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
304314787,MDU6SXNzdWUzMDQzMTQ3ODc=,1982,NetCDF coordinates in parent group is not used when reading sub group,4849151,open,0,,,10,2018-03-12T10:26:54Z,2021-12-27T18:19:22Z,,NONE,,,,"#### Code Sample, a copy-pastable example if possible

```python
ncfile_cf = ""x07z00017_cf.nc""
with xr.open_dataset(ncfile_cf, group=""x07"") as ds:
    ds_data_cf = ds.copy(deep=True)
print(ds_data_cf)
     
<xarray.Dataset>
Dimensions:  (time1: 100000, time2: 2)
Dimensions without coordinates: time1, time2
Data variables:
    aps      (time1) float64 ...
    iact     (time1) float64 ...
    vact     (time1) float64 ...
    dps      (time1) float64 ...
    tss      (time2) float64 ...


```
#### Problem description

When reading a sub group from a netCDF file with dimensions defined in the root group, the dimensions are not read from the root group. This contradicts the netCDF [documentation](https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_data_set_components.html), which states that dimensions are scoped such that they can be seen by all sub groups.

The attached netCDF file demonstrates this issue.
[x07z00017_cf.nc.zip](https://github.com/pydata/xarray/files/1802246/x07z00017_cf.nc.zip)



#### Expected Output

The dimensions from the root group should be used when reading the sub-group.

```python
with xr.open_dataset(ncfile_cf) as ds:
     for coord in ds.coords:
         ds_data_cf.coords[coord] = ds[coord]
print(ds_data_cf)

<xarray.Dataset>
Dimensions:  (time1: 100000, time2: 2)
Coordinates:
  * time1    (time1) float64 0.0 1e-06 2e-06 3e-06 4e-06 5e-06 6e-06 7e-06 ...
  * time2    (time2) float64 0.0 0.1
Data variables:
    aps      (time1) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 ...
    iact     (time1) float64 -0.00125 -0.000625 -0.00125 -0.0009375 ...
    vact     (time1) float64 -0.009375 -0.009375 -0.01875 -0.01875 -0.009375 ...
    dps      (time1) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    tss      (time2) float64 0.0 0.0
```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

xarray: 0.10.0
pandas: 0.22.0
numpy: 1.13.3
scipy: 1.0.0
netCDF4: 1.3.1
h5netcdf: None
Nio: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.15.3
matplotlib: 2.1.0
cartopy: None
seaborn: 0.8.0
setuptools: 38.5.1
pip: 9.0.1
conda: 4.4.11
pytest: 3.2.1
IPython: 6.2.1
sphinx: 1.6.3

</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1982/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
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
303809308,MDU6SXNzdWUzMDM4MDkzMDg=,1977,Netcdf char array not being decoded to string in compound dtype,4849151,open,0,,,3,2018-03-09T11:23:04Z,2020-02-14T13:26:11Z,,NONE,,,,"#### Code Sample, a copy-pastable example if possible

```python
script_nc_file = ""bolo_geom_fromscript.nc""
with xr.open_dataset(script_nc_file, group='/bolo/sxd', concat_characters=True) as ds:
    ds = ds.copy(deep=True)
print(ds.slits)
<xarray.DataArray 'slits' (slit_no: 4)>
array([ ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'1', b'', b'', b'', b'', b''], 1, (-0.06458486,  0.21803484, -0.97380162), ( 0.95881973,  0.28401534,  0.), (-0.52069675,  1.77104629, -1.564     ),  0.005,  0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'1', b'', b'', b'', b'', b''], 0),
       ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'2', b'', b'', b'', b'', b''], 1, (-0.16038567,  0.54145294, -0.82529095), ( 0.95881973,  0.28401534,  0.), (-0.5278879 ,  1.76891617, -1.564     ),  0.005,  0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'2', b'', b'', b'', b'', b''], 1),
       ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'3', b'', b'', b'', b'', b''], 1, (-0.26470454,  0.89362754, -0.36243804), ( 0.95881973,  0.28401534,  0.), (-0.31231469,  1.06756025, -1.57072314),  0.005,  0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'3', b'', b'', b'', b'', b''], 2),
       ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'4', b'', b'', b'', b'', b''], 1, (-0.19640032,  0.66303636,  0.72236396), ( 0.95881973,  0.28401534,  0.), (-0.31950584,  1.06543013, -1.57072314),  0.005,  0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'4', b'', b'', b'', b'', b''], 3)], 
      dtype={'names':['Object_type','ID','Version','basis_1','basis_2','centre_point','width','height','slit_id','slit_no'], 'formats':[('S1', (30,)),('S1', (30,)),'<i4',[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],'<f8','<f8',('S1', (30,)),'<i4'], 'offsets':[0,30,60,64,88,112,136,144,152,184], 'itemsize':192, 'aligned':True})
Coordinates:
  * slit_no  (slit_no) int64 0 1 2 3
Attributes:
    units: m
```
#### Problem description

When opening the attached dataset ,the char arrays in the compound dtype are not being converted into strings, despite `concat_characters=True`. 
[bolo_geom_fromscript.nc.zip](https://github.com/pydata/xarray/files/1796651/bolo_geom_fromscript.nc.zip)



#### Expected Output

The char arrays should be converted into strings (or at the very least, bytes if an encoding is not present):
```python
import netCDF4 as nc
longest_str = np.max([ds.slits.values[field].shape[-1] for field in ds.slits.values.dtype.fields 
                      if ds.slits.values[field].dtype.kind in ('S', 'U')])
str_dtype = '<U{}'.format(longest_str)
cartesian_coord = np.dtype([('x', np.float64), ('y', np.float64), ('z', np.float64)])
aperture_dtype_str = np.dtype([('Object_type', str_dtype), 
                               ('ID', str_dtype), ('Version', np.int32),
                               ('basis_1', cartesian_coord), ('basis_2', cartesian_coord), 
                               ('centre_point', cartesian_coord),
                               ('width', np.float64), ('height', np.float64),
                               ('slit_id', str_dtype), ('slit_no', np.int32)])
ds['slits_str'] = xr.DataArray(np.empty(ds.slits.size, aperture_dtype_str), 
                               coords=[('slit_no', ds.coords['slit_no'])])
for key in ds.slits.values.dtype.fields:
    if key in ('Object_type', 'ID', 'slit_id'):
        string_key = nc.chartostring(ds.slits.values[key])
        ds.slits_str.values[key] = string_key
    else:
        ds.slits_str.values[key] = ds.slits.values[key]
print(ds.slits_str)

<xarray.DataArray 'slits_str' (slit_no: 4)>
array([ ('BolometerSlit', 'MAST-U SXD - Outer Slit 1', 1, (-0.06458486,  0.21803484, -0.97380162), ( 0.95881973,  0.28401534,  0.), (-0.52069675,  1.77104629, -1.564     ),  0.005,  0.005, 'MAST-U SXD - Outer Slit 1', 0),
       ('BolometerSlit', 'MAST-U SXD - Outer Slit 2', 1, (-0.16038567,  0.54145294, -0.82529095), ( 0.95881973,  0.28401534,  0.), (-0.5278879 ,  1.76891617, -1.564     ),  0.005,  0.005, 'MAST-U SXD - Outer Slit 2', 1),
       ('BolometerSlit', 'MAST-U SXD - Upper Slit 3', 1, (-0.26470454,  0.89362754, -0.36243804), ( 0.95881973,  0.28401534,  0.), (-0.31231469,  1.06756025, -1.57072314),  0.005,  0.005, 'MAST-U SXD - Upper Slit 3', 2),
       ('BolometerSlit', 'MAST-U SXD - Upper Slit 4', 1, (-0.19640032,  0.66303636,  0.72236396), ( 0.95881973,  0.28401534,  0.), (-0.31950584,  1.06543013, -1.57072314),  0.005,  0.005, 'MAST-U SXD - Upper Slit 4', 3)], 
      dtype=[('Object_type', '<U30'), ('ID', '<U30'), ('Version', '<i4'), ('basis_1', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('basis_2', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('centre_point', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('width', '<f8'), ('height', '<f8'), ('slit_id', '<U30'), ('slit_no', '<i4')])
Coordinates:
  * slit_no  (slit_no) int64 0 1 2 3
```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

xarray: 0.10.0
pandas: 0.22.0
numpy: 1.13.3
scipy: 1.0.0
netCDF4: 1.3.1
h5netcdf: None
Nio: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.15.3
matplotlib: 2.1.0
cartopy: None
seaborn: 0.8.0
setuptools: 38.5.1
pip: 9.0.1
conda: 4.4.11
pytest: 3.2.1
IPython: 6.2.1
sphinx: 1.6.3


</details>
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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