python find the similarly with each elements of a nested list with other list, without using for loop

I have a list like this

l=[[1,2,3],[4,5,6],[2,3,4],[3,5,7],[5,6,7],[6,7,8],[7,8,9]]

Now I have a another list,

l1=[3,6,9]

Now I want to calculate similarity with each element of l with l1 and append the similarity value to an empty list.

I could do this using following code,

from sklearn.metrics.pairwise import cosine_similarity
values=[]
for i in l:
    values.append(cosine_similarity([i],[l1]))

Above code the results what I wanted but the execution time is huge. I am looking for some shortcuts to do this most efficiently.

You can convert everything to a 2D numpy array and then simply apply cosine_similarity once and let the shape broadcasting take care of everything

First you need to convert your data into numpy arrays

>>> l = np.array([[1,2,3],[4,5,6],[2,3,4],[3,5,7],[5,6,7],[6,7,8],[7,8,9]])
>>> l.shape
(7, 3)

Similarly, you will also need to convert l1. Additionally you will need to reshape it into a 2D array

>>> l1 = np.array([3,6,9])
>>> l1.shape
(3,)
>>> l1 = l1.reshape(1, -1)
>>> l1.shape
(1, 3)

Now, you can easily just apply the cosine similarity function

cosine_similarity(l, l1)

This gives the resulting array as

array([[1.        ],
       [0.97463185],
       [0.99258333],
       [0.9974149 ],
       [0.96832966],
       [0.96337534],
       [0.95941195]])

You can use numpy 2D arrays rather than list. below code might help:

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

l=np.array([[1,2,3],[4,5,6],[2,3,4],[3,5,7],[5,6,7],[6,7,8],[7,8,9]])

# You need to reshape your data 
l1=np.array([3,6,9]).reshape(1, -1)

def cal_cs(arr):
    # Reshaping here also
    return cosine_similarity(l1, arr.reshape(1, 3))

list(map(lambda x: cal_cs(x), l))

The result which I am getting is

[array([[1.]]),
 array([[0.97463185]]),
 array([[0.99258333]]),
 array([[0.9974149]]),
 array([[0.96832966]]),
 array([[0.96337534]]),
 array([[0.95941195]])

Hope this helps