issues: 587062505

Background: In data analyses, it is a common occurrence to have multidimensional datasets with missing conditions. For example, having a data array of power measurements for a multi-channel recording device with dimension nb_channel X nb_subjects X [...], it is current that some channels might be missing for some subjects, in which cases the array will have only NaN for this condition. Functions like Dataset.mean() performs well in this situation and will output a "RuntimeWarning: Mean of empty slice" and set the mean values for this all-NaN slice to NaN, which is what is expected for such a use case. Depending on the use case, the user has the possibility to filter these warnings to either ignore them or raise them as errors. This is all fine.

Problem: However, in the case of the Dataset.argmax(), there is no such option. The function will raise a "ValueError: All-NaN slice encountered" exception. I think it would be better if the behaviour of Dataset.argmax() was modelled on the behaviour of Dataset.mean() such that it would raise warning and set a NaN value. It seems fair to consider that the index that maximize the value of a all-NaN slice is NaN.

The implementation of such a feature may (but don't need to) depend on https://github.com/numpy/numpy/issues/12352

MCVE Code Sample

python dat = xr.DataArray(np.ones((3, 2)), coords=dict(x=[0.1, 0.2, 0.3], y=[1, 2]), dims=["x", "y"]) dat[:, 1] = np.nan dat.mean("x") dat.argmax("x") output ``` <xarray.DataArray (y: 2)> array([ 1., nan]) Coordinates: * y (y) int64 1 2

---

ValueError Traceback (most recent call last) <ipython-input-56-b53e860d1e36> in <module> 2 dat[:, 1] = np.nan 3 print(dat.mean("x")) ----> 4 print(dat.argmax("x"))

~/anaconda3/lib/python3.7/site-packages/xarray/core/common.py in wrapped_func(self, dim, axis, skipna, kwargs) 44 45 def wrapped_func(self, dim=None, axis=None, skipna=None, kwargs): ---> 46 return self.reduce(func, dim, axis, skipna=skipna, **kwargs) 47 48 else:

~/anaconda3/lib/python3.7/site-packages/xarray/core/dataarray.py in reduce(self, func, dim, axis, keep_attrs, keepdims, kwargs) 2233 """ 2234 -> 2235 var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, kwargs) 2236 return self._replace_maybe_drop_dims(var) 2237

~/anaconda3/lib/python3.7/site-packages/xarray/core/variable.py in reduce(self, func, dim, axis, keep_attrs, keepdims, allow_lazy, kwargs) 1533 1534 if axis is not None: -> 1535 data = func(input_data, axis=axis, kwargs) 1536 else: 1537 data = func(input_data, **kwargs)

~/anaconda3/lib/python3.7/site-packages/xarray/core/duck_array_ops.py in f(values, axis, skipna, kwargs) 305 306 try: --> 307 return func(values, axis=axis, kwargs) 308 except AttributeError: 309 if isinstance(values, dask_array_type):

~/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py in nanargmax(a, axis) 105 106 module = dask_array if isinstance(a, dask_array_type) else nputils --> 107 return module.nanargmax(a, axis=axis) 108 109

~/anaconda3/lib/python3.7/site-packages/xarray/core/nputils.py in f(values, axis, kwargs) 213 result = bn_func(values, axis=axis, kwargs) 214 else: --> 215 result = getattr(npmodule, name)(values, axis=axis, **kwargs) 216 217 return result

<array_function internals> in nanargmax(args, *kwargs)

~/anaconda3/lib/python3.7/site-packages/numpy/lib/nanfunctions.py in nanargmax(a, axis) 549 mask = np.all(mask, axis=axis) 550 if np.any(mask): --> 551 raise ValueError("All-NaN slice encountered") 552 return res 553

ValueError: All-NaN slice encountered ```

Expected Output

``` <xarray.DataArray (y: 2)> array([ 1., nan]) Coordinates: * y (y) int64 1 2 <xarray.DataArray (y: 2)> array([0, nan]) Coordinates: * y (y) int64 1 2

```

Versions

Output of `xr.show_versions()`

INSTALLED VERSIONS ------------------ commit: None python: 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 5.3.0-42-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_CA.UTF-8 LOCALE: en_CA.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.3 xarray: 0.15.0 pandas: 1.0.1 numpy: 1.18.2 scipy: 1.4.1 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.11.0 distributed: None matplotlib: 3.1.2 cartopy: None seaborn: 0.9.0 numbagg: None setuptools: 45.2.0.post20200210 pip: 20.0.2 conda: 4.8.2 pytest: None IPython: 7.12.0 sphinx: 2.3.1