How to Find Determinant of a Matrix in NumPy using np.linalg.det ?

This numpy code example is a guide to finding matrix determinants in NumPy with np.linalg.det to simplify complex computations. | ProjectPro

Determining the determinant of a matrix is a fundamental operation in linear algebra, playing a crucial role in various mathematical and scientific applications. NumPy, a powerful numerical computing library in Python, provides a convenient function for calculating the determinant of a matrix through np.linalg.det. This numpy code example will help you understand how to find determinant of a matrix in NumPy using np.linalg.det, as well as alternative methods to find the determinant without NumPy.

Access YOLO OCR Character Recognition Project with Source Code

How to Find the Determinant of a Matrix in NumPy?

NumPy simplifies the process of finding the determinant of a matrix using the np.linalg.det function. Check out the step-by-step guide below - 

Step 1 - Importing Library

    import numpy as np

We have only imported numpy which is needed.

Step 2 - Creating Matrix

We have created two matrices of which we will find determinants.

    matrixA = np.array([[1, 2, 3, 23],

                       [4, 5, 6, 25],

                       [7, 8, 9, 28],

                       [10, 11, 12, 41]])

    matrixB = np.array([[2, 3, 4],

                       [5, 6, 9],

                       [7, 8, 1]])

Step 3 - Finding determinant using np.linalg.det

We will finding determinant by using the function np.linalg.det to find the determinant of both the matrix - 

    print(np.linalg.det(matrixA))

    print(np.linalg.det(matrixB))

So the output comes as

-4.862776847858206e-15

33.99999999999999

How to Calculate Determinant of Matrix in Python without NumPy? 

While NumPy offers a convenient solution, you may encounter scenarios where using NumPy is not an option. In such cases, you can calculate the determinant without NumPy, especially for smaller matrices. One common approach is using basic arithmetic operations and loops.

Here's an example of how to find the determinant of a matrix in Python without NumPy:

python matrix determinant without numpy

It is important to substitute the elements of your matrix for a11, a12, etc. This method, however, becomes impractical for larger matrices, highlighting the advantages of using NumPy for efficiency.

Example - How to Find Determinant of 3x3 Matrix in Python without NumPy? 

Let's take a practical example to calculate the determinant of a 3x3 matrix without using NumPy.

Consider the matrix:

find determinant without NumPy

Using the custom function determinant_3x3 as defined above:

This will output the determinant of the provided matrix.

How to find determinant of 3x3 matrix in python without NumPy

Get Hands-On Exposure to NumPy Matrix Operations with ProjectPro! 

Gaining excellent NumPy skills for matrix operations is crucial for data scientists and programmers. Practical experience is key to solidifying this knowledge, and ProjectPro, with its repository of over 270+ real-world projects in data science and big data, serves as an invaluable platform. Working on these diverse projects help individuals not only enhance their NumPy proficiency but also gain the practical insights needed to tackle complex challenges. ProjectPro offers a one-stop solution for hands-on learning, making it an essential resource for those looking to elevate their skills and excel in the dynamic fields of data science and big data. Explore the projects, gain practical experience, and chart a successful path in these ever-evolving domains.

Download Materials

What Users are saying..

profile image

Ameeruddin Mohammed

ETL (Abintio) developer at IBM
linkedin profile url

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

Relevant Projects

Build an End-to-End AWS SageMaker Classification Model
MLOps on AWS SageMaker -Learn to Build an End-to-End Classification Model on SageMaker to predict a patient’s cause of death.

Build Time Series Models for Gaussian Processes in Python
Time Series Project - A hands-on approach to Gaussian Processes for Time Series Modelling in Python

Deploying Machine Learning Models with Flask for Beginners
In this MLOps on GCP project you will learn to deploy a sales forecasting ML Model using Flask.

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

Build CNN Image Classification Models for Real Time Prediction
Image Classification Project to build a CNN model in Python that can classify images into social security cards, driving licenses, and other key identity information.

Build an Image Segmentation Model using Amazon SageMaker
In this Machine Learning Project, you will learn to implement the UNet Architecture and build an Image Segmentation Model using Amazon SageMaker

Build a Multi-Class Classification Model in Python on Saturn Cloud
In this machine learning classification project, you will build a multi-class classification model in Python on Saturn Cloud to predict the license status of a business.

Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.

Build Regression (Linear,Ridge,Lasso) Models in NumPy Python
In this machine learning regression project, you will learn to build NumPy Regression Models (Linear Regression, Ridge Regression, Lasso Regression) from Scratch.

End-to-End ML Model Monitoring using Airflow and Docker
In this MLOps Project, you will learn to build an end to end pipeline to monitor any changes in the predictive power of model or degradation of data.