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In [1]:

    
import numpy as np
import pandas as pd
from pandas import Series,DataFrame



In [4]:

    
ser1 = Series([2,np.nan,4,np.nan,6,np.nan],
             index=['Q','R','S','T','U','V'])

ser1









    Out[4]:





Q    2.0
R    NaN
S    4.0
T    NaN
U    6.0
V    NaN
dtype: float64



In [3]:

    
ser2 = Series(np.arange(len(ser1)),
             index=['Q','R','S','T','U','V'],
             dtype=np.float64)
ser2









    Out[3]:





Q    0.0
R    1.0
S    2.0
T    3.0
U    4.0
V    5.0
dtype: float64



In [5]:

    
Series(np.where(pd.isnull(ser1),ser2,ser1),index=ser1.index)









    Out[5]:





Q    2.0
R    1.0
S    4.0
T    3.0
U    6.0
V    5.0
dtype: float64



In [6]:

    
# shorcut to previous command
ser1.combine_first(ser2)









    Out[6]:





Q    2.0
R    1.0
S    4.0
T    3.0
U    6.0
V    5.0
dtype: float64



In [7]:

    
# dataframes
nan = np.nan
df_odds = DataFrame({'X':[1.,nan,3.,nan],
                     'Y':[nan,5.,nan,7.],
                     'Z':[nan,9.,nan,11.]})



In [9]:

    
df_evens = DataFrame({'X':[2.,4.,nan,6.,8.],
                      'Y':[nan,10.,12.,14.,16.]})



In [10]:

    
df_odds



In [11]:

    
df_evens



In [12]:

    
df_odds.combine_first(df_evens)



In [ ]: