transactions +---------+---------------------+---------+ | user_id | trans_ts | amount | +---------+---------------------+---------+ | 11 |2024-06-03 10:00:00 | 25.80 | | 11 |2024-06-03 10:05:00 | 10.50 | | 12 |2024-06-03 12:00:00 | 40.00 | | 11 |2024-06-04 09:00:00 | 15.00 | | 12 |2024-06-05 13:20:00 | 33.30 | +---------+---------------------+---------+ ##### Scenario Analyst must clean monthly transaction logs and derive user-level features for downstream modeling. ##### Question Implement a Python function that removes users with fewer than 100 transactions per calendar month. Implement another function that returns each user's average time between consecutive transactions in seconds. ##### Hints Use pandas groupby with size()/filter and shift() on sorted timestamps; convert Timedelta to .dt.total_seconds().