How to compute the eigenvalues and right eigenvectors of a given square array using NumPY? (original) (raw)

Last Updated : 15 Jul, 2025

In this article, we will discuss how to compute the eigenvalues and right eigenvectors of a given square array using NumPy library.

Example:

Suppose we have a matrix as: [[1,2], [2,3]]

Eigenvalue we get from this matrix or square array is: [-0.23606798 4.23606798]

Eigenvectors of this matrix are: [[-0.85065081 -0.52573111], [ 0.52573111 -0.85065081]]

To know how they are calculated mathematically see thisCalculation of EigenValues and EigenVectors. In the below examples, we have used numpy.linalg.eig() to find eigenvalues and eigenvectors for the given square array.

Syntax: numpy.linalg.eig()

Parameter: An square array.

Return: It will return two values first is eigenvalues and second is eigenvectors.

Example 1:

Python3 `

importing numpy library

import numpy as np

create numpy 2d-array

m = np.array([[1, 2], [2, 3]])

print("Printing the Original square array:\n", m)

finding eigenvalues and eigenvectors

w, v = np.linalg.eig(m)

printing eigen values

print("Printing the Eigen values of the given square array:\n", w)

printing eigen vectors

print("Printing Right eigenvectors of the given square array:\n" v)

Output:

Printing the Original square array: [[1 2] [2 3]] Printing the Eigen values of the given square array: [-0.23606798 4.23606798] Printing Right eigenvectors of the given square array: [[-0.85065081 -0.52573111] [ 0.52573111 -0.85065081]]

Example 2: