issue_comments: 226547071

Yes following a similar line of thought to you I recently wrote an 'all missing' dataset constructor (rather than 'empty' which I think of as no variables):

python def all_missing_ds(coords, var_names, var_dims, var_types): """ Make a dataset whose data is all missing. """ # Empty dataset with appropirate coordinates ds = xr.Dataset(coords=coords) for v_name, v_dims, v_type in zip(var_names, var_dims, var_types): shape = tuple(ds[d].size for d in v_dims) if v_type == int or v_type == float: # Warn about up-casting int to float? nodata = np.tile(np.nan, shape) elif v_type == complex: # astype(complex) produces (nan + 0.0j) nodata = np.tile(np.nan + np.nan*1.0j, shape) else: nodata = np.tile(np.nan, shape).astype(object) ds[v_name] = (v_dims, nodata) return ds

To go with this (and this might be separate issue), a set_value method would be helpful --- just so that one does not have to remember which particular combination of

python ds.sel(...).var = new_values ds.sel(...)['var'] = new_values ds.var.sel(...) = new_values ds['var'].sel(...) = new_values

guarantees assigning a new value, (currently only the last syntax I believe).