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
322849322,MDU6SXNzdWUzMjI4NDkzMjI=,2129,Using `DataArray.where()` with a DataArray as the condition drops the name,4849151,closed,0,,,4,2018-05-14T14:46:18Z,2020-04-14T08:54:33Z,2020-04-14T08:54:33Z,NONE,,,,"#### Code Sample, a copy-pastable example if possible

Create a boolean DataArray to use as a mask for another DataArray, with the same coordinates:

```python
da_1 = xr.DataArray(np.array([0.861185, 0.301491, 0.642744, 0.773298, 0.786516, 0.011693, 0.659326, 0.07877 , 0.108488, 0.747863]), coords=[('coord1', np.arange(10))], name='da1')
da_2 = xr.DataArray(np.array([0.116959, 0.955742, 0.121562, 0.635573, 0.304895, 0.064529, 0.042461, 0.41751 , 0.607457, 0.110104]), coords=[('coord1', np.arange(10))], name='da2')
da_3 = np.rint(da_2).astype('bool')
print(da_1)
<xarray.DataArray 'da1' (coord1: 10)>
array([0.861185, 0.301491, 0.642744, 0.773298, 0.786516, 0.011693, 0.659326,
       0.07877 , 0.108488, 0.747863])
Coordinates:
  * coord1   (coord1) int64 0 1 2 3 4 5 6 7 8 9
print(da_1.where(da_3))
<xarray.DataArray (coord1: 10)>
array([     nan, 0.301491,      nan, 0.773298,      nan,      nan,      nan,
            nan, 0.108488,      nan])
Coordinates:
  * coord1   (coord1) int64 0 1 2 3 4 5 6 7 8 9
```
#### Problem description
When using a DataArray (`da_3` in the example) as the condition in the `where()` method of another DataArray (`da_1`), the name of the DataArray being masked (`da_1`) is dropped.

#### Expected Output
The name should be retained.
```python
print(da_1.where(da_3))
<xarray.DataArray 'da_1` (coord1: 10)>
array([     nan, 0.301491,      nan, 0.773298,      nan,      nan,      nan,
            nan, 0.108488,      nan])
Coordinates:
  * coord1   (coord1) int64 0 1 2 3 4 5 6 7 8 9
```

#### Output of ``xr.show_versions()``

<details>

INSTALLED VERSIONS
------------------

commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

xarray: 0.10.3
pandas: 0.22.0
numpy: 1.14.3
scipy: 1.1.0
netCDF4: 1.3.2
h5netcdf: None
h5py: 2.7.1
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.17.4
distributed: 1.21.8
matplotlib: 2.2.2
cartopy: None
seaborn: 0.8.1
setuptools: 39.1.0
pip: 10.0.1
conda: 4.5.2
pytest: 3.5.1
IPython: 6.4.0
sphinx: 1.7.4


</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2129/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
201617371,MDU6SXNzdWUyMDE2MTczNzE=,1217,Using where() in datasets with dataarrays with different dimensions results in huge RAM consumption,4849151,closed,0,,,6,2017-01-18T16:09:50Z,2019-02-23T07:47:01Z,2019-02-23T07:47:01Z,NONE,,,,"I have a dataset containing groups of data with different dimensions. e.g.:

```Python
ds = xr.Dataset()
ds['data1'] = xr.DataArray(data1, coords={'t1': t1})
ds['data2'] = xr.DataArray(data2, coords={'t2': t2})
```

If I do something like ds.where(ds.data1 < 0.1), Python ends up allocating huge amounts of memory (>30GB for a dataset of <1MB) and seems to loop indefinitely, until the call is interrupted with CTRL-C.

To use where() successfully, I have to use a subset of the dataset with only one dimension.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1217/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue