Calling Dataset.from_dataframe with single-level MultiIndex incorrectly orders data

This might be a bit of a corner case - or misunderstanding of how to use the relevant methods - but it tripped me up when working with arbitrary-dimension dataframes/datasets and setting up a pandas.MultiIndex.from_product then calling to_xarray on the resultant dataframe.

The case here is one-dimensional data (i.e. a single-level MultiIndex) with out-of-order index/coordinates labels.

MCVE Code Sample

import xarray
import pandas

#
# Create a DataFrame with a single-level MultiIndex, where the labels are not in alphabetical
# order
#
index_multi = pandas.MultiIndex.from_product(
    [['b', 'a', 'c']], 
    names=['test_multi']
)
df_multi = pandas.DataFrame({'test': [1,2,3]}, index=index_multi)

print(df_multi)
#             test
# test_multi
# b              1
# a              2
# c              3

# Convert to Dataset
xr_multi = xarray.Dataset.from_dataframe(df_multi)

#
# The index values have been sorted, but the data values have not been matched
#
print(xr_multi)
# <xarray.Dataset>
# Dimensions:     (test_multi: 3)
# Coordinates:
#   * test_index  (test_multi) object 'a' 'b' 'c'
# Data variables:
#     test        (test_multi) int64 1 2 3

assert xr_multi.test.sel(test_multi='a').data == 2
assert xr_multi.test.sel(test_multi='b').data == 1
assert xr_multi.test.sel(test_multi='c').data == 3

Expected Output

I would expect the assertions to pass - either the coordinates labels not to be sorted, or the data to be reordered to match. Similar examples work fine with a simple Index or two-level MultiIndex:

#
# For reference, the desired behaviour with a simple Index
#
index_simple = pandas.Index(
    ['b', 'a', 'c'], 
    name='test_simple'
)
df_simple = pandas.DataFrame({'test': [1,2,3]}, index=index_simple)

print(df_simple)
#              test
# test_simple
# b               1
# a               2
# c               3

xr_simple = xarray.Dataset.from_dataframe(df_simple)
print(xr_simple)
# <xarray.Dataset>
# Dimensions:      (test_simple: 3)
# Coordinates:
#   * test_simple  (test_simple) object 'b' 'a' 'c'
# Data variables:
#     test         (test_simple) int64 1 2 3

assert xr_simple.test.sel(test_simple='a').data == 2
assert xr_simple.test.sel(test_simple='b').data == 1
assert xr_simple.test.sel(test_simple='c').data == 3


#
# For reference, the desired behavior with a two-level MultiIndex
#
index_multi2 = pandas.MultiIndex.from_tuples(
    [('b', 'b'), ('a', 'a'), ('c', 'c')], 
    names=['test_multi1', 'test_multi2']
)
df_multi2 = pandas.DataFrame({'test': [1,2,3]}, index=index_multi2)

print(df_multi2)
#                          test
# test_multi1 test_multi2
# b           b               1
# a           a               2
# c           c               3

# Convert to Dataset
xr_multi2 = xarray.Dataset.from_dataframe(df_multi2)

#
# The index values have been sorted, and data is reordered and filled out with nans
#
print(xr_multi2)
# <xarray.Dataset>
# Dimensions:      (test_multi1: 3, test_multi2: 3)
# Coordinates:
#   * test_multi1  (test_multi1) object 'a' 'b' 'c'
#   * test_multi2  (test_multi2) object 'a' 'b' 'c'
# Data variables:
#     test         (test_multi1, test_multi2) float64 2.0 nan nan ... nan nan 3.0


assert xr_multi2.test.sel(test_multi1='a', test_multi2='a').data == 2
assert xr_multi2.test.sel(test_multi1='b', test_multi2='b').data == 1
assert xr_multi2.test.sel(test_multi1='c', test_multi2='c').data == 3

Problem Description

Creating a Dataset from a DataFrame with a single-level MultiIndex (where the labels are not in alphabetical order) results in the index/coordinates labels being sorted, but the data values are not reordered to match.

Output of xr.show_versions()

Start at the Dataset.from_dataframe entry point and reproduce the MCVE using pandas.MultiIndex.from_product with the labels b, a, and c. Compare the resulting coordinate order and data values with the assertions in the issue; done means the single-level MultiIndex preserves matching labels and values, while the simple Index and two-level MultiIndex behavior remains correct.