users +---------+---------+ | user_id | country | +---------+---------+ | 1 | US | | 2 | CA | | 3 | US | +---------+---------+ ​ transactions +--------+---------+--------+ | txn_id | user_id | amount | +--------+---------+--------+ | 10 | 1 | 30.5 | | 11 | 1 | 15.0 | | 12 | 2 | 20.0 | +--------+---------+--------+ ##### Scenario E-commerce analytics team has separate user and transaction tables and needs country-level spend reporting. ##### Question Using pandas, merge the users and transactions DataFrames on user_id; keep only rows with matching users. Group the merged result by country, computing total and average amount, and return a DataFrame sorted by total spend descending. ##### Hints Apply pandas merge, groupby, agg, and sort_values correctly.