The category that eats the chart

country on data-job-postings has 7 values, and one of them is 45.1% of the data.

• US — 1,579 rows, 45.1%
• GB — 436 rows, 12.5%
• IN — 417 rows, 11.9%
• DE — 333 rows, 9.5%
• CA — 247 rows, 7.1%

The top three take 69.5% between them. The remaining 4 share 30.5%, which is the part that gets rendered as an unreadable stack of slivers if you plot all of them.

select country,
       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.