Pycaret: shuffle in data

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from pycaret.clustering import *
from sklearn.datasets import make_blobs
from sklearn.model_selection import StratifiedKFold
mpl.rcParams['figure.dpi'] = 300


cols = ['column1', 'column2', 'column3',
        'column4', 'column5']
arr = make_blobs(n_samples=1000, n_features=5, random_state=20,
                 centers=3, cluster_std=1)
skfolds=StratifiedKFold(n_splits=3, shuffle=True, random_state=42)
data = pd.DataFrame(data=arr[0], columns = cols)
print(data.head())

#data.hist(bins=30, figsize=(12, 10), grid=False)
#plt.show()

#plt.figure(figsize=(10, 8))
#sns.heatmap(data.corr().round(decimals=2), annot=True)
#plt.show()

#plot_kws = {'scatter_kws': {'s': 2}, 'line_kws': {'color': 'red'}}
#sns.pairplot(data, kind='reg', vars=data.columns[:-1], plot_kws=plot_kws)
#plt.show()

cluster = setup(data, session_id=7652)
model = create_model('kmeans')

plot_model(model, 'elbow')

model = create_model('kmeans', num_clusters = 3)
plot_model(model, 'cluster')

#save_model(model, 'clustering_model')
results = assign_model(model)
print(results.head(10))