Adding a new column in pandas dataframe from another dataframe with differing indices

This is my original dataframe.This is my second dataframe containing one column.I want to add the column of second dataframe to the original dataframe at the end.Indices are different for both dataframes. I did like this

feature_file_df['RESULT']=RESULT_df['RESULT']

Result column got added but all values are NaN's

How to add columns with value

1 Answer

Assuming the size of your dataframes are the same, you can assign the RESULT_df['RESULT'].values to your original dataframe. This way, you don't have to worry about indexing issues.

# pre 0.24
feature_file_df['RESULT'] = RESULT_df['RESULT'].values
# >= 0.24
feature_file_df['RESULT'] = RESULT_df['RESULT'].to_numpy()

Minimal Code Sample

df A B
0 -1.202564 2.786483
1 0.180380 0.259736
2 -0.295206 1.175316
3 1.683482 0.927719
4 -0.199904 1.077655
df2 C
11 -0.140670
12 1.496007
13 0.263425
14 -0.557958
15 -0.018375

Let's try direct assignment first.

df['C'] = df2['C']
df A B C
0 -1.202564 2.786483 NaN
1 0.180380 0.259736 NaN
2 -0.295206 1.175316 NaN
3 1.683482 0.927719 NaN
4 -0.199904 1.077655 NaN

Now, assign the array returned by .values (or .to_numpy() for pandas versions >0.24). .values returns a numpy array which does not have an index.

df2['C'].values
array([-0.141, 1.496, 0.263, -0.558, -0.018])
df['C'] = df2['C'].values
df A B C
0 -1.202564 2.786483 -0.140670
1 0.180380 0.259736 1.496007
2 -0.295206 1.175316 0.263425
3 1.683482 0.927719 -0.557958
4 -0.199904 1.077655 -0.018375