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# pandas.api.typing.DataFrameGroupBy.groups

property DataFrameGroupBy.groups

  Dict {group name -> group labels}.

  This property provides a dictionary representation of the groupings formed during a groupby operation, where each key represents a unique group value from the specified column(s), and each value is a list of index labels that belong to that group.

  See also

  `core.groupby.DataFrameGroupBy.get_group`

    Retrieve group from a `DataFrameGroupBy` object with provided name.

  `core.groupby.SeriesGroupBy.get_group`

    Retrieve group from a `SeriesGroupBy` object with provided name.

  `core.resample.Resampler.get_group`

    Retrieve group from a `Resampler` object with provided name.

  Examples

  For SeriesGroupBy:

      >>> lst = ["a", "a", "b"]
      >>> ser = pd.Series([1, 2, 3], index=lst)
      >>> ser
      a    1
      a    2
      b    3
      dtype: int64
      >>> ser.groupby(level=0).groups
      {'a': ['a', 'a'], 'b': ['b']}

  For DataFrameGroupBy:

      >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
      >>> df = pd.DataFrame(data, columns=["a", "b", "c"])
      >>> df
         a  b  c
      0  1  2  3
      1  1  5  6
      2  7  8  9
      >>> df.groupby(by="a").groups
      {1: [0, 1], 7: [2]}

  For Resampler:

      >>> ser = pd.Series(
      ...     [1, 2, 3, 4],
      ...     index=pd.DatetimeIndex(
      ...         ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"]
      ...     ),
      ... )
      >>> ser
      2023-01-01    1
      2023-01-15    2
      2023-02-01    3
      2023-02-15    4
      dtype: int64
      >>> ser.resample("MS").groups
      {Timestamp('2023-01-01 00:00:00'): np.int64(2),
       Timestamp('2023-02-01 00:00:00'): np.int64(4)}