Ticket volume by topic
A count of rows per topic - the question asks about volume, so nothing is averaged and nothing is weighted.
How it decided
Support tickets: 3,358 rows, 8 columns. topic, counted. No other column is needed for a volume question.
3,358 rows in, 10 out across 2 columns. A count of rows per topic - the question asks about volume, so nothing is averaged and nothing is weighted.
hbar. Topic names are sentences, so the bars run horizontally rather than tilting the labels 45 degrees.
Palette, spacing, axis titles and legend placement applied. 0 issues found.
The transformation
This is the code, not a description of it — the chart above was drawn from what it returns, and the notebook download runs the same lines against the same file.
result = df.groupby("topic", as_index=False).agg(tickets=("ticket_id", "count")).sort_values("tickets", ascending=False)What it charted
10 rows out of 3,358, 2 columns. First 8 shown.
| topic | tickets |
|---|---|
| Billing | 742 |
| Login | 602 |
| Data import | 464 |
| Charts | 364 |
| Performance | 301 |
| SQL editor | 245 |
| Connectors | 219 |
| Exports | 182 |