Setting tick colors of matplotlib 3D plot

Question

If I have a 3D matplotlib plot (Axes3D object), how do I change the color of the tick marks? I figured out how to change the color of the axis line, the tick labels, and the axis label. The obvious solution, use ax.tick_params(axis='x', colors='red'), only changes the tick labels not the tick marks themselves.

Here is code that tries to change all the axes to red and gets everything but the tick marks:

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

fig = plt.figure()
ax = Axes3D(fig)

ax.scatter((0, 0, 1), (0, 1, 0), (1, 0, 0))
ax.w_xaxis.line.set_color('red')
ax.w_yaxis.line.set_color('red')
ax.w_zaxis.line.set_color('red')
ax.w_zaxis.line.set_color('red')
ax.xaxis.label.set_color('red')
ax.yaxis.label.set_color('red')
ax.zaxis.label.set_color('red')
ax.tick_params(axis='x', colors='red')  # only affects
ax.tick_params(axis='y', colors='red')  # tick labels
ax.tick_params(axis='z', colors='red')  # not tick marks

fig.show()

Answer 1

As mentioned in manual page about tick_params(axis='both', **kwargs) you get a bug:

While this function is currently implemented, the core part of the Axes3D object may ignore some of these settings. Future releases will fix this. Priority will be given to those who file bugs.

To override this problem use inner _axinfo dictionary as in this example:

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

fig = plt.figure()
ax = fig.gca(projection='3d')

ax.scatter((0, 0, 1), (0, 1, 0), (1, 0, 0))

ax.xaxis._axinfo['tick']['color']='r'
ax.yaxis._axinfo['tick']['color']='r'
ax.zaxis._axinfo['tick']['color']='r'
plt.show()

Answer 2

A straightforward way to achieve the expected result is as follows:

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

rcParams['xtick.color'] = 'red'
rcParams['ytick.color'] = 'red'
rcParams['axes.labelcolor'] = 'red'
rcParams['axes.edgecolor'] = 'red'

fig = plt.figure()
ax = Axes3D(fig)

ax.scatter((0, 0, 1), (0, 1, 0), (1, 0, 0))
plt.show()

The output display is: