k means in python

import numpy as np

import pandas as pd

from scipy.spatial.distance import pdist, squareform

num_instances = int(input("Enter number of instances (rows): "))

num_attributes = int(input("Enter number of attributes (columns): "))

data = []

for i in range(num_instances):

    print(f"\nEnter values for Instance {i+1}:")

    row = []

    for j in range(num_attributes):

        value = float(input(f" Attribute {j+1}: "))

        row.append(value)

    data.append(row)

df = pd.DataFrame(data, columns=[f'Attribute{j+1}' for j in range(num_attributes)])

print("\nDataset:")

print(df)

dissimilarity = pdist(df.values, metric='euclidean')

dissimilarity_matrix = squareform(dissimilarity)

instance_labels = [f'Instance{i+1}' for i in range(num_instances)]

dissimilarity_df = pd.DataFrame(dissimilarity_matrix,     index=instance_labels,        columns=instance_labels)

print("\nDissimilarity Matrix:")

print(dissimilarity_df)

"""

OUTPUT:

Enter number of instances (rows): 4

Enter number of attributes (columns): 2

Enter values for Instance 1:

  Attribute 1: 2

  Attribute 2: 3

Enter values for Instance 2:

  Attribute 1: 5

  Attribute 2: 8

Enter values for Instance 3:

  Attribute 1: 1

  Attribute 2: 2

Enter values for Instance 4:

  Attribute 1: 6

  Attribute 2: 7

Dataset:

   Attribute1 Attribute2

0 2.0 3.0

1 5.0 8.0

2 1.0 2.0

3 6.0 7.0

Dissimilarity Matrix:

           Instance1 Instance2 Instance3 Instance4

Instance1 0.000000 5.830952 1.414214 5.656854

Instance2 5.830952 0.000000 7.211103 1.414214

Instance3 1.414214 7.211103 0.000000 7.071068

Instance4 5.656854 1.414214 7.071068 0.000000

"""