Took me a minute to even figure out what the output shape should look like.
Clarify the input structure and desired pivot table format, then design a function that groups data by row and column keys and aggregates values. Implement using a hash map of hash maps for efficiency, and discuss trade-offs like handling missing values and choosing aggregation functions.
Pro tip: Demonstrate awareness of real-world data quirks: mention how you'd handle duplicate entries, missing combinations, and large datasets that don't fit in memory, showing you think beyond the happy path.
Ask about the input format (e.g., list of records, DataFrame), the desired output structure (e.g., nested dict, 2D array), and how to handle aggregation (sum, count, average) and missing values.
Identify the row key, column key, and value fields. Explain that the pivot table will have unique row keys as rows and unique column keys as columns, with aggregated values at intersections.
Propose using a hash map (dictionary) to group data: first by row key, then by column key, accumulating values. Discuss time complexity O(n) and space O(r*c) where r and c are unique row and column keys.
Address missing combinations (fill with 0 or None), duplicate entries (aggregate), and non-numeric values (if applicable). Mention sorting keys for deterministic output.
Write clean code with meaningful variable names, and walk through a small example to verify correctness. Discuss potential optimizations for large datasets, like using generators or external sorting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.