What salary_max_usd actually contains in data-job-postings

Working through data-job-postings again. salary_max_usd is the column people trip over, so here is what it actually looks like.

3,500 rows, 762 nulls (21.8%), 554 distinct values. Top of the disclosed band. Blank when undisclosed.

The five-number version: min 22,500, p10 55,350, median 131,000, p90 228,500, max 409,000. The mean is 138,807.

The p10 to p90 band — 55,350 to 228,500 — 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.

762 rows have no value at all here, which is 21.8% of the table. That is enough to move an aggregate and small enough that nobody notices it doing so — every average above is computed over 2,738 rows, not 3,500, and the two denominators produce different answers.

select
  count(*)                         as rows,
  count(salary_max_usd)            as present,
  count(*) - count(salary_max_usd) as nulls,
  count(distinct salary_max_usd)   as distinct_values
from data_job_postings;

Nothing dramatic: the mean and median are within 6.0% 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 posting, 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.