How to calculate euclidian distance between combinations of rows in pandas dataframe

I have the following dataframe:

import pandas as pd
foo = pd.DataFrame({'cluster': [1,2,3],
                   'var1': [0.3,0.5,1],
                   'var2': [0.6,0.2,0.7],
                   'var3': [0.4,0.4,0.3]})

Each row corresponds to a cluster, and the values of var‘s correspond the cluster centre with respect to the specific var

I would like to calculate the euclidian distance of each cluster to the rest.

I tried this

from itertools import combinations
def distance(list1, list2):
    """Distance between two vectors."""
    squares = [(p - q) ** 2 for p, q in zip(list1, list2)]
    return sum(squares) ** .5

foo_m = foo.melt(id_vars='cluster')
for k, v in list(combinations(foo_m.cluster.unique(),2)):
     print(k,v)
     print(distance(list(foo_m.query('cluster == @k')['value']), 
         list(foo_m.query('cluster == @v')['value'])))

I want though to output the result in a dataframe in a correlation-like matrix, where the rows and the columns will be the clusters and the values would be the distance between the respective clusters, any ideas ?

The expected output is a symmetric matrix that looks like this:

pd.DataFrame({‘cluster’: [1,2,3], ‘cluster_1’:[0,0.447213, 0.71414],
‘cluster_2’: [0.447213, 0, 0.714142], ‘cluster_3’:[0.71414, 0.714142, 0]})

>Solution :

Try with scipy:

from scipy.spatial.distance import pdist, squareform
output = pd.DataFrame(squareform(pdist(foo.set_index("cluster"))), 
                      index=foo["cluster"].values, 
                      columns=foo["cluster"].values)

>>> output
          1         2         3
1  0.000000  0.447214  0.714143
2  0.447214  0.000000  0.714143
3  0.714143  0.714143  0.000000