matplotlib Slow 3D scatter rotation

Question

I am using matplotlib to scatter plot a 3D matrix of points. I am using the following code:

import pylab as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

my_data = np.random.rand(6500,3)  # toy 3D points

fig = plt.figure()
ax = Axes3D(plt.gcf())
ax.scatter(my_data[:,0],my_data[:,1],my_data[:,2])
plt.show()

It works, so it opens a window where I can see my points.

However if I try to rotate the plot with the mouse (clicking and dragging it) it rotates REALLY slow.

I think 6500 points are not a lot for such a slow and laggy rotation, so I'm wondering if there is any pre-configuration to be done to speed it up.

Note: I tried to use Matlab and I can rotate a way bigger scatter plot without any lag, so it's not a computer limitation.

Can someone run this code and see if also experiences the slow rotation?

EDIT: Using the System monitor I can see that when rotating the points, only one CPU is used, so matplotlib is not parallelizing the process.

My computer specs:

• Intel(R) Core(TM) i5-3550 CPU @ 3.30GHz
• Graphic Card: Xeon E3-1200 v2/3rd Gen Core processor Graphics Controller
• Memory: 10GB RAM
• HDD: Samsung SSD 500 GB

running Ubuntu 16.10

Answer 1

(revised 2021)

Matplotlib was not really designed to be interactive. Plotly (among a few others) is a plotting package that is rather feature complete and uses a webGL backend for scatter3D that will render in your browser (and is blazing fast). More on why you might want to consider it as a go-to replacement at the bottom:

# pip install plotly
import numpy as np
import plotly.graph_objects as go

my_data = np.random.rand(6500,3)  # toy 3D points
marker_data = go.Scatter3d(
    x=my_data[:,0], 
    y=my_data[:,1], 
    z=my_data[:,2], 
    marker=go.scatter3d.Marker(size=3), 
    opacity=0.8, 
    mode='markers'
)
fig=go.Figure(data=marker_data)
fig.show()

I use plotly as a replacement for matplotlib because:

• it has great coverage (bar, scatter, line, hist, etc.)
• intuitive interactive components that are highly configurable
• portability: it can generate html plots which are easily viewed across a network(!) or saved as static file formats
• it has a clearer api than matplotlib (for full config, jsonish)
• it integrates strongly with pandas (you can use it as the backend)
• it is well supported and adoption is increasing