Histogram plot of DataArray can be extremely slow

[maahn]

The speed of plotting a histogram of a large DataArray depends a lot how you do it:

import xarray as xr
import numpy as np
import matplotlib.pyplot as plt

nPoints = 100000
data = xr.DataArray(np.random.random(nPoints),dims=['time'],coords=[np.arange(nPoints)])

It take sonly some ms if you use

plt.figure()
%time data.plot.hist()

plt.figure()
%time plt.hist(data.values)

However, if you omit .values it takes extremely long:

In [12]: %time plt.hist(data)
CPU times: user 2min, sys: 9.73 s, total: 2min 9s
Wall time: 2min 3s

Do if one forgets to add .values or to use xarray's plot routine, one can be stuck for a long time. I have to kill python 1-2 per day due to that issue.

[shoyer]

This might be something worth reporting on the matplotlib side. I don't know how what's going on internally when you try to histrogram a DataArray directly. Profiling (e.g., with %prun in IPython) would probably give some useful clues about what's going on.

[maahn]

OK, I reported it to the matplotlib team:
matplotlib/matplotlib#6804

[shoyer]

This has been fixed upstream