Most efficient way to construct similarity matrix

Question

I\'m using the following links to create a \"Euclidean Similarity Matrix\" (that I convert to a DataFrame). https://stats.stackexchange.com/questions/53068/euclidean-distan

Answer 1

I think you can just use pdist and squareform to broadcast directly on your DataFrame:

from scipy.spatial.distance import pdist,squareform

In [6]: squareform(pdist(DF_var, metric='euclidean'))

Out[6]:
array([[ 0.        ,  0.6164414 ,  1.4525839 ,  0.78740079],
       [ 0.6164414 ,  0.        ,  1.1       ,  0.24494897],
       [ 1.4525839 ,  1.1       ,  0.        ,  0.87749644],
       [ 0.78740079,  0.24494897,  0.87749644,  0.        ]])