subscriptions +-----------------+---------+-------------+ | subscription_id | status | status_date | +-----------------+---------+-------------+ | 101 | active | 2023-01-05 | | 101 | inactive| 2023-03-10 | | 102 | inactive| 2023-02-12 | | 102 | active | 2023-04-01 | +-----------------+---------+-------------+ ##### Scenario Subscription analytics – product team wants to understand when customers become active or churn. ##### Question Write an SQL query that explores column values and row patterns to confirm or deny assumptions about the structure of SUBSCRIPTIONS (e.g., uniqueness of subscription_id+status_date, allowed status transitions). In Python (pandas), build a DataFrame that returns, for every subscription_id, the first date it was ACTIVE and the last date it was INACTIVE. ##### Hints Think window functions for SQL; in pandas use groupby with idxmin / idxmax or boolean masks.