sales +-----------+------------+----------+-------+ | order_id | order_date | is_prime | price | +-----------+------------+----------+-------+ | 1001 | 2023-01-15 | 1 | 25.99 | | 1002 | 2023-01-20 | 0 | 18.50 | | 1003 | 2023-02-05 | 1 | 45.00 | | 1004 | 2023-02-14 | 0 | 12.99 | | 1005 | 2023-03-03 | 1 | 33.25 | +-----------+------------+----------+-------+ ##### Scenario E-commerce analytics team needs monthly insights on Prime vs non-Prime sales and price-bucket performance over the last year. ##### Question Write an SQL query that returns, for each of the past 12 months, total sales split into Prime and non-Prime customers. Using Python (pandas), compute for each month the percentage of sales contributed by each dynamically defined price_bucket (you decide reasonable bucket boundaries). ##### Hints Use month truncation, GROUP BY, window functions; create bins with pd.cut; ensure percentages sum to 100% per month.