Clean and Summarize User Purchase Data Efficiently

transactions

+-----------+---------------------+-----------+--------+ | user_id | txn_timestamp | txn_value | txn_id | +-----------+---------------------+-----------+--------+ | 201 | 2023-09-01 09:00:00 | 15.50 | 1 | | 201 | 2023-09-01 09:05:00 | 17.00 | 2 | | 202 | 2023-09-01 10:00:00 | 20.00 | 3 | | 201 | 2023-09-02 11:00:00 | 5.00 | 4 | | 203 | 2023-09-02 11:05:00 | 22.00 | 5 | +-----------+---------------------+-----------+--------+

Scenario

You have a pandas DataFrame with user purchase history and need to clean and summarize it for analysts.

Question

a) Implement a function that removes every user who has fewer than 100 transactions in any calendar month. b) Implement a second function that returns the average time between consecutive transactions, in seconds, for each remaining user.

Hints

Group by user & month; use shift to compute timedeltas; convert Timedelta to seconds via .dt.total_seconds().