K-Means clustering in Python2

I've implemented the K-Means clustering algorithm in Python2, and I wanted to know what remarks you guys could make regarding my code. I've included a small test set with 2D-vectors and 2 classes, but it works with higher dimensions and more classes. Here, it should sort all the elements starting with the same letters in the same classes (except ak, with is quite in between).

What I'm interested in:

• Did I do something unpythonic?
• Are there unclear or unnecessary parts?
• What changes could drastically improve the code's performance?

from __future__ import print_function
from operator import add, sub
from _collections import defaultdict
import random as rd
import math


class Element:
    def __init__(self, idf, data, cls='ukn'):
        self.idf = idf
        self.data = data
        self.cls = cls

    def __str__(self):
        return '%s: %s -> %s' % (str(self.idf), str(self.data), str(self.cls))

    def eucl_dist_to(self, other):
        diff = map(sub, self.data, other.data)
        norm2 = math.sqrt(sum([math.pow(coord, 2) for coord in diff]))
        return norm2


def main():
    test_data_tuple = [('aa', 1, 7), ('ab', 1, 8), ('ac', 1, 9), ('ba', 2, 1), ('bb', 2, 3), ('ad', 2, 8), ('ae', 2, 9),
                   ('bc', 3, 2), ('bd', 3, 4), ('af', 3, 7), ('be', 4, 2), ('bf', 4, 3), ('ag', 4, 8),
                   ('bg', 5, 1), ('bh', 5, 3), ('ah', 5, 6), ('bi', 6, 2), ('bj', 6, 4), ('ai', 6, 6), ('aj', 6, 7),
                   ('bk', 7, 0), ('bl', 7, 2), ('bm', 7, 3), ('ak', 7, 5)]
    test_data_elt = list()
    for t in test_data_tuple:
        test_data_elt.append(Element(t[0], tuple(t[1:])))
    # test_data_elt.sort(key=lambda e: e.idf)
    results = k_means(test_data_elt, 2, 5)


def k_means(elts, nb_classes, nb_steps):
    k = nb_classes
    #init
    centroids = list()
    cls_content = defaultdict(list)
    for cls_nb in range(k):
        cls_name = 'class_'+str(cls_nb)
        centroids.append(Element(cls_name, rd.choice(elts).data, cls='centroid'))
        cls_content[cls_name] = list()

    for itr in range(nb_steps):
        # assign
        cls_content.clear()
        for elt in elts:
            min_dist = float('inf')
            for c in centroids:
                dist = c.eucl_dist_to(elt)
                if dist < min_dist:
                    min_dist = dist
                    best_class = c.idf
            cls_content[best_class].append(elt)
            elt.cls = best_class
        # adjust
        for c in centroids:
            elts_in = list(cls_content[c.idf])
            if len(elts_in) == 0:
                sum_elts_data = 0
            else:
                sum_elts_data = list(elts_in[0].data)
                for elt in elts_in[1:]:
                    sum_elts_data = map(add, sum_elts_data, elt.data)
                sum_elts_data[:] = [e / float(len(elts_in)) for e in sum_elts_data]
                c.data = tuple(sum_elts_data)
        print('\nIteration %d' % itr)
        for c in centroids:
            cls_name = c.idf
            print('\n'+str(c))
            for e in cls_content[cls_name]:
                print(e)
    return cls_content


if __name__ == '__main__':
    main()