def combine(self, other, func, fill_value=None):
        """
        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 missing at some index
        from one of the two objects 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
        falcon    345.0
        dtype: float64

        In the previous example, the resulting value for duck is missing,
        because the maximum of a NaN and a float is a NaN.
        So, in the example, we set ``fill_value=0``,
        so the maximum value returned will be the value from some dataset.

        >>> s1.combine(s2, max, fill_value=0)
        duck       30.0
        eagle     200.0
        falcon    345.0
        dtype: float64
        """
        if fill_value is None:
            fill_value = na_value_for_dtype(self.dtype, compat=False)

        if isinstance(other, Series):
            # If other is a Series, result is based on union of Series,
            # so do this element by element
            new_index = self.index.union(other.index)
            new_name = ops.get_op_result_name(self, other)
            new_values = []
            for idx in new_index:
                lv = self.get(idx, fill_value)
                rv = other.get(idx, fill_value)
                with np.errstate(all='ignore'):
                    new_values.append(func(lv, rv))
        else:
            # Assume that other is a scalar, so apply the function for
            # each element in the Series
            new_index = self.index
            with np.errstate(all='ignore'):
                new_values = [func(lv, other) for lv in self._values]
            new_name = self.name

        if is_categorical_dtype(self.values):
            pass
        elif is_extension_array_dtype(self.values):
            # The function can return something of any type, so check
            # if the type is compatible with the calling EA.
            try:
                new_values = self._values._from_sequence(new_values)
            except Exception:
                # https://github.com/pandas-dev/pandas/issues/22850
                # pandas has no control over what 3rd-party ExtensionArrays
                # do in _values_from_sequence. We still want ops to work
                # though, so we catch any regular Exception.
                pass

        return self._constructor(new_values, index=new_index, name=new_name)
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 missing at some index
        from one of the two objects 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
        falcon    345.0
        dtype: float64

        In the previous example, the resulting value for duck is missing,
        because the maximum of a NaN and a float is a NaN.
        So, in the example, we set ``fill_value=0``,
        so the maximum value returned will be the value from some dataset.

        >>> s1.combine(s2, max, fill_value=0)
        duck       30.0
        eagle     200.0
        falcon    345.0
        dtype: float64