Field note · August 8, 2025
data-job-postings.city: 10 values, 41.8% in the top three
10 values with 15.2% concentrated in one of them, and what that does to every chart downstream.
Distribution of city on data-job-postings, because every chart built on it inherits this shape.
San Francisco— 532 rows, 15.2%New York— 495 rows, 14.1%London— 436 rows, 12.5%Bengaluru— 417 rows, 11.9%Berlin— 333 rows, 9.5%
The top three take 41.8% between them. The remaining 7 share 58.2%, which is the part that gets rendered as an unreadable stack of slivers if you plot all of them.
select city,
count(*) as rows,
round(100.0 * count(*) / sum(count(*)) over (), 1) as pct,
round(avg(salary_min_usd)::numeric, 2) as avg_salary_min_usd
from data_job_postings
group by 1
order by rows desc;The second column is the one that matters. Share of rows tells you what is common; avg_salary_min_usd tells you whether the common thing is the important thing. They disagree more often than not, and a chart that shows only the first is answering the easier question.
Decide what happens to the tail before you plot it. "Other" as an explicit bucket is honest; twelve slivers is not, and neither is silently taking the top eight.