: Optimized
Our last solution had a cubic time complexity: O(N^3). One iteration for the length of the list O(N), inside another iteration for the length of the list O(N^2), inside the inner loop, we copi…
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10
Pair Sum: Naive
We’ll end with a classic interview question: given a list of integers and a “target” integer, return a pair of indices whose values sum to the target. nums = [4, 2, 8, 9, 6] target = 8 pair_s…
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11
Pair Sum: Optimized
We’ll explore a common trade-off: time vs. space. Our previous solution used nested for loops to iterate through each element in the list and then iterate again for each element in the list to…
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12
Review
Lists are a fundamental data structure for any program. When faced with a technical interview problem using lists, here are a few strategies to keep in mind: * Be comfortable with variables which …
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