Support marginal_x/marginal_y="heatmap" in density_heatmap/density_contour #5706

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1. robertclaus commented on Aug 17, 2026

   

   robertclaus

   on Aug 17, 2026

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   @lucasjamar thank you for submitting this and opening a PR! If you don't mind adding an image too that would be great!
2. robertclaus

   added

   P3backlogbacklog

   enhancement

   size: 1

   on Aug 17, 2026
3. lucasjamar commented on Aug 18, 2026

   

   lucasjamar

   on Aug 18, 2026

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   @lucasjamar thank you for submitting this and opening a PR! If you don't mind adding an image too that would be great!

   addressed in 789b082
4. robertclaus commented on Aug 18, 2026

   

   robertclaus

   on Aug 18, 2026

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   @lucasjamar I meant an image in the PR description to show the feature. That commit doesn't seem to have an image if you meant something else?
5. emilykl commented on Aug 20, 2026

   

   emilykl

   on Aug 20, 2026

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   Thanks for the issue and PR @lucasjamar! I think this is a reasonable feature request.

   Are you able to share some examples of the types of charts you're trying to create, or data you're trying to visualize? That would be helpful for discussion if you're able to share.

   I propose removing density_contour from the scope of this work, and supporting marginal heatmaps only for density_heatmap.

   Two reasons:

   1. px.density_heatmap() doesn't use color fills by default, so there's no extra synergy achieved by pairing marginal heatmaps with density_contour plots. At that point, you might as well allow marginal heatmaps for all plot types (which is another possibility!)
   2. Possibly more important: Even if you do manually add fills to the density_contour plot, colorbars for histogram2dcontour traces (which is what density_contour uses under the hood) aren't compatible with heatmap colorbars. Heatmap colorbars are continuous, while histogram2dcontour colorbars are discrete and place labels at the contour boundaries. This results in weird visual artifacts and a misleading colorbar when using the PR implementation (see screenshot below), and I'm not sure if there's a way to reconcile the two (although maybe it's possible).

   Code:

   fig = px.density_contour(
       df,
       x="total_bill",y="tip", 
       marginal_x="heatmap", marginal_y="heatmap"
   )
   fig.update_traces(
       selector=dict(type='histogram2dcontour'),
       contours_coloring="fill", 
   )
   fig.show()

   Screenshot:

   

   For reference, here's what the histogram2dcontour colorbar looks like normally:

   

   The implementation for density_heatmap, on the other hand, looks pretty reasonable and I'd be happy to have it as a feature.

   Let us know your thoughts!

   👍React with 👍1Reacted by Lucas Jamar
6. lucasjamar commented on Aug 23, 2026

   

   lucasjamar

   on Aug 23, 2026

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   Thanks for the issue and PR @lucasjamar! I think this is a reasonable feature request.

   Are you able to share some examples of the types of charts you're trying to create, or data you're trying to visualize? That would be helpful for discussion if you're able to share.

   I propose removing density_contour from the scope of this work, and supporting marginal heatmaps only for density_heatmap.

   Two reasons:

   1. px.density_heatmap() doesn't use color fills by default, so there's no extra synergy achieved by pairing marginal heatmaps with density_contour plots. At that point, you might as well allow marginal heatmaps for all plot types (which is another possibility!)
   2. Possibly more important: Even if you do manually add fills to the density_contour plot, colorbars for histogram2dcontour traces (which is what density_contour uses under the hood) aren't compatible with heatmap colorbars. Heatmap colorbars are continuous, while histogram2dcontour colorbars are discrete and place labels at the contour boundaries. This results in weird visual artifacts and a misleading colorbar when using the PR implementation (see screenshot below), and I'm not sure if there's a way to reconcile the two (although maybe it's possible).

   Code:

   fig = px.density_contour(
   df,
   x="total_bill",y="tip",
   marginal_x="heatmap", marginal_y="heatmap"
   )
   fig.update_traces(
   selector=dict(type='histogram2dcontour'),
   contours_coloring="fill",
   )
   fig.show()
   Screenshot:

   For reference, here's what the `histogram2dcontour` colorbar looks like normally: The implementation for `density_heatmap`, on the other hand, looks pretty reasonable and I'd be happy to have it as a feature.

   Let us know your thoughts!

   i see the issue, i therefore removed support and now its only density heatmap that is supported.
7. lucasjamar commented on Aug 23, 2026

   

   lucasjamar

   on Aug 23, 2026

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   @lucasjamar I meant an image in the PR description to show the feature. That commit doesn't seem to have an image if you meant something else?

   done

   👍React with 👍1Reacted by Robert Claus
8. emilykl

   closed this as completedin #5707on Aug 27, 2026