Harvesine vectorization of vector lists

Question content

I have a code snippet that uses the semisine function to calculate the distance matrix between two coordinate lists. While the current implementation works, it involves nested loops and can be very time-consuming for large data sets. I'm looking for a more efficient alternative that avoids using a for loop.

import numpy as np
from haversine import haversine
    
string_list_1 = [(20.00,-100.1),...]  # List of vector pair coordinates (lat,long)

string_list_2 = [(21.00,-101.1),...]  # Another list of pair coordinates

dist_mat = np.zeros((len(string_list_1), len(string_list_2)))

for i, coord1 in enumerate(string_list_1):
   dist_mat[i, :] = np.array([haversine(coord1, coord2) for coord2 in string_list_2])

I would like suggestions or code examples for a more efficient and faster implementation avoiding the use of for loops.

Correct answer

Use haversine in sklearn. index:

from sklearn.metrics.pairwise import haversine_distances
haversine_distances(string_list_1,string_list_2)

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