Example:

>>> s1 = Series([10,20,30], ['A','B','C'])
>>> s2 = Series([4,3,2], ['D','C','B'])

>>> s1
A 10
B 20
C 30
dtype: int64

>>> s2
D 4
C 3
B 2
dtype: int64

>>> s1 + s2
A NaN
B 22
C 33
D NaN
dtype: float64

Note that the type of the series was changed to a float type, in order to accommodate the NaN value. That reflects a limitation of NumPy, which for efficiency uses contiguous arrays of the underlying machine types; these machine types do not contain an NaN value for integer types. So NumPy would have to be made more complex, by storing the mask bits elsewhere.