# Matplotlib 3D Scatter Plot with Colorbar

Each Answer to this Q is separated by one/two green lines.

Borrowing from the example on the Matplotlib documentation page and slightly modifying the code,

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

def randrange(n, vmin, vmax):
return (vmax-vmin)*np.random.rand(n) + vmin

fig = plt.figure()
n = 100
for c, m, zl, zh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
xs = randrange(n, 23, 32)
ys = randrange(n, 0, 100)
zs = randrange(n, zl, zh)
cs = randrange(n, 0, 100)
ax.scatter(xs, ys, zs, c=cs, marker=m)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

plt.show()


Will give a 3D scatter plot with different colors for each point (random colors in this example). What’s the correct way to add a colorbar to the figure, since adding in plt.colorbar() or ax.colorbar() doesn’t seem to work.

This produces a colorbar (though possibly not the one you need):

Replace this line:

ax.scatter(xs, ys, zs, c=cs, marker=m)


with

p = ax.scatter(xs, ys, zs, c=cs, marker=m)


then use

fig.colorbar(p)


near the end

Using the above answer did not solve my problem. The colorbar colormap was not linked to the axes (note also the incorrect colorbar limits):

from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

data = np.random.rand(3, 100)
x, y, z = data  # for show
c = np.arange(len(x)) / len(x)  # create some colours

p = ax.scatter(x, y, z, c=plt.cm.magma(0.5*c))
ax.set_xlabel('$\psi_1$')
ax.set_ylabel('$\Phi$')
ax.set_zlabel('$\psi_2$')

ax.set_box_aspect([np.ptp(i) for i in data])  # equal aspect ratio

fig.colorbar(p, ax=ax)


The solution (see here also) is to use cmap in ax.scatter:

from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

data = np.random.rand(3, 100)
x, y, z = data  # for show
c = np.arange(len(x)) / len(x)  # create some colours

p = ax.scatter(x, y, z, c=0.5*c, cmap=plt.cm.magma)
ax.set_xlabel('$\psi_1$')
ax.set_ylabel('$\Phi$')
ax.set_zlabel('$\psi_2$')

ax.set_box_aspect([np.ptp(i) for i in data])  # equal aspect ratio

fig.colorbar(p, ax=ax)


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