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# pandas.api.typing.DataFrameGroupBy.get_group

DataFrameGroupBy.get_group(name)

  Construct DataFrame from group with provided name.

  Parameters:
    name object

      The name of the group to get as a DataFrame.

  Returns:
    Series or DataFrame

      Get the respective Series or DataFrame corresponding to the group provided.

  See also

  `DataFrameGroupBy.groups`

    Dictionary representation of the groupings formed during a groupby operation.

  `DataFrameGroupBy.indices`

    Provides a mapping of group rows to positions of the elements.

  `SeriesGroupBy.groups`

    Dictionary representation of the groupings formed during a groupby operation.

  `SeriesGroupBy.indices`

    Provides a mapping of group rows to positions of the elements.

  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).get_group("a")
      a    1
      a    2
      dtype: int64

  For DataFrameGroupBy:

      >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
      >>> df = pd.DataFrame(
      ...     data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"]
      ... )
      >>> df
              a  b  c
      owl     1  2  3
      toucan  1  5  6
      eagle   7  8  9
      >>> df.groupby(by=["a"]).get_group((1,))
              a  b  c
      owl     1  2  3
      toucan  1  5  6

  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").get_group("2023-01-01")
      2023-01-01    1
      2023-01-15    2
      dtype: int64