transactions +---------------+-------------+------------+--------+ | transaction_id| from_user_id| to_user_id | amount | +---------------+-------------+------------+--------+ | 1 | 101 | 202 | 50 | | 2 | 303 | 101 | -20 | | 3 | 101 | 404 | 30 | | 4 | 202 | 303 | 100 | | 5 | 404 | 101 | -60 | +---------------+-------------+------------+--------+ ​ users +---------+----------+ | user_id | name | +---------+----------+ | 101 | Alice | | 202 | Bob | | 303 | Carol | | 404 | Dave | +---------+----------+ ##### Scenario A peer-to-peer payments start-up wants quick insights from its transaction log and user directory to understand money flow and user engagement. ##### Question SQL – Using tables users(user_id, name) and transactions(transaction_id, from_user_id, to_user_id, amount), write a query that joins the two tables to return each user’s name, total_amount_sent and total_amount_received. SQL – Add a window function to the previous query to show, for every transaction, the running cumulative amount_sent per from_user_id ordered by transaction_id. Python – Given the same transactions dataframe, output two Series: the top 5 user_ids by number of sent transactions and the top 5 user_ids by number of received transactions (treat negative amounts as money flowing in the opposite direction). ##### Hints For SQL, think LEFT JOINs/Aggregate and SUM with PARTITION BY. For Python, normalise sender/receiver per row, then value_counts.