Add 2-D tensor as column of dataframe
My dataframe looks like
   INCIDENT_NUMBER                      
0  INC000030884498
1  INC000029956111
2  INC000029555353
3  INC000029555338

I also have a 2-D tensor for the above four incidents like
  sample_concatenated_embedding=
  tensor(                    
  [[ 0.6993, -0.1427, -0.1532,  ...,  0.8386,  0.5151,  0.8906],
  [ 0.7382, -0.8497,  0.1363,  ...,  0.8054,  0.5432,  0.9082],
  [ 0.0835, -0.2431, -0.0815,  ...,  0.8025,  0.5217,  0.9041],
  [-0.0346, -0.2396, -0.5831,  ...,  0.7591,  0.6138,  0.9649]],
  grad_fn=&lt;ViewBackward&gt;)

The embeddings are of size [4, 161280]
I want to insert the tensor in the four consecutive lines of my Dataframe
The end Dataframe should look like
   INCIDENT_NUMBER      embedding         
0  INC000030884498      [ 0.6993, -0.1427, -0.1532,  ...,  0.8386,  0.5151,  0.8906]
1  INC000029956111      [ 0.7382, -0.8497,  0.1363,  ...,  0.8054,  0.5432,  0.9082]
2  INC000029555353      [ 0.0835, -0.2431, -0.0815,  ...,  0.8025,  0.5217,  0.9041]
3  INC000029555338      [-0.0346, -0.2396, -0.5831,  ...,  0.7591,  0.6138,  0.9649]

If the tensors would have been series, i could have simply used the below command
 my_dataframe['embedding'] = sample_concatenated_embedding

I could use a for loop and easily insert into dataframe like
 empty_dataframe = pd.DataFrame(columns=['incident','embedding'])

 for item in range(0,4):
     INCIDENT_NUMBER = my_dataframe['INCIDENT_NUMBER'].iloc[item]
     temp_df = pd.DataFrame([[INCIDENT_NUMBER, sample_concatenated_embedding[item]], columns=['incident','embedding']) 
     frames = [empty_dataframe, temp_df]
     empty_dataframe = pd.concat(frames)

But for loop would be inefficent. Is there any shorter way to achieve the end goal

If the value index of INCIDENT_NUMBER and value index of sample_concatenated_embedding matches. You can just convert sample_concatenated_embedding to list then assign it to the new column like
import pandas as pd


df = pd.DataFrame({'INCIDENT_NUMBER': ['INC000030884498', 'INC000029956111', 'INC000029555353', 'INC000029555338']})

data = [[ 0.6993, -0.1427, -0.1532, 0.8386,  0.5151,  0.8906],
        [ 0.7382, -0.8497,  0.1363, 0.8054,  0.5432,  0.9082],
        [ 0.0835, -0.2431, -0.0815, 0.8025,  0.5217,  0.9041],
        [-0.0346, -0.2396, -0.5831, 0.7591,  0.6138,  0.9649]]

df['embedding'] = data
df.rename(columns={'INCIDENT_NUMBER': 'incident'}, inplace=True)

print(df)

          incident                                            embedding
0  INC000030884498   [0.6993, -0.1427, -0.1532, 0.8386, 0.5151, 0.8906]
1  INC000029956111    [0.7382, -0.8497, 0.1363, 0.8054, 0.5432, 0.9082]
2  INC000029555353   [0.0835, -0.2431, -0.0815, 0.8025, 0.5217, 0.9041]
3  INC000029555338  [-0.0346, -0.2396, -0.5831, 0.7591, 0.6138, 0.9649]

