I have a dataframe df made up of n columns which are groups and one, "data". This dataframe is then grouped on the n group columns. df = pd.DataFrame(data={"g0": ["foo", "foo", "bar", "bar"], "g1": ["baz", "baz", "baz", "qux"], ..., "gn": [...], "data": [0.1, 0.3, 0.4, 0.2]}, index=["a", "b", "c", "d"]) groups = df.groupby(by=["g0", "g1", ..., "gn"], sort=False) Then I have a list idx_kept which includes only some of the original dataframe indices e.g. idx_kept = ["a", "b", "d"]. Is there a way to filter groups and keep only the data which initiall had the indices in idx_kept? My understanding of DataFrameGroupBy.filter is that it is not appropriate in that case as it uses an aggregate function and remove whole goups. I could filter df directly to get a df_filtered and do a groups_filtered=df_filtered.groupby(by=["g0", "g1", ..., "gn"], sort=False). However, In my process, I need both groups and groups_filtered so my goal is to avoid a second grouby to save some time. Is there an elegant/fast way to achieve that? Continue reading...
