issue_comments: 358017392

Below is how I work around the issue in practice: writing a loop over each item in the groupby, and then looping over each variable, loading, and writing to disk.

python gb = ds.groupby('time.month') for month, dsm in gb: dsm_anom2 = ((dsm - ds_mm.sel(month=month))**2).mean(dim='time') dsm_anom2 = dsm_anom2.rename({f: f + '2' for f in fields}) dsm_anom2.coords['month'] = month for var in dsm_anom2.data_vars: filename = save_dir + '%02d.%s_%s.nc' % (month, prefix, var) print(filename) ds_out = dsm_anom2[[var]].load() ds_out.to_netcdf(filename)

Needless to say, this feels more like my pre-xarray/dask workflow.

Since @mrocklin has made it pretty clear that dask will not automatically solve this for us any time soon, we need to brainstorm some creative ways to make this extremely common use case more friendly with out-of-core data.