def combine(self, other, func, fill_value=None):
if fill_value is None:<EOL><INDENT>fill_value = na_value_for_dtype(self.dtype, compat=False)<EOL><DEDENT>if isinstance(other, Series):<EOL><INDENT>new_index = self.index.union(other.index)<EOL>new_name = ops.get_op_result_name(self, other)<EOL>new_values = []<EOL>for idx in new_index:<EOL><INDENT>lv = self.get(idx, fill_value)<EOL>rv = other.get(idx, fill_value)<EOL>with np.errstate(all='<STR_LIT:ignore>'):<EOL><INDENT>new_values.append(func(lv, rv))<EOL><DEDENT><DEDENT><DEDENT>else:<EOL><INDENT>new_index = self.index<EOL>with np.errstate(all='<STR_LIT:ignore>'):<EOL><INDENT>new_values = [func(lv, other) for lv in self._values]<EOL><DEDENT>new_name = self.name<EOL><DEDENT>if is_categorical_dtype(self.values):<EOL><INDENT>pass<EOL><DEDENT>elif is_extension_array_dtype(self.values):<EOL><INDENT>try:<EOL><INDENT>new_values = self._values._from_sequence(new_values)<EOL><DEDENT>except Exception:<EOL><INDENT>pass<EOL><DEDENT><DEDENT>return self._constructor(new_values, index=new_index, name=new_name)<EOL>
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