Review Preprocessing Code and Tests
Company: Capital One
Role: Data Scientist
Category: Coding & Algorithms
Difficulty: easy
Interview Round: Onsite
You are reviewing a small Python preprocessing codebase during an interview. You do not need to write code.
**Part A: Environment and execution**
A shell script activates a Python virtual environment and runs a data-processing job. Explain what such a script is typically doing and why isolated virtual environments are useful in collaborative analytics work.
**Part B: Outlier processor**
You are shown an `OutlierProcessor` class with three methods:
- `input_check(df, columns)`: validates inputs,
- `fit(df)`: computes lower and upper percentile cutoffs for selected columns,
- `transform(df)`: truncates values outside those cutoffs.
Explain how this class should work, what failure cases you would look for, and what unit tests you would add.
**Part C: Imputer review**
You are shown a messy `Imputer` class that implements `mean`, `median`, and `mode` filling strategies. What design, readability, and reliability improvements would you recommend?
Quick Answer: This question evaluates a candidate's competency in data-science engineering tasks including code review, data preprocessing techniques (outlier handling and imputation), reproducibility via virtual environments, and design and unit testing of data pipelines.
