Can you reproduce a self contained example here?

In general matplotlib does not do automatic conversion on the data that is run through a norm so expect to jump through some hoops to get a colorbar with dates on it.

sure, here it is:

import numpy as np
import matplotlib.pyplot as plt

dates = np.datetime64('2019-11') + np.arange(10)*np.timedelta64(1, 'D')
X= np.random.randn(10, 2)

plt.scatter(X[:, 0], X[:, 1], c= dates)
plt.colorbar()
plt.show()

On the other hand, if I use the dates for some axis, they get printed just as dates:

plt.scatter(X[:, 0], dates)
plt.show()

Well actually that scale could be improved, this is another problem, but fact is I think there is an inconsistency here between how dates are treated to make scales in axes, and to make scales on colorbars. Dates are not readable anymore when turned into numbers, and a scale is useless if not readable.

The following is basically what is done for the x and y axis if the numpy array is a date. Adding the same support to the third dimension is really hard - in general, colormaps and colorbars are also used for images, pcolors, and contours, and the idea of those having convertible units is non-trivial.

Here, you just convert the dates to floats and then set the proper formatters and locators:

dates = np.datetime64('2019-11-01') + np.arange(10)*np.timedelta64(1, 'D')
X= np.random.randn(10, 2)

plt.scatter(X[:, 0], X[:, 1], c=mdates.date2num(dates))
cb = plt.colorbar()
loc = mdates.AutoDateLocator()
cb.ax.yaxis.set_major_locator(loc)
cb.ax.yaxis.set_major_formatter(mdates.ConciseDateFormatter(loc))
plt.show()

and the idea of those having convertible units is non-trivial.

Yes/No - I think you might be able to get it by putting a convertor on NoNorm.

Its not the conversion thats particularly the issue, its getting the unit info down to the colorbar.

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