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
755105132,MDU6SXNzdWU3NTUxMDUxMzI=,4641,Wrong hue assignment in scatter plot,42246615,closed,0,,,7,2020-12-02T09:34:40Z,2021-01-13T23:02:33Z,2021-01-13T23:02:33Z,NONE,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

Please be concise with code posted. See guidelines below on how to provide a good bug report:

- Craft Minimal Bug Reports: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples: https://stackoverflow.com/help/mcve

Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->

**What happened**:
When using the hue keyword in a scatter plot to color the points based on a string variable, the color assignment in the plot is wrong (whereas the legend is correct).

**What you expected to happen**:
In the example, data of category `""A""` ranges between 0 and 2 in u-direction and 0 and 0.5 in v-direction. Points in that square should be orange (the color for ""A"") but currently are blue.

**Minimal Complete Verifiable Example**:

```python
import xarray as xr
import numpy as np

u = np.random.rand(50, 2) * np.array([1, 2])
v = np.random.rand(50, 2) * np.array([1, 0.5])

ds = xr.Dataset(
    {
        ""u"": ((""x"", ""category""), u),
        ""v"": ((""x"", ""category""), v),
    },
    coords={""category"": [""B"", ""A""],}
)

g = ds.plot.scatter(
    y=""u"",
    x=""v"",
    hue=""category"",
);
```

**Anything else we need to know?**:
I think that this might be related to sorting at some point. If the variable by which I color is sorted alphabetically (`[""A"", ""B""]` instead of `[""B"", ""A""]`), the color assignment is correct.

Not sure if this issue is related to https://github.com/pydata/xarray/issues/4126, bit it looks different to me (the problem is not the legend, but the colors in the plot itself).

**Environment**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

<!-- Paste the output here xr.show_versions() here -->
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 02:25:08) 
[GCC 7.5.0]
python-bits: 64
OS: Linux
OS-release: 4.15.0-122-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.6
libnetcdf: 4.7.4

xarray: 0.16.0
pandas: 1.1.2
numpy: 1.17.5
scipy: 1.5.2
netCDF4: 1.5.4
pydap: None
h5netcdf: None
h5py: 2.10.0
Nio: None
zarr: 2.4.0
cftime: 1.2.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2.26.0
distributed: 2.26.0
matplotlib: 3.3.2
cartopy: None
seaborn: 0.11.0
numbagg: None
pint: None
setuptools: 49.6.0.post20200814
pip: 20.2.3
conda: 4.8.3
pytest: 6.0.1
IPython: 7.18.1
sphinx: None


</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4641/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
352909556,MDU6SXNzdWUzNTI5MDk1NTY=,2376,File written by to_netcdf() not closed when Dataset is generated from dask delayed object using a dask Client(),42246615,closed,0,,,2,2018-08-22T11:21:05Z,2018-10-09T04:13:41Z,2018-10-09T04:13:41Z,NONE,,,,"#### Code Sample
```python
import numpy as np
import xarray as xr
import dask.array as da
import dask
from dask.distributed import Client

@dask.delayed
def run_sim(n_time):
    result = np.array([np.random.randn(n_time)])
    return result

client = Client()

# Parameters
n_sims = 5
n_time = 100
output_file = 'out.nc'

# if I use this as output, computing the data after reopening the file 
#produces an error
out = da.stack([da.from_delayed(run_sim(n_time), (1,n_time,),np.float64) for i in range(n_sims)])

# If I use this as output, reopening the netcdf file is no problem
#out = np.random.randn(n_sims,2,n_time) 

ds = xr.Dataset({'var1': (['realization', 'time'], out[:,0,:])},
                 coords={'realization': np.arange(n_sims),
                         'time': np.arange(n_time)*.1})

# Save to a netcdf file -> at this point, computations will be carried out
ds.to_netcdf(output_file, engine='netcdf4')

# Reopen the file
with xr.open_dataset(output_file, chunks={'realization': 2}, engine='netcdf4')as ds:
    # Now acces the data
    ds.compute()

```
#### Problem description

When I generate a Dataset using a dask delayed object and save the Dataset to a netcdf file, it seems that the file is not properly closed. When trying to reopen it, I get an error (see below). Also, `ncdump -h` fails on the output file after it has been written. However, after the first unsuccessful attempt to open the file, the file seems to be closed. I can run `ncdump -h`  on it and a second attempt to open it works.

Note that the problem  _only_ arises if I

- store output form a dask delayed object in the Dataset (not if I store a simple numpy array of random numbers)
- start a dask.distributed.Client() 

This issue is related to my question on [stackoverflow](https://stackoverflow.com/questions/51930488/problems-reopening-netcdf-file-written-with-xarray-dask/51959512#51959512).

Traceback of the python code:

```python-traceback

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-2-83478559c186> in <module>()
     36 with xr.open_dataset(output_file, chunks={'realization': 2}, engine='netcdf4')as ds:
     37     # Now acces the data
---> 38     ds.compute()

~/miniconda3/lib/python3.6/site-packages/xarray/core/dataset.py in compute(self, **kwargs)
    592         """"""
    593         new = self.copy(deep=False)
--> 594         return new.load(**kwargs)
    595 
    596     def _persist_inplace(self, **kwargs):

~/miniconda3/lib/python3.6/site-packages/xarray/core/dataset.py in load(self, **kwargs)
    489 
    490             # evaluate all the dask arrays simultaneously
--> 491             evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    492 
    493             for k, data in zip(lazy_data, evaluated_data):

~/miniconda3/lib/python3.6/site-packages/dask/base.py in compute(*args, **kwargs)
    400     keys = [x.__dask_keys__() for x in collections]
    401     postcomputes = [x.__dask_postcompute__() for x in collections]
--> 402     results = schedule(dsk, keys, **kwargs)
    403     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    404 

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in get(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, **kwargs)
   2191             try:
   2192                 results = self.gather(packed, asynchronous=asynchronous,
-> 2193                                       direct=direct)
   2194             finally:
   2195                 for f in futures.values():

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in gather(self, futures, errors, maxsize, direct, asynchronous)
   1566             return self.sync(self._gather, futures, errors=errors,
   1567                              direct=direct, local_worker=local_worker,
-> 1568                              asynchronous=asynchronous)
   1569 
   1570     @gen.coroutine

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in sync(self, func, *args, **kwargs)
    651             return future
    652         else:
--> 653             return sync(self.loop, func, *args, **kwargs)
    654 
    655     def __repr__(self):

~/miniconda3/lib/python3.6/site-packages/distributed/utils.py in sync(loop, func, *args, **kwargs)
    275             e.wait(10)
    276     if error[0]:
--> 277         six.reraise(*error[0])
    278     else:
    279         return result[0]

~/miniconda3/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

~/miniconda3/lib/python3.6/site-packages/distributed/utils.py in f()
    260             if timeout is not None:
    261                 future = gen.with_timeout(timedelta(seconds=timeout), future)
--> 262             result[0] = yield future
    263         except Exception as exc:
    264             error[0] = sys.exc_info()

~/miniconda3/lib/python3.6/site-packages/tornado/gen.py in run(self)
   1131 
   1132                     try:
-> 1133                         value = future.result()
   1134                     except Exception:
   1135                         self.had_exception = True

~/miniconda3/lib/python3.6/site-packages/tornado/gen.py in run(self)
   1139                     if exc_info is not None:
   1140                         try:
-> 1141                             yielded = self.gen.throw(*exc_info)
   1142                         finally:
   1143                             # Break up a reference to itself

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in _gather(self, futures, errors, direct, local_worker)
   1445                             six.reraise(type(exception),
   1446                                         exception,
-> 1447                                         traceback)
   1448                     if errors == 'skip':
   1449                         bad_keys.add(key)

~/miniconda3/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    690                 value = tp()
    691             if value.__traceback__ is not tb:
--> 692                 raise value.with_traceback(tb)
    693             raise value
    694         finally:

~/miniconda3/lib/python3.6/site-packages/dask/array/core.py in getter()
     87         c = a[b]
     88         if asarray:
---> 89             c = np.asarray(c)
     90     finally:
     91         if lock:

~/miniconda3/lib/python3.6/site-packages/numpy/core/numeric.py in asarray()
    490 
    491     """"""
--> 492     return array(a, dtype, copy=False, order=order)
    493 
    494 

~/miniconda3/lib/python3.6/site-packages/xarray/core/indexing.py in __array__()
    600 
    601     def __array__(self, dtype=None):
--> 602         return np.asarray(self.array, dtype=dtype)
    603 
    604     def __getitem__(self, key):

~/miniconda3/lib/python3.6/site-packages/numpy/core/numeric.py in asarray()
    490 
    491     """"""
--> 492     return array(a, dtype, copy=False, order=order)
    493 
    494 

~/miniconda3/lib/python3.6/site-packages/xarray/core/indexing.py in __array__()
    506     def __array__(self, dtype=None):
    507         array = as_indexable(self.array)
--> 508         return np.asarray(array[self.key], dtype=None)
    509 
    510     def transpose(self, order):

~/miniconda3/lib/python3.6/site-packages/xarray/backends/netCDF4_.py in __getitem__()
     62             getitem = operator.getitem
     63 
---> 64         with self.datastore.ensure_open(autoclose=True):
     65             try:
     66                 array = getitem(self.get_array(), key.tuple)

~/miniconda3/lib/python3.6/contextlib.py in __enter__()
     79     def __enter__(self):
     80         try:
---> 81             return next(self.gen)
     82         except StopIteration:
     83             raise RuntimeError(""generator didn't yield"") from None

~/miniconda3/lib/python3.6/site-packages/xarray/backends/common.py in ensure_open()
    502         if not self._isopen:
    503             try:
--> 504                 self._ds = self._opener()
    505                 self._isopen = True
    506                 yield

~/miniconda3/lib/python3.6/site-packages/xarray/backends/netCDF4_.py in _open_netcdf4_group()
    229     import netCDF4 as nc4
    230 
--> 231     ds = nc4.Dataset(filename, mode=mode, **kwargs)
    232 
    233     with close_on_error(ds):

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.__init__()

netCDF4/_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()

OSError: [Errno -101] NetCDF: HDF error: b'/home/user/code/test/out.nc'

```
Output of `ncdump -h` after writing the file (before reopening):

```
HDF5-DIAG: Error detected in HDF5 (1.10.2) thread 139952254916352:
  #000: H5F.c line 511 in H5Fopen(): unable to open file
    major: File accessibilty
    minor: Unable to open file
  #001: H5Fint.c line 1519 in H5F_open(): unable to lock the file
    major: File accessibilty
    minor: Unable to open file
  #002: H5FD.c line 1650 in H5FD_lock(): driver lock request failed
    major: Virtual File Layer
    minor: Can't update object
  #003: H5FDsec2.c line 941 in H5FD_sec2_lock(): unable to lock file, errno = 11, error message = 'Resource temporarily unavailable'
    major: File accessibilty
    minor: Bad file ID accessed
ncdump: out.nc: NetCDF: HDF error

```
#### Expected Output
The netcdf-file is closed after writing it with to_netcdf().

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-133-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

xarray: 0.10.8
pandas: 0.23.3
numpy: 1.14.5
scipy: 1.1.0
netCDF4: 1.4.1
h5netcdf: 0.6.2
h5py: 2.8.0
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.18.2
distributed: 1.22.1
matplotlib: 2.2.2
cartopy: None
seaborn: 0.9.0
setuptools: 40.0.0
pip: 18.0
conda: 4.5.10
pytest: 3.6.4
IPython: 6.5.0
sphinx: 1.7.5
</details>
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