from sklearn.metrics.pairwise import cosine_similarity
from scipy import sparse

a = np.random.random((3, 10))
b = np.random.random((3, 10))

# Create sparse matrices, which compute faster and give more understandable output
a_sparse, b_sparse = sparse.csr_matrix(a), sparse.csr_matrix(b)

sim_sparse = cosine_similarity(a_sparse, b_sparse, dense_output=False)
print(sim_sparse)

(0, 2)    0.7938732813430508
  (0, 1)    0.7575978172453429
  (0, 0)    0.7897664361147338
  (1, 2)    0.740418315571796
  (1, 1)    0.833981672896221
  (1, 0)    0.7184526671218405
  (2, 2)    0.8746293481677073
  (2, 1)    0.6456666045233884
  (2, 0)    0.7925289217609924