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 index

Answer 1

It makes more sense to use pd.concat() as it can accept more columns.

import pandas as pd
import numpy as np

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

pd.concat((a,b), axis=1).sum(1, min_count=1)

Output:

California         NaN
Ohio           70000.0
Oregon         32000.0
Texas         142000.0
Utah            5000.0
dtype: float64

---

Or with 3 series:

import pandas as pd
import numpy as np

a = pd.Series([1, np.NaN, 4, 5])
b = pd.Series([3, np.NaN, 5, np.NaN])
c = pd.Series([np.NaN,np.NaN,np.NaN,np.NaN])

print(pd.concat((a,b,c), axis=1).sum(1, min_count=1))

#0    4.0
#1    NaN
#2    9.0
#3    5.0
#dtype: float64

Answer 2

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 3

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).