Element-wise Maximum of Two DataFrames Ignoring NaNs

Question

I have two dataframes (df1 and df2) that each have the same rows and columns. I would like to take the maximum of these two dataframes, element-by-element. In addition, the result of any element-wise maximum with a number and NaN should be the number. The approach I have implemented so far seems inefficient:

def element_max(df1,df2):
    import pandas as pd
    cond = df1 >= df2
    res = pd.DataFrame(index=df1.index, columns=df1.columns)
    res[(df1==df1)&(df2==df2)&(cond)]  = df1[(df1==df1)&(df2==df2)&(cond)]
    res[(df1==df1)&(df2==df2)&(~cond)] = df2[(df1==df1)&(df2==df2)&(~cond)]
    res[(df1==df1)&(df2!=df2)&(~cond)] = df1[(df1==df1)&(df2!=df2)]
    res[(df1!=df1)&(df2==df2)&(~cond)] = df2[(df1!=df1)&(df2==df2)]
    return res

Any other ideas? Thank you for your time.

Answer 1

A more readable way to do this in recent versions of pandas is concat-and-max:

import scipy as sp
import pandas as pd

A = pd.DataFrame([[1., 2., 3.]])
B = pd.DataFrame([[3., sp.nan, 1.]])

pd.concat([A, B]).max(level=0)
# 
#           0    1    2
#      0  3.0  2.0  3.0 
#