New vs Returning Riders by Month
For each month, split riders with a completed trip into new riders (their first completed trip ever was in that month) and returning riders (their first completed trip was in an earlier month).
Expected output
month (YYYY-MM), new_riders, returning_riders. Sorted by month.
Row order is checked. Column names are not checked; numbers are compared to 2 decimals.
Dataset: Ride-share marketplace
Trip requests in four US cities from January to March 2026, with drivers, riders, fares and ratings.
drivers
One row per driver. A driver only serves their own city.
| driver_id | INTEGER | primary key |
| name | TEXT | |
| city | TEXT | |
| joined_date | TEXT | YYYY-MM-DD |
| vehicle_type | TEXT | standard, comfort or xl |
riders
One row per rider account.
| rider_id | INTEGER | primary key |
| name | TEXT | |
| city | TEXT | |
| signup_date | TEXT | YYYY-MM-DD |
trips
One row per trip request, whatever its outcome.
| trip_id | INTEGER | primary key, increases with requested_at |
| rider_id | INTEGER | |
| driver_id | INTEGER | NULL when no driver was assigned |
| city | TEXT | |
| requested_at | TEXT | YYYY-MM-DD HH:MM:SS (UTC) |
| dropoff_at | TEXT | NULL unless completed |
| status | TEXT | completed, cancelled_rider, cancelled_driver or no_driver |
| distance_km | REAL | estimated route distance |
| fare | REAL | USD, NULL unless completed |
| rider_rating | INTEGER | 1-5 stars given by the rider, NULL if not rated |
Engine: SQLite. Dates are text (YYYY-MM-DD); use strftime(), date() and julianday() for date math.
Related problems
- Monthly Revenue Trend (easy)
- Average Order Value by Country (easy)
- Quarterly Cancellation Rate (medium)
- Repeat Purchase Rate (medium)
- Days from Signup to First Order (medium)