This is an example of a simple implementation of the k means algorithm.

k_means.py

from copy import deepcopy
from math import ceil, sqrt
from random import randint

import numpy as np
from matplotlib import pyplot as plt


class Point(object):
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __str__(self):
        return 'Coordinates of this point: x: {}, y: {}'.format(self.x, self.y)

    def __repr__(self):
        return self.__str__()

    def get_distance_from(self, point):
        return sqrt(pow(point.x - self.x, 2) + pow(point.y - self.y, 2))


def generate_random_points(points_count, min, max):
    return [Point(randint(min, max), randint(min, max)) for _ in range(0, points_count)]


# ─── CODE ───────────────────────────────────────────────────────────────────────

POINTS_COUNT = 100
INITIAL_BARYCENTERS_COUNT = 3
BARYCENTER_COLORS = ['#ff0000', '#00ff00', '#0000ff']
MIN_X_Y = 0
MAX_X_Y = 1000
MAX_STEPS = 4

points = generate_random_points(POINTS_COUNT, MIN_X_Y, MAX_X_Y)
initial_barycenters = generate_random_points(
    INITIAL_BARYCENTERS_COUNT, MIN_X_Y, MAX_X_Y)
barycenters = deepcopy(initial_barycenters)

clusters = [[] for cluster in range(INITIAL_BARYCENTERS_COUNT)]


# ─── K-MEANS ────────────────────────────────────────────────────────────────────

for step in range(MAX_STEPS):
    # ─── CLUSTERIZATION ─────────────────────────────────────────────────────────────
    for point_index, point in enumerate(points):
        closest_index = 0
        for barycenter_index, barycenter in enumerate(barycenters):
            if point.get_distance_from(barycenter) < point.get_distance_from(barycenters[closest_index]):
                closest_index = barycenter_index
        clusters[closest_index].append(point)

    # ─── DISPLAY ────────────────────────────────────────────────────────────────────
    plt.subplot(ceil(MAX_STEPS/2), 2, step+1)
    plt.axis('equal')
    plt.title('Step {}'.format(step))
    plt.scatter([point.x for point in barycenters], [
                point.y for point in barycenters], c=BARYCENTER_COLORS, edgecolors='#000000')

    for cluster_index, cluster in enumerate(clusters):
        plt.scatter([point.x for point in cluster], [
                    point.y for point in cluster], c=BARYCENTER_COLORS[cluster_index])

    # ─── NEW BARYCENTERS CALCULATION ────────────────────────────────────────────────
    for cluster_index, cluster in enumerate(clusters):
        barycenters[cluster_index] = Point(sum([point.x for point in cluster]) / len(cluster),
                                           sum([point.y for point in cluster]) / len(cluster))

    # Here we go again
    
plt.show()