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# pandas.core.groupby.DataFrameGroupBy.get_group

DataFrameGroupBy.get_group(name, obj=None)

  Construct DataFrame from group with provided name.

  Parameters:
    nameobject

      The name of the group to get as a DataFrame.

    objDataFrame, default None

      The DataFrame to take the DataFrame out of. If it is None, the object groupby was called on will be used.

      Deprecated since version 2.1.0: The obj is deprecated and will be removed in a future version. Do `df.iloc[gb.indices.get(name)]` instead of `gb.get_group(name, obj=df)`.

  Returns:
    same type as obj

  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