Practice / aggregations
The Daily Grind's finance team wants a refund tally per store — and crucially, a store that issued no refunds should still show up with a 0, not vanish from the report.
You have the txns DataFrame:
Your task: return each store and a column refunds — the number of transactions whose status is "refund". Every store must appear, including those with zero refunds. Use conditional aggregation rather than pre-filtering.
Assign your answer DataFrame to result. Row order doesn't matter.
txnsinput DataFrame| column | type |
|---|---|
| txn_id | long |
| store | string |
| status | string |
| txn_id | store | status |
|---|---|---|
| 1 | Downtown | sale |
| 2 | Downtown | refund |
| 3 | Downtown | sale |
| 4 | Pier | refund |
| 5 | Pier | refund |
| 6 | Airport | sale |
| 7 | Airport | sale |
storerefunds· 3 rowsUse conditional aggregation, not a pre-filter:
F.sum(F.when(F.col("status") == "refund", 1).otherwise(0)).alias("refunds").
The .otherwise(0) is what keeps zero-refund stores in the result.
This problem builds on Aggregations & groupBy (~7 min). Pop it open in a new tab if you want a quick recap.