从另一个具有不同索引的 dataframe 在 pandas dataframe 添加新列

Question

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.

df1['RESULT'] = df2['RESULT']

It doesn't return an error and the column is added but all values are NaNs. How do I add these columns with their values?

Answer 1

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()

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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

Answer 2

You can also call set_axis() to change the index of a dataframe/column. So if the lengths are the same, then with set_axis() , you can coerce the index of one dataframe to be the same as the other dataframe.

df1['A'] = df2['A'].set_axis(df1.index)

If you get SettingWithCopyWarning , then to silence it, you can create a copy by either calling join() or assign() .

df1 = df1.join(df2['A'].set_axis(df1.index))
# or
df1 = df1.assign(new_col = df2['A'].set_axis(df1.index))

set_axis() is especially useful if you want to add multiple columns from another dataframe. You can just call join() after calling it on the new dataframe.

df1 = df1.join(df2[['A', 'B', 'C']].set_axis(df1.index))