Method var

pandas/core/groupby/groupby.py:2512–2622

Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. engine : str, default None * ``'cython'`` :

(
        self,
        ddof: int = 1,
        engine: Literal["cython", "numba"] | None = None,
        engine_kwargs: dict[str, bool] | None = None,
        numeric_only: bool = False,
        skipna: bool = True,
    )

    @final
    def var(
        self,
        ddof: int = 1,
        engine: Literal["cython", "numba"] | None = None,
        engine_kwargs: dict[str, bool] | None = None,
        numeric_only: bool = False,
        skipna: bool = True,
    ):
        """
        Compute variance of groups, excluding missing values.

        For multiple groupings, the result index will be a MultiIndex.

        Parameters
        ----------
        ddof : int, default 1
            Degrees of freedom.

        engine : str, default None
            * ``'cython'`` : Runs the operation through C-extensions from cython.
            * ``'numba'`` : Runs the operation through JIT compiled code from numba.
            * ``None`` : Defaults to ``'cython'`` or globally setting
              ``compute.use_numba``

        engine_kwargs : dict, default None
            * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
            * For ``'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}``

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

            .. versionchanged:: 2.0.0

                numeric_only now defaults to ``False``.

        skipna : bool, default True
            Exclude NA/null values. If an entire group is NA, the result will be NA.

            .. versionadded:: 3.0.0

        Returns
        -------
        Series or DataFrame
            Variance of values within each group.

        See Also
        --------
        Series.var : Apply function var to a Series.
        DataFrame.var : Apply function var to each row or column of a DataFrame.

        Examples
        --------
        For SeriesGroupBy:

        >>> lst = ["a", "a", "a", "b", "b", "b"]