def __create_aggregator(dataset, formula, name, groups, dframe=None):
# TODO this should work with index eventually
columns = parse_columns(dataset, formula, name, dframe, no_index=True)
dependent_columns = Parser.dependent_columns(formula, dataset)
aggregation = Parser.parse_aggregation(formula)
# get dframe with only the necessary columns
select = combine_dicts({group: 1 for group in groups},
{col: 1 for col in dependent_columns})
# ensure at least one column (MONGO_ID) for the count aggregation
query_args = QueryArgs(select=select or {MONGO_ID: 1})
dframe = dataset.dframe(query_args=query_args, keep_mongo_keys=not select)
return Aggregator(dframe, groups, aggregation, name, columns)
