sales +------------+-----------+-------+---------+ | date | product_id| units | revenue | +------------+-----------+-------+---------+ | 2023-01-01 | 101 | 5 | 100.0 | | 2023-01-02 | 101 | 3 | 60.0 | | 2023-01-03 | 102 | 8 | 160.0 | | 2023-01-04 | 101 | 2 | 40.0 | | 2023-01-05 | 102 | 7 | 140.0 | +------------+-----------+-------+---------+ ##### Scenario Cleaning and aggregating daily sales data to compute rolling revenue trends per product. ##### Question Using Python/pandas on the sales table, compute the 3-day rolling average of revenue for each product_id, pivot the result so dates are rows and product_ids are columns, and include total units sold per product. ##### Hints Show imports, set date index, sort, groupby+rolling, pivot, merge totals; code should run end-to-end.