Reading payment_type before trusting it in ride-hail-trips

Profiling payment_type on ride-hail-trips before anyone builds anything on top of it.

8,000 rows, no nulls, 3 distinct values. card, wallet, or cash.

3 values, and they are not evenly spread: card 62.3%, wallet 26.6%, cash 11.2%. The largest takes 62.3% on its own.

Read the distinct list rather than the distinct count. Casing differences and trailing whitespace produce values that look identical in a report and group separately in SQL, and the count will not show you that — select distinct payment_type order by 1 will, in about a second.

select
  count(*)                       as rows,
  count(payment_type)            as present,
  count(*) - count(payment_type) as nulls,
  count(distinct payment_type)   as distinct_values
from ride_hail_trips;

Low cardinality, stable values — this is a column you can group by without thinking about it, and a reasonable candidate for a chart facet. Check the distinct list, not just the count, because a stray casing variant hides in the count and shows up in the group-by.

Where this bites: a group-by on this column produces 3 rows today. If it is a column an upstream system can add values to, it produces an unknown number tomorrow, and any dashboard laid out for 3 categories reflows without warning. Values arriving is a schema change that no schema check catches.

The grain is one row per completed trip, which is the context every one of those numbers depends on. None of them survive a change of grain, which is why "profile the column" and "profile the table" are the same job. Full schema, and the CSV, on the dataset page.