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Walmart Labs·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Got a technical screen for a Data Scientist role at Walmart Labs that was basically one pandas coding question. Pretty straightforward if you know your way around DataFrames, but the data structure they gave you requires a bit of thought before you just start typing.

Questions Asked (1)

Q1

Given a dictionary where each key is a column name and each value is a list of [row_id, cell_value] pairs, write Python code using pandas to produce a wide DataFrame aligned by row_id.

Data ModelingAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

The structure trips you up for a second because it's not a normal dict-of-lists you can just throw at pd.DataFrame().

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AI HintsAI Generated

Suggested Approach

First, explain that you will flatten the dictionary into a long-format DataFrame with columns for column name, row_id, and cell_value. Then, use pandas pivot to reshape the long DataFrame into a wide format, with row_id as the index and column names as columns. Finally, discuss handling potential issues like duplicate entries or missing values.

Pro tip: Mention that using pivot_table with an aggregation function (e.g., 'first') handles duplicate (row_id, column) pairs gracefully, which is common in real-world messy data. Also, note that setting row_id as index ensures proper alignment and efficient memory usage.

1. Flatten the dictionary

Iterate over the dictionary items to create a list of records, each containing the column name, row_id, and cell_value. Convert this list into a pandas DataFrame.

2. Pivot to wide format

Use the pivot method (or pivot_table) with row_id as index, column name as columns, and cell_value as values to reshape the data into a wide DataFrame.

3. Handle duplicates and missing values

If duplicates exist, decide on an aggregation strategy (e.g., first, mean) and apply it. For missing values, consider filling with a default or leaving as NaN based on requirements.

4. Optimize and validate

Ensure the resulting DataFrame has the correct data types, and consider memory usage. Validate by checking shape, column names, and sample rows.

Key Points to Mention

  • Use pd.DataFrame to create a long-format DataFrame from the dictionary.
  • Leverage pivot or pivot_table for reshaping, noting the difference and when to use each.
  • Discuss handling duplicate (row_id, column) pairs with aggregation functions.
  • Mention setting row_id as index for alignment and performance.
  • Consider data types and memory efficiency, especially with large datasets.
  • Validate the output by comparing with expected structure or using assertions.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.