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

### When is a matrix invertible?

A square matrix, for example, `M`

is said to be invertible, if there exists a matrix `N`

such that, `MN = NM = I`

where `I`

is an identity matrix. The following image shows an invertible matrix.

Alternatively, we can say that `N`

is equal to the inverse of the matrix `M`

. That is, a matrix is invertible if you can take its inverse.

A square matrix that is not invertible is called a singular matrix.

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

To check if a matrix is invertible or not in Numpy, check if it has a non-zero determinant.

If a matrix has a non-zero determinant (the determinant is not zero), we can say that the matrix is invertible. Use the `numpy.linalg.det()`

function to compute the determinant of a matrix. The following is the syntax –

import numpy as np # check if matrix ar is invertible not np.isclose(np.linalg.det(ar), 0)

Let’s now look at the above method with the help of some examples. First, we will create two matrices that we will use throughout this tutorial, one invertible and the other non-invertible.

import numpy as np # create matrices ar1 = np.array([ [1, 2, 3], [0, 4, 5], [0, 0, 6] ]) ar2 = np.array([ [2, 4, 6], [2, 0, 2], [6, 8, 14] ])

Here, the matrix `ar1`

is invertible and the matrix `ar2`

is non-invertible.

**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 the determinant is non-zero

In this method, we calculate the determinant of the matrix using the `numpy.linalg.det()`

function and check whether it is non-zero or not. If the determinant is non-zero, we say the matrix is invertible.

Let’s apply this method to the matrices created above.

# check if ar1 is invertible print(np.linalg.det(ar1) != 0) # check if ar2 is invertible print(np.linalg.det(ar2) != 0)

Output:

True True

We get `True`

for both matrices, which is **not the correct answer**. The matrix `ar1`

is invertible, so the first `True`

is okay, but the matrix `ar2`

is singular and this method should give `False`

but we get `True`

.

Why is this happening? Let’s print out the determinant for `ar2`

.

np.linalg.det(ar2)

Output:

7.105427357600985e-15

We get a very small value which is not exactly zero.

To work around this, you can use the `numpy.isclose()`

function which lets you define a tolerance to determine how close two values need to be to be considered equal. By default, the tolerance is set to 1e-05, which means that two values are considered equal if they are within 0.00001 of each other. You can adjust this tolerance by passing a different value to the `rtol`

(relative tolerance) or `atol`

(absolute tolerance) parameters of `numpy.isclose()`

.

Using the `numpy.isclose()`

function to compare the results from `numpy.linalg.det()`

to zero helps resolve the above issue.

# check if ar1 is invertible print(not np.isclose(np.linalg.det(ar1), 0)) # check if ar2 is invertible print(not np.isclose(np.linalg.det(ar2), 0))

Output:

True False

We now get the correct answer. In the above example, we first check whether the determinant is zero (very close to zero) or not, if it isn’t, we say that the matric is invertible.

You might also be interested in –

- How to check if a matrix is symmetric in Numpy?
- How to check if a matrix is a square matrix in Numpy?
- How to check if a matrix is a diagonal matrix in Numpy?

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