issue_comments

I tested the method of adding a new level with mi.values, which worked, though x='x' must still be specified:

``` import numpy as np import pandas as pd import xarray as xr

data = np.random.rand(50,5)

x_idx = np.linspace(0, 50)

mi_idx1 = ['a','b','c','d','e'] mi_idx2 = [1,2,3,4,5]

mi = pd.MultiIndex.from_arrays([mi_idx1,mi_idx2], names=['mi_idx1', 'mi_idx2'])

coords = { 'x': x_idx, 'mi': mi }

da = xr.DataArray(data, coords=coords, dims = ['x', 'mi'])

da = da.assign_coords(mi_plot = ('mi', da.indexes['mi'].values)) # add a level for plot display

da.plot(hue='mi_plot', x='x') ```

It seems like this potentially happen behind the scenes, but I'm not sure.

I tested this (hue="mi_idx1") and it does not work.

I get the following error on 0.15.1:

ValueError: y must be a dimension name if x is not supplied

and on 0.16.1:

ValueError: ('mi', 'mi') must be a permuted list of ('x', 'mi'), unless...is included

(also, sorry, I accidentally clicked close and comment)

It makes sense to allow a MultiIndex as a label but probably not as a coordinate

To elaborate a little more on my particular use case, as it might give insight or an alternative solution: I often have time data taken under different experimental parameters, which are my coordinates. However, often the coordinate matrix is very sparse, meaning that my coordinate matrix might be 5x5x5, but I only have 10 data points or so somewhat randomly sampling this space. So being able to see all my 'test cases' with respect to hue/col etc is very useful to quickly examine the data and coordinate combinations, which helps once I want to unstack the array deal with all of the empty parameter space.