In this tutorial, we will look at how to check if a numpy matrix (a 2d numpy array) is a diagonal matrix or not with the help of some examples.

### What is a diagonal matrix?

A square matrix (n x n matrix) is said to be a diagonal matrix if all the elements above and below the main diagonal are zero. That is, non-zero elements are only allowed on the main diagonal. The following image shows a diagonal matrix.

Notice that all the elements above and below the main diagonal are zero.

## How to check if a matrix is a diagonal matrix in Numpy?

To check if a matrix is a diagonal matrix or not, compare the original matrix with the diagonal matrix generated from the original matrix, if both the matrices are equal, we can say that the original matrix is a diagonal matrix.

Use the following steps –

- Pass the original matrix (array) as an argument to the
`numpy.diag()`

function. This will return a 1d array of the main diagonal elements. - Pass the resulting 1d array to the
`numpy.diag()`

function. This will return a diagonal matrix using the diagonal elements passed. - Compare if the above-generated matrix is the same as the original matrix. You can use the
`numpy.array_equal()`

function to compare two arrays for equality. If the matrices are equal, then, the original matrix is diagonal.

Note that if you pass a 2d array to the `numpy.diag()`

function, it returns the diagonal elements as a 1d array and if you pass a 1d array to the `numpy.diag()`

function, it returns the diagonal matrix using the given diagonal elements.

The following is the syntax –

import numpy as np # check if the matrix ar is diagonal np.array_equal(ar, np.diag(np.diag(ar)))

Let’s now look at some examples of using the above syntax –

**Data Science Programs By Skill Level**

**Introductory** ⭐

- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science

**Intermediate ⭐⭐⭐**

- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization

**Advanced ⭐⭐⭐⭐⭐**

- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science

**🔎 Find Data Science Programs 👨💻 111,889 already enrolled**

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

### Example – Check if a square matrix is a diagonal matrix

Let’s create a diagonal matrix and check if the above method identifies it correctly or not.

import numpy as np # create a diagonal matrix ar = np.array([ [1, 0, 0], [0, 2, 0], [0, 0, 3] ]) # check if the matrix ar is diagonal print(np.array_equal(ar, np.diag(np.diag(ar))))

Output:

True

We get `True`

as the output which indicates that the array `ar`

is a diagonal matrix. Notice that all the elements above and below the main diagonal, 1-2-3 are zero.

Let’s look at an example where the matrix is not a lower triangular matrix.

import numpy as np # create a matrix ar = np.array([ [1, 1, 0], [0, 2, 0], [0, 0, 3] ]) # check if the matrix ar is diagonal print(np.array_equal(ar, np.diag(np.diag(ar))))

Output:

False

We get `False`

as the output since in the above matrix there are non-zero elements above or below the main diagonal, that is, it is not a diagonal matrix.

## Additional Notes

Keep in mind that a diagonal matrix is defined as a square matrix with non-diagonal elements as zero. Now, if you use the above method on a non-square matrix, you’d get `False`

which is the expected response.

import numpy as np # create a matrix ar = np.array([ [1, 0, 0], [0, 2, 0] ]) # check if the matrix ar is diagonal print(np.array_equal(ar, np.diag(np.diag(ar))))

Output:

False

Now, if you look under the hood. You’ll find that, `np.diag(ar)`

returns a 1d array with `[1, 2]`

and then passing this to `numpy.diag()`

returns a 2×2 diagonal matrix `[[1, 0], [0, 2]]`

which is then compared with the original matrix which is a 2×3 matrix.

You might also be interested in –

- Numpy – Check if Matrix is a Lower Triangular Matrix
- Numpy – Check if Matrix is an Upper Triangular Matrix
- Numpy – Create a Diagonal Matrix (With Examples)

**Subscribe to our newsletter for more informative guides and tutorials. ****We do not spam and you can opt out any time.**