Reading o3_ppb before trusting it in city-air-quality

Profiling o3_ppb on city-air-quality before anyone builds anything on top of it.

5,480 rows, no nulls, 522 distinct values. Ozone, parts per billion.

The five-number version: min 4.00, p10 15.00, median 31.10, p90 45.31, max 64.50. The mean is 30.48.

The p10 to p90 band — 15.00 to 45.31 — 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(o3_ppb)            as present,
  count(*) - count(o3_ppb) as nulls,
  count(distinct o3_ppb)   as distinct_values
from city_air_quality;

Nothing dramatic: the mean and median are within 2.0% 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 city per day, 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.