Is your feature request related to a problem?

We have several tests that write and/or read test datasets using a common name, using open_zarr and to_zarr. Run individually, they work. However, when (e.g.) writing twice, we have errors as the zarr storage is trying to check to see which variables we may have updated, when in fact it is a new dataset.

Describe the solution you'd like

We would like a method to ensure there is no cached state between tests.

Describe alternatives you've considered

If there is an undocumented way to clear cached state, would be grateful for a pointer (and suggest inclusion in the documentation).

Additional context

No response

What happened?

I have xarray, zarr installed, but not dask, and am trying to call to_zarr in an async routine. I am looking for something I can await. The doc claims that a Dask.Delayed is returned. I understand that if I have a dask client open with asynchronous=True I can await the result.

However, not using Dask. Is there some way to get an awaitable from this object without a dask client?

What did you expect to happen?

I should get something back I can await in my async routine.

Minimal Complete Verifiable Example

```Python import xarray as xr from asyncio import get_event_loop

ds = xr.Dataset(data_vars = dict(x = ('x', [1, 2]))) deld = ds.to_zarr("bar.zarr", compute=False) loop.run_until_complete(deld. ...?) ```

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.8.5 (default, Sep 4 2020, 02:22:02) [Clang 10.0.0 ] python-bits: 64 OS: Darwin OS-release: 20.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.10.4 libnetcdf: 4.7.3

xarray: 2022.3.0 pandas: 1.4.1 numpy: 1.22.3 scipy: 1.6.2 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.11.1 cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.10.0 distributed: 2021.10.0 matplotlib: 3.1.3 cartopy: None seaborn: None numbagg: None fsspec: 2022.02.0 cupy: None pint: None sparse: None setuptools: 60.7.1 pip: 22.0.3 conda: 4.11.0 pytest: 7.1.1 IPython: 7.31.1 sphinx: None

What happened:

I have an dataarray with dtype np.bool_. When I write it using netcdf (engine h5netcdf, or default) and then read in a copy, the copy has dtype int8.

What you expected to happen:

The loaded data array should have dtype bool

Minimal Complete Verifiable Example:

I have had a hard time reducing this to a sample. The data array comes from a larger dataset which exhibits the same problem. I can copy the dataarray using copy() and it still exhibits the problem; however if I build a new data array using the constructor, the new array doesn't exhibit the problem. As far as I can tell, though, the original and the rebuilt dataarray are otherwise identical.

```python

in a pdb session

(Pdb) ci <xarray.DataArray 'cut_inclusive' (cut: 15)> array([False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]) Dimensions without coordinates: cut (Pdb) ci.to_netcdf('foo_ci.nca', engine="h5netcdf") (Pdb) csi = xr.read_dataarray('foo_ci_nca', engine="h5netcdf"); csi <xarray.DataArray 'cut_inclusive' (cut: 15)> array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8) Dimensions without coordinates: cut (Pdb) ci2 = xr.DataArray(ci, dims=('cut', )) (Pdb) ci2.equals(ci) True (Pdb) ci2.to_netcdf('foo_ci2.nca', engine="h5netcdf") (Pdb) csi2 = xr.open_dataarray('foo_ci2.nca', engine="h5netcdf"); csi2 <xarray.DataArray 'cut_inclusive' (cut: 15)> array([False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]) Dimensions without coordinates: cut (Pdb) ci3 = ci.copy() (Pdb) ci3.to_netcdf('foo_ci3.nca', engine="h5netcdf") (Pdb) csi3 = xr.open_dataarray('foo_ci3.nca', engine="h5netcdf"); csi3 <xarray.DataArray 'cut_inclusive' (cut: 15)> array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8) Dimensions without coordinates: cut ``` Anything else we need to know?:

I am at a loss how to investigate why ci and ci3 don't survive round-trip, but ci2 does. Unfortunately, I also have been unable to produce a free-standing example -- whenever I try I get an object that survives round trip intact. I suspect that xarray internals is somewhere/somehow keeping a cache to the original ci (presumably still linked to the overall dataset from which ci came), and this is what is causing the problem, but I don't know where to look. (Suggestions welcome!)

Environment:

Is your feature request related to a problem? Please describe.

I am trying to drop particular indices from a dimension that doesn't have coordinates.

Following: drop_sel() documentation, but leaving out the coordinate labels: python data = np.random.randn(2, 3) ds = xr.Dataset({"A": (["x", "y"], data)}) ds.drop_sel(y=[1]) gives me an error.

Describe the solution you'd like

I would think drop_isel should exist and work in analogy to drop_sel as isel does to sel.

Describe alternatives you've considered

As far as I know, I could either create coordinates especially to in order to drop, or rebuild a new dataset. Both are not congenial. (I'd be grateful to know if there is actually a straightforward way to do this I've overlooked.