user_events +----------+------------+---------------------+-------------+ | user_id | event_type | event_timestamp | product_id | +----------+------------+---------------------+-------------+ | 101 | view | 2024-01-02 10:00:00 | 55 | | 101 | purchase | 2024-01-02 10:05:00 | 55 | | 102 | purchase | 2024-01-03 09:30:00 | 77 | | 101 | purchase | 2024-02-01 12:00:00 | 88 | | 103 | view | 2024-02-02 08:00:00 | 23 | ##### Scenario Online marketplace wants to understand user purchase behavior stored in user_events table. ##### Question SQL: For each user, return the first product_id they purchased and the purchase timestamp. SQL: Count the number of distinct users who made at least two purchases on the same day. SQL: Find the top 3 products by total number of purchases. SQL: Calculate the 7-day rolling average of daily purchases overall. Pandas: Given the same data in DataFrame df, compute daily active users (unique user_id per date). ##### Hints Use window functions, GROUP BY, DISTINCT, rolling(), and groupby().