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This is a medium difficulty Coding & Algorithms question, commonly asked during Technical Screen rounds at Capital One.

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

Critique and Test Python Preprocessing Utilities Effectively

Quick Overview

This question evaluates a candidate's ability to review and critique Python preprocessing utilities, covering competencies in software design patterns (fit/transform separation), coding style and import hygiene, and unit-test specification for data-science pipelines.

Company: Capital One

Role: Data Scientist

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Code review of Python preprocessing utilities (OutlierHandler and three Imputer classes) ##### Question Summarize, at a high level, what the OutlierHandler class accomplishes. Why is keeping fit and transform as separate methods advantageous? Identify and critique any coding-style or import problems found in the three Imputer classes. Hand-write one critical unit test you would add for OutlierHandler. ##### Hints Think scikit-learn patterns, PEP-8, avoiding "from numpy import *", assert expected vs. actual.

Quick Answer: This question evaluates a candidate's ability to review and critique Python preprocessing utilities, covering competencies in software design patterns (fit/transform separation), coding style and import hygiene, and unit-test specification for data-science pipelines.

In many preprocessing pipelines, a utility first learns statistics from training data (`fit`) and later applies them to any dataset (`transform`). Implement a simplified OutlierHandler. Your function receives two lists: - `train`: the data used to learn outlier thresholds - `values`: the data to transform Rules: 1. Ignore `None` values in `train`. 2. Sort the remaining values and compute quartiles using this definition: - If the number of values is odd, exclude the overall median when splitting into lower and upper halves. - `Q1` is the median of the lower half. - `Q3` is the median of the upper half. - The median of an even-length list is the average of the two middle values. 3. Compute `IQR = Q3 - Q1`. 4. Compute bounds: - `lower = Q1 - 1.5 * IQR` - `upper = Q3 + 1.5 * IQR` 5. Transform each element in `values`: - keep `None` as `None` - if a number is below `lower`, replace it with `lower` - if a number is above `upper`, replace it with `upper` - otherwise leave it unchanged If `train` contains fewer than 2 non-`None` values, return `values` unchanged. Return the transformed list.

Constraints

0 <= len(train), len(values) <= 100000
Each element is either an integer, a float, or None
Quartiles must be computed from `train` only, never from `values`

Examples

Input: ([10, 12, 13, 14, 15, 16, 18, 19, 100], [5, 15, 30])

Expected Output: [5, 15, 27.5]

Explanation: From the training data, Q1 = 12.5 and Q3 = 18.5, so IQR = 6. The bounds are 3.5 and 27.5. Only 30 is above the upper bound, so it is clipped to 27.5.

Input: ([None, 1, 2, 2, 3, 4, 100], [None, -5, 8, 4])

Expected Output: [None, -1.0, 7.0, 4]

Explanation: Ignoring None, the sorted training data is [1, 2, 2, 3, 4, 100]. Q1 = 2, Q3 = 4, so IQR = 2 and the bounds are -1 and 7. None stays None, -5 becomes -1, 8 becomes 7, and 4 stays unchanged.

Hints

Write a small helper function to compute the median of a sorted list, then reuse it for Q1 and Q3.
This is a fit/transform pattern: learn the clipping bounds from `train` once, then apply them to every element in `values`.