to_xarray() result is incorrect when one of multi-index levels is not sorted #4186
Description
What happened:
to_xarray() sorts multi-index level values and returns result for the sorted values but it doesn't sort levels or expects levels to be sorted resulting in completely incorrect order of data for the displayed coordinates.
df:
C1 C2
lev1 lev2
b foo 0 1
a foo 2 3
df.to_xarray():
<xarray.Dataset>
Dimensions: (lev1: 2, lev2: 1)
Coordinates:
* lev1 (lev1) object 'b' 'a'
* lev2 (lev2) object 'foo'
Data variables:
C1 (lev1, lev2) int64 2 0
C2 (lev1, lev2) int64 3 1 What you expected to happen:
Should account for the order of levels in the original index.
Minimal Complete Verifiable Example:
import pandas as pd
df = pd.concat(
{
'b': pd.DataFrame([[0, 1]], index=['foo'], columns=['C1', 'C2']),
'a': pd.DataFrame([[2, 3]], index=['foo'], columns=['C1', 'C2']),
}
).rename_axis(['lev1', 'lev2'])
print('df:\n', df, '\n')
print('df.to_xarray():\n', df.to_xarray(), '\n')
print('df.index.levels[0]:\n', df.index.levels[0])Anything else we need to know?:
Environment:
Output of xr.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.8.2 | packaged by conda-forge | (default, Apr 24 2020, 08:20:52)
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 5.4.7-100.fc30.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.3
xarray: 0.15.1
pandas: 1.0.5
numpy: 1.19.0
scipy: 1.5.0
netCDF4: 1.5.3
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.1.3
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2.19.0
distributed: 2.19.0
matplotlib: 3.2.2
cartopy: None
seaborn: 0.10.1
numbagg: installed
setuptools: 46.3.0.post20200513
pip: 20.1
conda: None
pytest: 5.4.3
IPython: 7.15.0
sphinx: None