Practice / aggregations
CityCycle, the city bike-share, wants to know how busy each day was.
You have the rides DataFrame:
Your task: return one row per ride_date with a column num_rides giving the total number of rides taken that day. (Some rides have a missing station — they still count.)
Assign your answer DataFrame to result. Row order doesn't matter.
ridesinput DataFrame| column | type |
|---|---|
| ride_id | long |
| ride_date | string |
| station | string |
| ride_id | ride_date | station |
|---|---|---|
| 1 | 2026-06-01 | Harbor |
| 2 | 2026-06-01 | Elm St |
| 3 | 2026-06-01 | Harbor |
| 4 | 2026-06-02 | Elm St |
| 5 | 2026-06-02 | null |
| 6 | 2026-06-03 | Harbor |
ride_datenum_rides· 3 rowsgroupBy("ride_date") then agg(F.count("*").alias("num_rides")). Count the
rows on each day — count("*") counts every row, even ones with missing fields.
This problem builds on Aggregations & groupBy (~7 min). Pop it open in a new tab if you want a quick recap.