issues: 179969119

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
179969119 MDU6SXNzdWUxNzk5NjkxMTk= 1019 groupby_bins: exclude bin or assign bin with nan when bin has no values 17701232 closed 0     10 2016-09-29T07:09:02Z 2016-10-03T21:54:38Z 2016-10-03T15:22:15Z NONE      

When using groupby_bins there are cases where no values are found for some of the bins specified. Currently, it appears that in these cases, the bin is skipped, with no value neither a bin entry added to the output dataarray.

Is there a way to identify which bins have been skipped. Or preferably, is it possible to have an option to include those bins, but with nan values. This would make comparing two dataarrays easier in cases where despite the same bin intervals as inputs, the outputs result in dataarrays with different variable and coordinates lengths.

``` import xarray as xr var = xr.open_dataset('c:\users\saveMWE.nc') pop = xr.open_dataset('c:\users\savePOP.nc')

binns includes very small bin to test this

binns = [-100, -50, 0, 50, 50.00001, 100] binned = pop.p2010T.groupby_bins(var.EnsembleMean, binns).sum() print binned print binned.EnsembleMean_bins ```

In this case, no data falls in the 4th bin between 50 and 50.00001.

<xarray.DataArray 'p2010T' (EnsembleMean_bins: 4)> array([ 2.64352214e+09, 3.46869168e+09, 3.08998110e+08, 1.48247440e+07]) Coordinates: * EnsembleMean_bins (EnsembleMean_bins) object '(0, 50]' '(-50, 0]' ... <xarray.DataArray 'EnsembleMean_bins' (EnsembleMean_bins: 4)> array(['(0, 50]', '(-50, 0]', '(51, 100]', '(-100, -50]'], dtype=object)

Obviously one can count the lengths but this doesn't indicate which bin was skipped. An option to include the empty bin with a nan value would be useful! Thanks

bins_example.zip

 
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