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
383844989,MDU6SXNzdWUzODM4NDQ5ODk=,2566,Unexpected warning when taking mean of all-NaN slice in chunked DataArray,30007270,closed,0,,,2,2018-11-23T14:33:00Z,2020-10-24T04:17:32Z,2020-10-24T04:17:32Z,NONE,,,,"#### Problem description
When taking the mean of a DataArray with an all-NaN axis, I get the following warning:
```
data = np.array([[np.nan,0],[np.nan,1]])
xc = xr.DataArray(data).chunk()
xc.mean('dim_0').compute()

/nbhome/xrc/anaconda2/envs/py361/lib/python3.6/site-packages/dask/array/numpy_compat.py:28: RuntimeWarning: invalid value encountered in true_divide
  x = np.divide(x1, x2, out)

<xarray.DataArray (dim_1: 2)>
array([ nan,  0.5])
Dimensions without coordinates: dim_1
```
This confused me because the warning suggests a [0/0 division](https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.geterr.html) and/or issues with typecasting. Furthermore, the warning is different when the data is not chunked:

```python


x = xr.DataArray(data)
x.mean('dim_0')

/nbhome/xrc/anaconda2/envs/py361/lib/python3.6/site-packages/xarray/core/nanops.py:161: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)

<xarray.DataArray (dim_1: 2)>
array([ nan,  0.5])
Dimensions without coordinates: dim_1

```
Also, using pure dask does not raise this warning.
```
xd = da.from_array(data,chunks=(1,1))

xd.mean(axis=0).compute()

array([ nan,  0.5])
```




#### Expected Output

Either the warning from a non-chunked DataArray or no warning would be preferred.

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

<details>
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-696.30.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
LOCALE: en_US.ISO8859-1

xarray: 0.11.0
pandas: 0.23.4
numpy: 1.13.1
scipy: 0.19.1
netCDF4: 1.3.1
h5netcdf: 0.5.0
h5py: 2.7.1
Nio: None
zarr: None
cftime: 1.0.2.1
PseudonetCDF: None
rasterio: None
iris: None
bottleneck: 1.2.0
cyordereddict: None
dask: 0.20.1
distributed: 1.24.0
matplotlib: 3.0.1
cartopy: 0.16.0
seaborn: 0.8.1
setuptools: 35.0.2
pip: 18.1
conda: None
pytest: 3.0.7
IPython: 6.5.0
sphinx: None

</details>
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