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# pandas.api.typing.DataFrameGroupBy.filter

DataFrameGroupBy.filter(func, dropna=True, *args, **kwargs)

  Filter elements from groups that don’t satisfy a criterion.

  Elements from groups are filtered if they do not satisfy the boolean criterion specified by func.

  Parameters:
    func function

      Criterion to apply to each group. Should return True or False.

    dropna bool

      Drop groups that do not pass the filter. True by default; if False, groups that evaluate False are filled with NaNs.

    *args tuple

      Additional positional arguments to pass to func.

    **kwargs dict

      Additional keyword arguments to pass to func.

  Returns:
    DataFrame

      The filtered subset of the original DataFrame.

  See also

  `DataFrame.filter`

    Filter elements of ungrouped DataFrame.

  `SeriesGroupBy.filter`

    Filter elements from groups base on criterion.

  Notes

  Each subframe is endowed the attribute ‘name’ in case you need to know which group you are working on.

  Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods for more details.

  Examples

      >>> df = pd.DataFrame(
      ...     {
      ...         "A": ["foo", "bar", "foo", "bar", "foo", "bar"],
      ...         "B": [1, 2, 3, 4, 5, 6],
      ...         "C": [2.0, 5.0, 8.0, 1.0, 2.0, 9.0],
      ...     }
      ... )
      >>> grouped = df.groupby("A")
      >>> grouped.filter(lambda x: x["B"].mean() > 3.0)
           A  B    C
      1  bar  2  5.0
      3  bar  4  1.0
      5  bar  6  9.0