Adding two Series with NaNs

Question

I'm working through the "Python For Data Analysis" and I don't understand a particular functionality. Adding two pandas series objects will automatically align the indexed data but if one object does not contain that index it is returned as NaN. For example from book:

a = Series([35000,71000,16000,5000],index=['Ohio','Texas','Oregon','Utah']) b = Series([NaN,71000,16000,35000],index=['California', 'Texas', 'Oregon', 'Ohio']) 

Result:

    In [63]: a     Out[63]: Ohio          35000              Texas         71000              Oregon        16000              Utah           5000     In [64]: b     Out[64]: California      NaN              Texas         71000              Oregon        16000              Ohio          35000 

When I add them together I get this...

    In [65]: a+b     Out[65]: California       NaN              Ohio           70000              Oregon         32000              Texas         142000              Utah             NaN 

So why is the Utah value NaN and not 500? It seems that 500+NaN=500. What gives? I'm missing something, please explain.

Update:

    In [92]: # fill NaN with zero              b = b.fillna(0)              b     Out[92]: California        0              Texas         71000              Oregon        16000              Ohio          35000      In [93]: a     Out[93]: Ohio      35000              Texas     71000              Oregon    16000              Utah       5000      In [94]: # a is still good              a+b     Out[94]: California       NaN              Ohio           70000              Oregon         32000              Texas         142000               Utah             NaN 

Answer 1

Pandas does not assume that 500+NaN=500, but it is easy to ask it to do that:

a.add(b, fill_value=0) 

Answer 2

The default approach is to assume that any computation involving NaN gives NaN as the result. Anything plus NaN is NaN, anything divided by NaN is NaN, etc. If you want to fill the NaN with some value, you have to do that explicitly (as Dan Allan showed in his answer).