Method mean

pandas/core/groupby/groupby.py:2193–2293

Compute mean of groups, excluding missing values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None`` and defau

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

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

        Parameters
        ----------
        numeric_only : bool, default False
            Include only float, int, boolean columns.

            .. versionchanged:: 2.0.0

                numeric_only no longer accepts ``None`` and defaults to ``False``.

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

        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}``

        Returns
        -------
        pandas.Series or pandas.DataFrame
            Mean of values within each group. Same object type as the caller.

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

        Examples
        --------
        >>> df = pd.DataFrame(
        ...     {"A": [1, 1, 2, 1, 2], "B": [np.nan, 2, 3, 4, 5], "C": [1, 2, 1, 1, 2]},
        ...     columns=["A", "B", "C"],
        ... )

        Groupby one column and return the mean of the remaining columns in
        each group.

        >>> df.groupby("A").mean()
             B         C
        A