Aggregating two Pandas series by summing [duplicate]

Question

I am trying to sum two series that have some matching indexes, but some that are unique. e.g.:

a = pd.Series([0.2, 0.1, 0.3], index=['A', 'B', 'C'])
b = pd.Series([0.2, 0.2], index=['A', 'D'])

Notice that index A is in both a, and b. I want to end up with a new series, which has the summed up aggregate of all indices:

A    0.4
B    0.1
C    0.3
D    0.2
dtype: float64

notice index A is the sum of both a and b (0.2 + 0.2), whereas B, C, and D are the original value. If I try to do:

c = a + b

I get the proper value for index A, but NaN for all other values. Any thoughts on the best way to do this?

Answer 1

c = a.add(b, fill_value=0)
In [28]: c
Out[28]: 
A    0.4
B    0.1
C    0.3
D    0.2
dtype: float64 

Use the .add method.

http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Series.add.html#pandas.Series.add

Adding two pandas.series objects