Sample Code which represent Kmeans algo and plots the result using matplotlib. Dataset was made using make_blobs(sklearn.dataset.make_blobs)

Kmeans.py

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.datasets import make_blobs
# data is a tuple with 2 columns, one is the dataset that we made, and second is the labels representing centre.
data = make_blobs(n_samples = 200, n_features=2, centers=3, cluster_std=1.5)

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.fit(data[0])

# kmeans.labels_ <- These are the labels to the cluster's centroid
# Since we actually do know the labels here (data[1]), we can check how good our kmeans is in finding the clusters!

fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(12, 8))
ax1.set_title('Kmeans clusters')
ax1.scatter(data[0][:, 0], data[0][:, 1], c=kmeans.labels_, cmap='rainbow')
ax2.set_title('Actual dataset using labels from make_blobs')
ax2.scatter(data[0][:, 0], data[0][:, 1], c=data[1], cmap='rainbow')