# Task 6: How to compute the eigenvalues and right eigenvectors of a given square array using Numpy? Write a program to compute the eigenvalues and right eigenvectors. Hint: Use __*linalg.eig*__ function Sample data : [[1,2],[2,3]] Sample output : Eigen values of the given square array: [-0.23606798 4.23606798] Right eigenvectors of the given square array: [[-0.85065081 -0.52573111] [ 0.52573111 -0.85065081]]

Sure, here's a solution using NumPy:

import numpy as np

# Define the square array
arr = np.array([[1, 2], [2, 3]])

# Compute eigenvalues and right eigenvectors
eigvals, eigvecs = np.linalg.eig(arr)

print("Eigen values of the given square array:\n", eigvals)
print("Right eigenvectors of the given square array:\n", eigvecs)

This will output:

Eigen values of the given square array:
 [-0.23606798  4.23606798]
Right eigenvectors of the given square array:
 [[-0.85065081 -0.52573111]
 [ 0.52573111 -0.85065081]]

Here, we use the `linalg.eig()` function to compute the eigenvalues and right eigenvectors of the square array. The function returns two arrays: the eigenvalues and the eigenvectors. The eigenvectors are normalized to unit length.

I hope this helps! Let me know if you have any other questions.