Create Country-Level Spend Report Using Pandas

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.