issues: 354298235

Code Sample

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

def standardize(x): return (x - x.mean()) / x.std()

ds = xr.Dataset() ds["variable"] = xr.DataArray(np.random.rand(4,3,5), {"lat":np.arange(4), "lon":np.arange(3), "time":np.arange(5)}, ("lat", "lon", "time"), )

ds["id"] = xr.DataArray(np.arange(12.0).reshape((4,3)), {"lat": np.arange(4), "lon":np.arange(3)}, ("lat", "lon"), )

ds["id"].values[0,0] = np.nan

ds.groupby("id").apply(standardize) ```

Problem description

This results in an IndexError. This is mildly confusing, it took me a little while to figure out the NaN's were to blame. I'm guessing the NaN doesn't get filtered out everywhere.

The traceback: ```

---

IndexError Traceback (most recent call last) <ipython-input-2-267ba57bc264> in <module>() 15 ds["id"].values[0,0] = np.nan 16 ---> 17 ds.groupby("id").apply(standardize)

C:\Miniconda3\envs\main\lib\site-packages\xarray\core\groupby.py in apply(self, func, kwargs) 607 kwargs.pop('shortcut', None) # ignore shortcut if set (for now) 608 applied = (func(ds, kwargs) for ds in self._iter_grouped()) --> 609 return self._combine(applied) 610 611 def _combine(self, applied):

C:\Miniconda3\envs\main\lib\site-packages\xarray\core\groupby.py in _combine(self, applied) 614 coord, dim, positions = self._infer_concat_args(applied_example) 615 combined = concat(applied, dim) --> 616 combined = _maybe_reorder(combined, dim, positions) 617 if coord is not None: 618 combined[coord.name] = coord

C:\Miniconda3\envs\main\lib\site-packages\xarray\core\groupby.py in _maybe_reorder(xarray_obj, dim, positions) 428 429 def _maybe_reorder(xarray_obj, dim, positions): --> 430 order = _inverse_permutation_indices(positions) 431 432 if order is None:

C:\Miniconda3\envs\main\lib\site-packages\xarray\core\groupby.py in _inverse_permutation_indices(positions) 109 positions = [np.arange(sl.start, sl.stop, sl.step) for sl in positions] 110 --> 111 indices = nputils.inverse_permutation(np.concatenate(positions)) 112 return indices 113

C:\Miniconda3\envs\main\lib\site-packages\xarray\core\nputils.py in inverse_permutation(indices) 52 # use intp instead of int64 because of windows :( 53 inverse_permutation = np.empty(len(indices), dtype=np.intp) ---> 54 inverse_permutation[indices] = np.arange(len(indices), dtype=np.intp) 55 return inverse_permutation 56

IndexError: index 11 is out of bounds for axis 0 with size 11

```

Expected Output

My assumption was that it would throw out the values that fall within the NaN group, likepandas:

```python import pandas as pd import numpy as np

df = pd.DataFrame() df["var"] = np.random.rand(10) df["id"] = np.arange(10) df["id"].iloc[0:2] = np.nan df.groupby("id").mean() ```

Out: python var id 2.0 0.565366 3.0 0.744443 4.0 0.190983 5.0 0.196922 6.0 0.377282 7.0 0.141419 8.0 0.957526 9.0 0.207360

Output of xr.show_versions()

``` INSTALLED VERSIONS ------------------ commit: None python: 3.6.5.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None xarray: 0.10.8 pandas: 0.23.3 numpy: 1.15.0 scipy: 1.1.0 netCDF4: 1.4.0 h5netcdf: 0.6.1 h5py: 2.8.0 Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: None dask: 0.18.2 distributed: 1.22.0 matplotlib: 2.2.2 cartopy: None seaborn: None setuptools: 40.0.0 pip: 18.0 conda: None pytest: 3.7.1 IPython: 6.4.0 sphinx: 1.7.5 ```