The Transaction Mix
Product at Northline Bank has just rebuilt the transfer screen and wants to know what it is dealing with: how the month's activity splits across deposits, withdrawals and transfers. A feature used once is a very different investment case from one used constantly.
transactions — one row per movement of money. customer_id points at a row in customers; type is one of deposit, withdrawal or transfer; txn_date is the day it settled. The ledger is signed: money arriving in an account is stored as a positive amount, and money leaving it as a negative one, so a 600-dollar withdrawal is recorded as -600.
| id | customer_id | amount | type | txn_date |
|---|---|---|---|---|
| 1 | 1 | 500 | deposit | 2023-07-01 |
| 2 | 1 | -120 | withdrawal | 2023-07-03 |
| 3 | 2 | 1000 | deposit | 2023-07-02 |
| 4 | 2 | -300 | withdrawal | 2023-07-05 |
| 5 | 3 | 250 | deposit | 2023-07-04 |
| 6 | 3 | -600 | withdrawal | 2023-07-06 |
| 7 | 4 | 800 | deposit | 2023-07-07 |
| 8 | 4 | -450 | withdrawal | 2023-07-08 |
| 9 | 5 | 2000 | deposit | 2023-07-09 |
| 10 | 5 | -1500 | withdrawal | 2023-07-10 |
| 11 | 1 | -200 | transfer | 2023-07-11 |
| 12 | 3 | 400 | deposit | 2023-07-12 |
The table already exists in the database — there is nothing to create or load.
Task: Write a query that returns one row per transaction type, with columns type and n, n being how many movements carry that type. Most frequent first; two types on the same number come back in alphabetical order of type.
Example output — shape only, on invented tallies. The type labels are the real stored ones; the counts are made up:
| type | n |
|---|---|
| withdrawal | 31 |
| transfer | 22 |
| deposit | 14 |
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