Method combine

pandas/core/series.py:3264–3366

Combine the Series with a Series or scalar according to `func`. Combine the Series and `other` using `func` to perform elementwise selection for combined Series. `fill_value` is assumed when value is not present at some index from one of the two Series being

(
        self,
        other: Series | Hashable,
        func: Callable[[Hashable, Hashable], Hashable],
        fill_value: Hashable | None = None,
    )

        )

    def combine(
        self,
        other: Series | Hashable,
        func: Callable[[Hashable, Hashable], Hashable],
        fill_value: Hashable | None = None,
    ) -> Series:
        """
        Combine the Series with a Series or scalar according to `func`.

        Combine the Series and `other` using `func` to perform elementwise
        selection for combined Series.
        `fill_value` is assumed when value is not present at some index
        from one of the two Series being combined.

        Parameters
        ----------
        other : Series or scalar
            The value(s) to be combined with the `Series`.
        func : function
            Function that takes two scalars as inputs and returns an element.
        fill_value : scalar, optional
            The value to assume when an index is missing from
            one Series or the other. The default specifies to use the
            appropriate NaN value for the underlying dtype of the Series.

        Returns
        -------
        Series
            The result of combining the Series with the other object.

        See Also
        --------
        Series.combine_first : Combine Series values, choosing the calling
            Series' values first.

        Examples
        --------
        Consider 2 Datasets ``s1`` and ``s2`` containing
        highest clocked speeds of different birds.

        >>> s1 = pd.Series({"falcon": 330.0, "eagle": 160.0})
        >>> s1
        falcon    330.0
        eagle     160.0
        dtype: float64
        >>> s2 = pd.Series({"falcon": 345.0, "eagle": 200.0, "duck": 30.0})
        >>> s2
        falcon    345.0
        eagle     200.0
        duck       30.0
        dtype: float64

        Now, to combine the two datasets and view the highest speeds
        of the birds across the two datasets

        >>> s1.combine(s2, max)
        duck        NaN
        eagle     200.0