## Problem Given a list of records with dimensions and a metric, produce a pivot table. Rows are indexed by one dimension, columns by another, and cells contain an aggregated metric (sum or average). ```python def pivot_table( records: list[dict], row_dim: str, col_dim: str, metric: str, agg: str = "sum" # "sum" or "avg" ) -> dict[str, dict[str, float]]: ... ``` ``` Records: [{"region":"North","product":"A","sales":100}, {"region":"North","product":"B","sales":200}, {"region":"South","product":"A","sales":150}, {"region":"South","product":"A","sales": 50}] pivot_table(records, row_dim="region", col_dim="product", metric="sales", agg="sum") Output: { "North": {"A": 100, "B": 200}, "South": {"A": 200} # 150+50 } ``` ## Follow-ups 1. How do you handle missing combinations (e.g., South has no product B)? Return 0, null, or omit the key? 2. Add a `totals=True` option that appends row totals and a grand total column. Walk through the implementation. 3. Support a third dimension as a page filter (e.g., filter to only `year=2024` before pivoting). How does the function signature change? 4. If records come from a SQL database, write the equivalent SQL `GROUP BY` query with `CASE WHEN` column pivoting.

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Data Pivot Table: Transform Row-Level Records into a Cross-Tabulated Summary Table

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Problem Given a list of records with dimensions and a metric, produce a pivot table. Rows are indexed by one dimension, columns by another, and cells contain an aggregated metric (sum or average). Follow-ups How do you handle missing combinations (e.g., South has no product B)? Return 0, null, or omit the key? Add a totals=True option that appends row totals and a grand total column. Walk through the implementation. Support a third dimension as a page filter (e.g., filter to only year=2024 befor…

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This is a candidate experience report from a instacart interview during the onsite round.

It covers the following topics: Coding, Sql, Onsite .

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Data Pivot Table: Transform Row-Level Records into a Cross-Tabulated Summary Table

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