id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1197117301,I_kwDOAMm_X85HWo91,6456,Writing a a dataset to .zarr in a loop makes all the data NaNs,34276374,closed,0,,,11,2022-04-08T10:05:25Z,2023-10-14T20:30:49Z,2023-10-14T20:30:48Z,NONE,,,,"### What happened?

I have lots (61) pickled pandas dataframes that I'm trying to convert from pickle/pandas to zarr/xarray. Since the dataframes are large (10000x2048) I can't load them all into memory. To get around this I'm (MCVE below) looping through the pickle files, reading them into dataframes, constructing DataArrays and then Datasets from the data, concatinating the dataset with the previous dataset and updating the dataset to point to this new concatenated dataset.

Since I didn't want to use up too much memory, I'm also periodically writing the Dataset to .zarr in the loop and reopening it (hoping to make use of dask storing data on disk?).

When I do this however, the final dataset ends up being all NaNs.

### What did you expect to happen?

I expected the final dataset to contain all the concatenated data.

### Minimal Complete Verifiable Example

```Python
import pandas as pd
import numpy as np
import glob
import xarray as xr
from tqdm import tqdm

# Creating pkl files
[pd.DataFrame(np.random.randint(0,10, (1000,500))).astype(object).to_pickle('df{}.pkl'.format(i)) for i in range(4)]

fnames = glob.glob('*.pkl')

df = pd.read_pickle(fnames[0])
df.columns = np.arange(0,500).astype(object) # the real pkl files contain all objects
df.index = np.arange(0,1000).astype(object)
df = df.astype(np.float32)

ds = xr.DataArray(df.values, dims=['fname', 'res_dim'],
                  coords={'fname': df.index.values, 'res_dim': df.columns.values})
ds = ds.to_dataset(name='low_dim')

for idx, fname in enumerate(tqdm(fnames[1:])):
    df = pd.read_pickle(fname)
    df.columns = np.arange(0,500).astype(object)
    df.index = np.arange(0,1000).astype(object)
    df = df.astype(np.float32)


    ds2 = xr.DataArray(df.values, dims=['fname', 'res_dim'],
                  coords={'fname': df.index.values, 'res_dim': df.columns.values})
    ds2 = ds2.to_dataset(name='low_dim')

    ds = xr.concat([ds, ds2], dim='fname')
    ds['fname'] = ds.fname.astype(str)
    if (idx%2 == 0) & (idx !=0):
        ds.to_zarr('zarr_bug.zarr', mode='w')
        ds = xr.open_zarr('zarr_bug.zarr')

ds.to_zarr('zarr_bug.zarr', mode='w')
ds = xr.open_zarr('zarr_bug.zarr')

print(ds.low_dim.values)
```


### Relevant log output

```Python
[[nan nan nan ... nan nan nan]
 [nan nan nan ... nan nan nan]
 [nan nan nan ... nan nan nan]
 ...
 [nan nan nan ... nan nan nan]
 [nan nan nan ... nan nan nan]
 [nan nan nan ... nan nan nan]]
```


### Anything else we need to know?

If I get rid of the loop saving, everything works normally.

### Environment


INSTALLED VERSIONS
------------------
commit: None
python: 3.9.11 (main, Mar 28 2022, 10:10:35) 
[GCC 7.5.0]
python-bits: 64
OS: Linux
OS-release: 5.11.0-27-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.0
libnetcdf: 4.7.4

xarray: 2022.3.0
pandas: 1.4.1
numpy: 1.21.0
scipy: 1.8.0
netCDF4: 1.5.8
pydap: installed
h5netcdf: 1.0.0
h5py: 3.6.0
Nio: None
zarr: 2.11.1
cftime: 1.6.0
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.10
cfgrib: 0.9.10.1
iris: None
bottleneck: None
dask: 2022.03.0
distributed: 2022.3.0
matplotlib: 3.5.1
cartopy: None
seaborn: 0.11.2
numbagg: None
fsspec: 2022.02.0
cupy: None
pint: None
sparse: None
setuptools: 58.0.4
pip: 21.2.4
conda: None
pytest: None
IPython: 8.1.1
sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6456/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,not_planned,13221727,issue
1674532233,I_kwDOAMm_X85jz1WJ,7767,Inconsistency between xr.where() and da.where(),34276374,closed,0,,,6,2023-04-19T09:30:02Z,2023-09-20T19:25:58Z,2023-09-20T19:25:58Z,NONE,,,,"### What is your issue?

`xr.where()` and `da.where()` behave in seemingly opposite ways.

Example:

```python
da = xr.DataArray(np.arange(10)
print(xr.where(da < 5, 0, da).values)
print(da.where(da < 5, 0).values)
```
```
[0 0 0 0 0 5 6 7 8 9]
[0 1 2 3 4 0 0 0 0 0]
```

It seems like these two methods with the same name _should_ have the same functionality, but they give inverse results.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7767/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
1318369110,I_kwDOAMm_X85OlLdW,6828,xarray.DataArray.str.cat() doesn't work on chunked data,34276374,open,0,,,3,2022-07-26T14:58:16Z,2023-01-17T18:36:14Z,,NONE,,,,"### What happened?

I was trying to concatenate some DataArrays of strings, and it kept just returning the first DataArray without any changes.

### What did you expect to happen?

I was expecting it to just provide the strings, concatenated together with the spearator between them.

### Minimal Complete Verifiable Example

```Python
da = xr.DataArray(
    np.zeros((2, 2)).astype(str),
    coords={'x':np.arange(2), 'y': np.arange(2)},
    dims=['x', 'y'])

dac = da.chunk()

print((da == dac).values.all())
print((da.str.cat(da, sep='--') == dac.str.cat(dac, sep='--')).values.all())
print((da.str.cat(da, sep='--') == dac.compute().str.cat(dac.compute(), sep='--')).values.all())

>>> True
>>> False
>>> True
```


### 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](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), 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?

_No response_

### Environment

<details>


INSTALLED VERSIONS
------------------
commit: None
python: 3.8.10 (default, Mar 15 2022, 12:22:08) 
[GCC 9.4.0]
python-bits: 64
OS: Linux
OS-release: 5.11.0-27-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.2
libnetcdf: 4.9.0

xarray: 2022.3.0
pandas: 1.4.2
numpy: 1.22.4
scipy: 1.8.1
netCDF4: 1.6.0
pydap: None
h5netcdf: 1.0.1
h5py: 3.6.0
Nio: None
zarr: 2.11.3
cftime: 1.6.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.10
cfgrib: None
iris: None
bottleneck: None
dask: 2022.7.0
distributed: None
matplotlib: 3.5.2
cartopy: None
seaborn: None
numbagg: None
fsspec: 2022.5.0
cupy: None
pint: None
sparse: None
setuptools: 62.3.2
pip: 22.1.2
conda: None
pytest: None
IPython: 8.3.0
sphinx: None


</details>
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