Reading flight_no before trusting it in flight-delays

Profiling flight_no on flight-delays before anyone builds anything on top of it.

8,000 rows, no nulls, 4,715 distinct values. Flight number.

The five-number version: min 101, p10 761, median 3,574, p90 6,328, max 6,999. The mean is 3,560.

The p10 to p90 band — 761 to 6,328 — is where ordinary rows live, and it is the pair worth quoting when somebody asks what to expect. Min and max describe the two strangest rows in the table and nothing else; they are useful for spotting impossible values and misleading for everything else.

select
  count(*)                    as rows,
  count(flight_no)            as present,
  count(*) - count(flight_no) as nulls,
  count(distinct flight_no)   as distinct_values
from flight_delays;

Nothing dramatic: the mean and median are within 0.4% of each other, so an average is a fair summary. That is worth confirming rather than assuming — it is not true of most money columns.

Where this matters: a symmetric column is one you can average, threshold and chart without hedging, which makes it unusually cheap to work with. Knowing which of your columns are like this and which are not is most of knowing when to be careful.

The grain is one row per scheduled flight, 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.