y weighted mean.

        Parameters
        ----------
        update: DataFrame or Series, default None
            New values to continue calculating the
            exponentially weighted mean from the last values and weights.
            Values should be float64 dtype.

            ``update`` needs to be ``None`` the first time the
            exponentially weighted mean is calculated.

        update_times: Series or 1-D np.ndarray, default None
            New times to continue calculating the
            exponentially weighted mean from the last values and weights.
            If ``None``, values are assumed to be evenly spaced
            in time.
            This feature is currently unsupported.

        Returns
        -------
        DataFrame or Series

        Examples
        --------
        >>> df = pd.DataFrame({"a": range(5), "b": range(5, 10)})
        >>> online_ewm = df.head(2).ewm(0.5).online()
        >>> online_ewm.mean()
              a     b
        0  0.00  5.00
        1  0.75  5.75
        >>> online_ewm.mean(update=df.tail(3))
                  a         b
        2  1.615385  6.615385
        3  2.550000  7.550000
        4  3.520661  8.520661
        >>> online_ewm.reset()
        >>> online_ewm.mean()
              a     b
        0  0.00  5.00
        1  0.75  5.75
        r/