Sales +------------+---------+------------+--------+---------+ | date | store_id| product_id | units | revenue | +------------+---------+------------+--------+---------+ |2023-01-01 |1 |101 |3 |30.00 | |2023-01-01 |1 |102 |2 |40.00 | |2023-01-02 |2 |101 |5 |50.00 | |2023-01-02 |2 |103 |1 |20.00 | |2023-01-02 |3 |101 |4 |40.00 | +------------+---------+------------+--------+---------+ ​ Stores +---------+---------+ | store_id| region | +---------+---------+ |1 | West | |2 | East | |3 | Central | +---------+---------+ ##### Scenario Pandas data-wrangling coding task on sales data ##### Question Using pandas, compute total revenue per store per day from the sales table. Merge the sales table with the stores table on store_id, then list the top three regions by total revenue. ##### Hints Use groupby, agg, merge, sort_values, and reset_index appropriately.