swap_dims() incorrectly changes underlying index name

[jaicher]

MCVE Code Sample

import xarray as xr

# create data array with named dimension and named coordinate
x = xr.DataArray([1], {"idx": [2], "y": ("idx", [3])}, ["idx"], name="x")

# what's our current index? (idx, this is fine)
x.indexes
# prints "idx: Int64Index([2], dtype='int64', name='idx')"

# swap dim so that y is our dimension, what's index now?
x.swap_dims({"idx": "y"}).indexes
# prints "y: Int64Index([3], dtype='int64', name='idx')"

The dimension name is appropriately swapped but the pandas index name is incorrect.

Expected Output

# swap dim so that y is our dimension, what's index now?
x.swap_dims({"idx": "y"}).indexes
# prints "y: Int64Index([3], dtype='int64', name='y')"

Problem Description

This is a problem because running x.swap_dims({"idx": "y"}).to_dataframe() gives
a dataframe with columns ["x", "idx"] and index "idx". This gives ambiguous names and drops the original name, while the DataArray string representation gives no indication that this might be happening.

Output of xr.show_versions()

# Paste the output here xr.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.8.1 | packaged by conda-forge | (default, Jan 29 2020, 15:06:10) [Clang 9.0.1 ] python-bits: 64 OS: Darwin OS-release: 18.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.3

xarray: 0.15.0
pandas: 0.25.3
numpy: 1.18.1
scipy: 1.4.1
netCDF4: 1.5.3
pydap: None
h5netcdf: None
h5py: 2.10.0
Nio: None
zarr: None
cftime: 1.0.4.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2.10.1
distributed: 2.10.0
matplotlib: None
cartopy: None
seaborn: None
numbagg: None
setuptools: 45.1.0.post20200119
pip: 20.0.2
conda: None
pytest: 5.3.5
IPython: 7.12.0
sphinx: None

[dcherian]

Thanks for reporting @jaicher