issues

What happened?

I dropped an index on one coord, then later called assign_coords to change another unrelated coord. I expected the index on the original coord to stay dropped.

What did you expect to happen?

The index was silently created again.

Minimal Complete Verifiable Example

Python import xarray import numpy as np ds = xarray.Dataset( {'foo': (('x','y'), np.ones((3,5)))}, coords={'x': [1,2,3], 'y': [4,5,6,7,8]}) ds = ds.drop_indexes('x') assert 'x' not in ds.indexes ds = ds.assign_coords(y=ds.y+1) assert 'x' not in ds.indexes # Fails

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

In general it would be nice if xarray made it easier to avoid indexes being automatically created.

E.g. right now, as far as I can tell there's no way to avoid an index being created when you construct a DataArray or Dataset with a coordinate of the same name as a dimension.

Admittedly I have a slightly niche use case -- I'm using xarray with wrapped JAX arrays, which can't be converted into pandas indexes. Indexes being (re-)created in these cases isn't just an inconvenience it actually causes a crash.

Environment

What happened?

When doing a faceted plot and specifying a projection (via e.g. subplot_kws=dict(projection=ccrs.PlateCarree())), the padding becomes very weird (often unusable) and strangely unstable to changes in size and aspect. For example this produces very bad results:

python data = xarray.DataArray( dims=('lat', 'lon', 'row', 'col'), data=np.ones((180, 360, 2, 2)), coords={'lon': np.arange(360), 'lat': np.arange(-90, 90)} ) xarray.plot.pcolormesh( data, row='row', col='col', size=5, aspect=1.5, subplot_kws=dict(projection=ccrs.PlateCarree()), )

whereas if you change size from 5 to 4, you suddenly get a much better (although still not quite right) layout:

What did you expect to happen?

I expected a layout closer to what you get if you comment out subplot_kws=dict(projection=ccrs.PlateCarree()), above:

Minimal Complete Verifiable Example

```Python import xarray import cartopy.crs as ccrs import numpy as np

data = xarray.DataArray( dims=('lat', 'lon', 'row', 'col'), data=np.ones((180, 360, 2, 2)), coords={'lon': np.arange(360), 'lat': np.arange(-90, 90)} ) xarray.plot.pcolormesh( data, row='row', col='col', size=5, aspect=1.5, subplot_kws=dict(projection=ccrs.PlateCarree()), ) ```

Relevant log output

In the case where the layout isn't (as) broken, I see a warning:

.../xarray/plot/facetgrid.py:394: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations

It seems that when tight_layout does manage to get applied, things really go wrong. Perhaps at a minimum we could have a way to disable tight_layout? (which currently seems to be mandatory)

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.7.10 (stable, redacted, redacted) [Clang google3-trunk (e5b1b9edb8b6f6cd926c2ba3e1ad1b6f767021d6)] python-bits: 64 OS: Linux OS-release: 4.15.0-smp-920.39.0.0 machine: x86_64 processor: byteorder: little LC_ALL: en_US.UTF-8 LANG: None LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.1 libnetcdf: 4.6.1

xarray: 0.18.2 pandas: 1.1.5 numpy: 1.21.5 scipy: 1.2.1 netCDF4: 1.4.1 pydap: None h5netcdf: 0.11.0 h5py: 3.2.1 Nio: None zarr: 2.7.0 cftime: 1.0.0 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: 3.3.4 cartopy: 0+unknown seaborn: 0.11.1 numbagg: None pint: None setuptools: unknown pip: None conda: None pytest: None IPython: 3.2.3 sphinx: None