Pivot Table Creation

Pandas data engineering interview problem. Difficulty: beginner. Pattern: Pivot. About 12 minutes. Free to practice.

Pivot long product amounts to wide columns per date. Treat this as a production helper: match the contracted return shape, including empty and duplicate inputs.

A long table has 1 row for each date and product. A pivot table turns it into a wide table with 1 row per date and 1 column per product, which is easier to read. From the DataFrame long (with date, product and amount), create a pivot table where each row is a date, each column is a product, and each cell is the sum of amount for that date and product. Show 0 for combinations that have no sales. Reset the index so that date is a normal column, not the index. Save your final table in result.

Requirements

• Use dates as rows and products as columns.
• Each cell is the sum of amount for that date and product.
• Fill missing combinations with 0.
• Reset the index so date is a column.

Constraints

• The input is long with the columns date, product and amount.
• Use pivot_table(index='date', columns='product', values='amount', aggfunc='sum', fill_value=0).
• The product columns are A, B and C, as they appear in the data.

Examples

Input: long sales Output: date | A | B | C 2024-01-01 | 10.0 | 5.0 | 0.0 2024-01-02 | 7.0 | 0.0 | 3.0 Missing product/date pairs become 0.

Topics: lakebench, pandas, pivot_table.

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