CODEWITHSUNDEEP
pythonpandasdataframemergeconcatenation
Kenny Liao
Kenny Liao

Reputation: 43

Combining dataframes in pandas with the same rows and columns, but different cell values

I'm interested in combining two dataframes in pandas that have the same row indices and column names, but different cell values. See the example below:

import pandas as pd
import numpy as np

df1 = pd.DataFrame({'A':[22,2,np.NaN,np.NaN],
                    'B':[23,4,np.NaN,np.NaN],
                    'C':[24,6,np.NaN,np.NaN],
                    'D':[25,8,np.NaN,np.NaN]})

df2 = pd.DataFrame({'A':[np.NaN,np.NaN,56,100],
                    'B':[np.NaN,np.NaN,58,101],
                    'C':[np.NaN,np.NaN,59,102],
                    'D':[np.NaN,np.NaN,60,103]})

In[6]: print(df1)
      A     B     C     D
0  22.0  23.0  24.0  25.0
1   2.0   4.0   6.0   8.0
2   NaN   NaN   NaN   NaN
3   NaN   NaN   NaN   NaN

In[7]: print(df2)
       A      B      C      D
0    NaN    NaN    NaN    NaN
1    NaN    NaN    NaN    NaN
2   56.0   58.0   59.0   60.0
3  100.0  101.0  102.0  103.0

I would like the resulting frame to look like this:

       A      B      C      D
0   22.0   23.0   24.0   25.0
1    2.0    4.0    6.0    8.0
2   56.0   58.0   59.0   60.0
3  100.0  101.0  102.0  103.0

I have tried different ways of pd.concat and pd.merge but some of the data always gets replaced with NaNs. Any pointers in the right direction would be greatly appreciated.

Upvotes: 3

Views: 96

Answers (2)

Zeugma
Zeugma

Reputation: 32095

Use combine_first

df1.combine_first(df2)

Upvotes: 1

jezrael
jezrael

Reputation: 862761

Use combine_first:

print (df1.combine_first(df2))
       A      B      C      D
0   22.0   23.0   24.0   25.0
1    2.0    4.0    6.0    8.0
2   56.0   58.0   59.0   60.0
3  100.0  101.0  102.0  103.0

Or fillna:

print (df1.fillna(df2))
       A      B      C      D
0   22.0   23.0   24.0   25.0
1    2.0    4.0    6.0    8.0
2   56.0   58.0   59.0   60.0
3  100.0  101.0  102.0  103.0

Or update:

df1.update(df2)
print (df1)
       A      B      C      D
0   22.0   23.0   24.0   25.0
1    2.0    4.0    6.0    8.0
2   56.0   58.0   59.0   60.0
3  100.0  101.0  102.0  103.0

Upvotes: 2

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