html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1772#issuecomment-791379121,https://api.github.com/repos/pydata/xarray/issues/1772,791379121,MDEyOklzc3VlQ29tbWVudDc5MTM3OTEyMQ==,1119116,2021-03-05T12:08:17Z,2021-03-05T12:09:32Z,NONE,"A possible code for this functionality could be:
```
def nonzero4xarray (da:xarray.DataArray):
    c = da.where(da, drop=True).coords
    s = da.sel(c).to_series()
    s = s[s]
    o = []
    for record in s.index:
        o.append(dict(list(zip(s.index.names, record))))
    return o
```
however the output is sub-optimal, as it yields a list of dicts, each with the corresponding coordinate to the nonzero value.

Consider the following example:
```
>>> import numpy as np
>>> import xarray as xr
>>> x = np.arange(5) * 100
>>> y = np.arange(4) * 100
>>> z = np.arange(3) * 10
>>> da = xr.DataArray(np.random.randn(z.size, y.size, x.size), dims=('z', 'y', 'x'), coords={'x': x, 'y': y, 'z': z})
>>> da.loc[da.isel({'x':[0, 2], 'y':[1,3], 'z':[0]}).coords] = np.nan
>>> da.loc[da.isel({'x':[1, 3], 'y':[0], 'z':[-1]}).coords] = np.nan
```
and imagine I want to capture the locations with NaN values. If I do:
```
>>> coords = da.where(da.isnull(), drop=True).coords
>>> da.sel(coords)
array([[[ 1.586261, -0.2003  ,  0.734872, -1.198746],
        [      nan, -0.108265,       nan,  0.754857],
        [      nan,  0.700742,       nan, -1.514642]],

       [[ 1.109573,       nan, -1.596756,       nan],
        [ 0.031771, -0.341263,  1.358767,  0.221174],
        [-0.671605,  0.923703,  0.085335, -1.672686]]])
```
I will get the coordinates for a mix of NaN and valid values, depending on how these are distributed.

If I want to obtain only the NaN locations:
```
>>> coords = nonzero4xarray(da.isnull())
>>> coords
[{'z': 0, 'y': 100, 'x': 0}, {'z': 0, 'y': 100, 'x': 200}, {'z': 0, 'y': 300, 'x': 0}, {'z': 0, 'y': 300, 'x': 200}, {'z': 20, 'y': 0, 'x': 100}, {'z': 20, 'y': 0, 'x': 300}]
>>> vals = [da.sel(c).values[()] for c in coords]
>>> vals
[nan, nan, nan, nan, nan, nan]
```
whose functionality is similar to `numpy.nonzero`, although sub-optimal as we cannot issue `da.sel(coords)` directly.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,280875330