issue_comments: 553709888

I just ran in to this issue. While the previous fix seems to handle one case it doesn't handle all the cases. Before I clean this up and open a new PR does this look like its on the right track (it worked for my issue where I was concating multiple datasets which always had the same dims and coordinates but sometimes were missing variables)?

starts at line 353 on concat.py for k in datasets[0].variables: if k in concat_over: try: #new code for ds in datasets: if k not in ds.variables: #make a new array with the same dimensions and coordinates #by default this will be initialized to np.nan which is what we want from .dataarray import DataArray new_array = DataArray(coords=ds.coords, dims=ds.dims) ds[k] = new_array #end new code vars = ensure_common_dims([ds.variables[k] for ds in datasets]) except KeyError: #this can likely be removed then raise ValueError("%r is not present in all datasets." % k) combined = concat_vars(vars, dim, positions) assert isinstance(combined, Variable) result_vars[k] = combined