The category that eats the chart

borough on ride-hail-trips has 5 values, and one of them is 30.0% of the data.

• Downtown — 2,399 rows, 30.0%
• Midtown — 2,034 rows, 25.4%
• Uptown — 1,453 rows, 18.2%
• Harbour — 1,149 rows, 14.4%
• Airport — 965 rows, 12.1%

The top three take 73.6% between them. With only 5 values there is no tail to worry about, which makes this a genuinely easy column to chart.

select borough,
       count(*)                                      as rows,
       round(100.0 * count(*) / sum(count(*)) over (), 1) as pct,
       round(avg(fare_usd)::numeric, 2)             as avg_fare_usd
from ride_hail_trips
group by 1
order by rows desc;

The second column is the one that matters. Share of rows tells you what is common; avg_fare_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.