How to make an animation show every change in the dataset? With matplotlib

I'm using matplotlib to show my data set in 3d. But I'd like to show each change to the dataset in a 3D chart. And create an animation of it or maybe save the animation. Follow the code I'm using:

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
import random
import time

K = 3
ATTRIBUTE_COUNT = 4
_random = random.seed()
data = []
data_classes = {}


def manhattan_distance(point_x, point_y):
    distance, length = 0, len(point_x)
    for i in range(length):
        distance += abs( point_x[i] - point_y[i])
    return distance

def get_centroid(cluster):
    summ = []
    for j in range(ATTRIBUTE_COUNT):
        summ.append(0)
    for element in cluster:
        for index, attribute in enumerate(element):
            summ[index] += attribute
    if len(cluster) != 0:
        new_centroid = list(map(lambda x: x/len(cluster), summ))
        return new_centroid
    else:
        return None

#read data set from file
with open('iris.data') as f:
    for line in f:
        line_data = line.split(',I')
        classe_name = 'I' + line_data[1]
        point = list((map(float, line_data[0].split(','))))
        data.append(point)
        data_classes[str(point)] = classe_name.rstrip()

# I am just showing the original data set.
aux = [[],[],[],[]]
for point in data:
    aux[0].append(point[0])
    aux[1].append(point[1])
    aux[2].append(point[2])
    aux[3].append(point[3])
fig = plt.figure(1)
fig.suptitle('Data Set')
ax = fig.gca(projection='3d')
ax.scatter(aux[0],aux[1],aux[2], c='b', s=[25 * x for x in aux[3]],cmap=plt.hot())
plt.show()

centroids = []
centroids = random.sample(data, K)
changed_centroid = True

while(changed_centroid):
    clusters = []
    new_centroids = []
    for i in range(K):
        clusters.append([])

    for element in data:
        distances = []
        for centroid in centroids:
            distances.append(manhattan_distance(element, centroid))
        index = distances.index(min(distances))#index of minimuim distance
        clusters[index].append(element)

    #for each cluster I would like to show it in a different color.
    for cluster in clusters:
        new_centroid = get_centroid(cluster)
        new_centroids.append(new_centroid)

    if new_centroids == centroids:
        changed_centroid = False
    centroids = new_centroids

    #for centroid in centroids:
       #for each centroid I would like to show like a triangle.

#show result of classification
for index, cluster in enumerate(clusters):
    print('classe', index ,'------------------------------')
    for point in cluster:
        print(data_classes[str(point)], end=' ')
    print('\n')