numba'`` engine, the engine can accept ``nopython``, ``nogil``
              and ``parallel`` dictionary keys. The values must either be ``True`` or
              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
              ``{{'nopython': True, 'nogil': False, 'parallel': False}}``

            .. versionadded:: 1.4.0

        numeric_only : bool, default False
            Include only `float`, `int` or `boolean` data.

            .. versionadded:: 1.5.0

            .. versionchanged:: 2.0.0

                numeric_only now defaults to ``False``.

        Returns
        -------
        Series or DataFrame
            Variance of values within each group.
        %(see_also)s
        Examples
        --------
        For SeriesGroupBy:

        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
        >>> ser
        a     7
        a     2
        a     8
        b     4
        b     3
        b     3
        dtype: int64
        >>> ser.groupby(level=0).var()
        a    10.333333
        b     0.333333
        dtype: float64

        For DataFrameGroupBy:

        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
        >>> df
                 a  b
          dog    1  1
          dog    3  4
          dog    5  8
        mouse    7  4
        mouse    7  4
        mouse    8  2
        mouse    3  1
        >>> df.groupby(level=0).var()
                      a          b
        dog    4.000000  12.333333
        mouse  4.916667   2.250000
        r