Python：如何将 Dataframe 列作为参数传递给 function？

Question

I have a dataframe df with 2 columns of text embeddings namely embedding_1 and embedding_2 . I want to create a third column in df named distances which should contain the cosine_similarity between every row of embedding_1 and embedding_2 .

But when I try to implement this using the following code I get a ValueError .

How to fix it?

Dataframe df

           embedding_1              |            embedding_2                                 
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.49163356, -0.4877703,...]]
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.06686627, -0.75147504...]]
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.42776933, -0.88310856,...]]
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.6520882, -1.049325,...]]
 [[-0.28876397, -0.6367827, ...]]   |  [[-1.4216679, -0.8930428,...]]

Code to Calculate Cosine Similarity

df['distances'] = cosine_similarity(df['embeddings_1'], df['embeddings_2'])

Error

ValueError: setting an array element with a sequence.

Required Dataframe

       embedding_1              |            embedding_2                 |  distances                        
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.49163356, -0.4877703,...]]   |    0.427
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.06686627, -0.75147504...]]   |    0.673
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.42776933, -0.88310856,...]]  |    0.882
 [[-0.28876397, -0.6367827, ...]]   |  [[-0.6520882, -1.049325,...]]     |    0.665
 [[-0.28876397, -0.6367827, ...]]   |  [[-1.4216679, -0.8930428,...]]    |    0.312

Answer 1

You can use apply() to use cosine_similarity() on each row:

def cal_cosine_similarity(row):
    return cosine_similarity(row['embeddings_1'], row['embeddings_2'])

df['distances'] = df.apply(cal_cosine_similarity, axis=1)

or one liner

df['distances'] = df.apply(lambda row: cosine_similarity(row['embeddings_1'], row['embeddings_2']), axis=1)