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/60#issuecomment-532354509,https://api.github.com/repos/pydata/xarray/issues/60,532354509,MDEyOklzc3VlQ29tbWVudDUzMjM1NDUwOQ==,4762214,2019-09-17T18:54:40Z,2019-09-17T18:54:40Z,NONE,"I got around this with some (masked) numpy operations. perhaps it is useful? I was seeing the `np.argmax` results on entries with all NaN evaluate to zero, which was not useful since the axis I was computing argmax across had valid entries if the result was 0 (think 0-index month, i.e., January, within a year). So I did this instead:

```
# test_arr is some array with some nodata value, and is of dims [channels, rows, columns]
nodata = -32768
ma = np.ma.masked_equal(test_arr, nodata)

# use np.any to get a mask of rows/columns which have all masked entries
spec_axis = 0
all_na_mask = np.any(ma, axis=spec_axis)

# get the argmax across specified axis
argm = np.argmax(test_arr, axis=spec_axis)
argm = np.ma.masked_less(argm, -np.inf)
argm.mask = ~all_na_mask
```

big piece here is modifying the mask directly and making sure that is correct. numpy docs advise against this approach but it seems to be giving me what I want.
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