go.Scatter ignores the masked array elements in numpy masked arrays
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Description
Bug summary
Plotting a numpy masked array, px.scatter plots the non-masked array elements and limits the axis accordingly, while go.Scatter also displays the masked data and does also include the masked elements for the axis limits. The behavior from go.Scatter is unexpected in this regard and ignoring the mask defeats the purpose of masked arrays.
Minimal viable code
import numpy.ma as ma
import plotly.express as px
import plotly.graph_objects as go
# Create timestamps
time_unmasked = [1,2,3,4,5,6,7]
# Create a masked array with three masked variables, one datapoint far from others
data_masked = ma.array([15,14,50,15,15,15,16], mask=[0,0,1,1,1,0,0])
fig_px = px.scatter(x=time_unmasked, y=data_masked, title='Plotly Express')
fig_px.show()
fig_go = go.Figure(layout=go.Layout(
title=go.layout.Title(text="Plotly Graph Objects")))
fig_go.add_trace(go.Scatter(x=time_unmasked, y=data_masked))
fig_go.show()
Thanks to @Benblob688 for his code example in the issue mentioned below.
Actual outcome
Using plotly.graph_objects, the masked data entries at position 2,3,4 with the values 50,15,15 are drawn into the plot and the axis limits are set accordingly.
Expected outcome
Using plotly.express, the masked data entries are not shown and the axis limits are set accordingly to only include non-masked entries in the array.
Additional information
Matplotlib had a similar issue here, where the code has been adapted from to display this issue here. It seems that the usage of np.column_stack caused an issue there, that function has since been replaced with np.ma.column_stack.
Operating system
Ubuntu 20.04
Plotly Version
5.14.1
Numpy Version
1.24.1
Python version
3.11.3
Installation
conda
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the minimal code example and compare px.scatter with go.Scatter when given the masked NumPy array. Trace how each entry point handles masked elements and axis limits, then add or update coverage for the reported example. Done means go.Scatter no longer draws masked values or includes them in the axis limits.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- data-visualization
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100