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What difficulty level is this coding question?

This is a Medium difficulty Coding & Algorithms question, commonly asked during Onsite rounds at Thumbtack.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Thumbtack during technical interviews.

Implement min, mean, median robustly | Thumbtack Coding Question

Quick Overview

This question evaluates implementation skills in basic descriptive statistics (min, mean, median), numerical stability in online aggregation methods, robust handling of edge cases like None/NaN and extreme numeric ranges, and algorithmic complexity reasoning in the Coding & Algorithms domain for a Data Scientist role.

Implement min, mean, median robustly

Company: Thumbtack

Role: Data Scientist

Category: Coding & Algorithms

Difficulty: Medium

Interview Round: Onsite

Implement three functions in Python without using numpy/pandas: (1) my_min(nums) returning the minimum in O(n) time and O(1) space; (2) my_mean(nums) using a numerically stable online method (e.g., Kahan or Welford) and explaining why naive summation can be inaccurate; (3) my_median(nums) for both odd and even lengths. First provide an O(n log n) solution using sorting, then implement an O(n) expected-time solution using quickselect; discuss behavior for empty lists, presence of None/NaN, and very large integers/floats; analyze time/space complexity and worst-case pitfalls.

Quick Answer: This question evaluates implementation skills in basic descriptive statistics (min, mean, median), numerical stability in online aggregation methods, robust handling of edge cases like None/NaN and extreme numeric ranges, and algorithmic complexity reasoning in the Coding & Algorithms domain for a Data Scientist role.

Minimum of a Numeric List

Return the minimum value, or None for empty input.

Constraints

Inputs are Python literals matching the function signature.
Return a deterministic exact-match value.

Examples

Input: ([3,1,2],)

Expected Output: 1

Explanation: Minimum.

Input: ([],)

Expected Output: None

Explanation: Empty list.

Hints

Use deterministic tie-breaking for prompts with multiple valid outputs.
For design-style APIs, simulate operations with explicit inputs.

Numerically Stable Mean

Return the mean using an online update, rounded to 6 decimals.

Constraints

Inputs are Python literals matching the function signature.
Return a deterministic exact-match value.

Examples

Input: ([1,2,3],)

Expected Output: 2.0

Explanation: Mean 2.

Input: ([],)

Expected Output: None

Explanation: Empty list.

Input: ([1e16,1,-1e16],)

Expected Output: 0.0

Explanation: Large cancellation case.

Hints

Use deterministic tie-breaking for prompts with multiple valid outputs.
For design-style APIs, simulate operations with explicit inputs.

Median of a Numeric List

Return the median after sorting, or None for empty input.

Constraints

Inputs are Python literals matching the function signature.
Return a deterministic exact-match value.

Examples

Input: ([3,1,2],)

Expected Output: 2

Explanation: Odd length.

Input: ([4,1,2,3],)

Expected Output: 2.5

Explanation: Even length.

Input: ([],)

Expected Output: None

Explanation: Empty list.

Hints

Use deterministic tie-breaking for prompts with multiple valid outputs.
For design-style APIs, simulate operations with explicit inputs.

Quick Overview