issue_comments: 791019238

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https://github.com/pydata/xarray/issues/4995#issuecomment-791019238 https://api.github.com/repos/pydata/xarray/issues/4995 791019238 MDEyOklzc3VlQ29tbWVudDc5MTAxOTIzOA== 43613877 2021-03-04T23:10:11Z 2021-03-04T23:10:11Z CONTRIBUTOR

Introducing a fill_value seems like a good idea, such that the size of the output does not change compared to the intended selection. Choosing the original/requested coordinate as a label for the missing datapoint seems to be a valid choice because this position has been checked for valid data nearby without success. I would suggest, that the fill_value should then be automatically determined from the _FillValue, the datatype and only at last requires the fill_value to be set.

However, the shortcoming that I see in using a fill_value is that the indexing has to modify the data ( insert e.g. -999) and also 'invent' a new coordinate point ( here 40). This gets reasonably complex, when applying to a dataset with DataArrays of different types, e.g. ```python import numpy as np import xarray as xr

ds = xr.Dataset() ds['data1'] = xr.DataArray(np.array([1,2,3,4,5], dtype=int), dims=["lat"], coords={'lat':[10,20,30,50,60]}) ds['data2'] = xr.DataArray(np.array([1,2,3,4,5], dtype=float), dims=["lat"], coords={'lat':[10,20,30,50,60]}) `` Onefill_valuemight not fit to all data arrays being it because of the datatype or the actual data. E.g.-999might be a goodfill_value` for one DataArray but a valid datapoint in another one.

 
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