Join us at FabCon Atlanta from March 16 - 20, 2026, for the ultimate Fabric, Power BI, AI and SQL community-led event. Save $200 with code FABCOMM.
Register now!Get Fabric certified for FREE! Don't miss your chance! Learn more
Hello Everyone,
I was exploring below sales data in pyspark notebook after converting the panda df to pyspark df.
I wanted to explore the data through some chart. when I chose Pie chart , dispite different chart type the chart setting is not customising itself, I mean having the same field like x-axis, y-axis which is relevent with barchart or column chart.
It also giving me two pie chart.I tried different field selection but seems weird.
Am I missing something here? Need your experties please. Thank you.
Regards,
Amit Devkatte
Solved! Go to Solution.
Hey Amit,
It’s actually the Series group setting causing the double charts.
You have Qty selected there, so it’s trying to split the view and create a separate pie for every quantity.
Just clear the Series group field (leave it empty) and you’ll get a single chart.
For the mapping confusion:
- Xaxis = Legend (CityId)
- Yaxis = Values (Qty)
If you find the notebook charts limiting, you can create a Visual Query from your Lakehouse explorer. It gives you a full drag-and-drop interface to build your charts without coding!
(Just remember to save your DataFrame as a table first, otherwise the Visual Query editor won’t pick it up).
Let me know if that works. If it does, a Kudo and marking as Solved would be appreciated!
QuentinGa
Hey Amit,
It’s actually the Series group setting causing the double charts.
You have Qty selected there, so it’s trying to split the view and create a separate pie for every quantity.
Just clear the Series group field (leave it empty) and you’ll get a single chart.
For the mapping confusion:
- Xaxis = Legend (CityId)
- Yaxis = Values (Qty)
If you find the notebook charts limiting, you can create a Visual Query from your Lakehouse explorer. It gives you a full drag-and-drop interface to build your charts without coding!
(Just remember to save your DataFrame as a table first, otherwise the Visual Query editor won’t pick it up).
Let me know if that works. If it does, a Kudo and marking as Solved would be appreciated!
QuentinGa
Thank you so much for taking time to respond for the solution. I will try tomorrow and let you know the outcome.
Regards,
Amit Devkatte
Share feedback directly with Fabric product managers, participate in targeted research studies and influence the Fabric roadmap.
Check out the February 2026 Fabric update to learn about new features.
| User | Count |
|---|---|
| 18 | |
| 7 | |
| 4 | |
| 3 | |
| 3 |