假设数据框的大小相同，则可以分配 RESULT_df ['RESULT']。values 到原始数据框。这样，您就不必担心索引问题。

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()

最小代码样本

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

让我们先尝试直接分配。

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

现在，a分配由 .values 返回的数组（或对于 .to_numpy（）返回）。 .values 返回一个没有索引的 numpy 数组。

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