How to Work with Augmented Matrices in Python (Step-by-Step Tutorial)

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 Need to solve systems of linear equations in Python? In this tutorial, you'll learn how to build and manipulate augmented matrices step by step using NumPy—a key concept in linear algebra and data science. Code: https://ryanandmattdatascience.com/python-augmented-matrix/ 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Linear Algebra for Data Science Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4KiWaq3VkVC94aJaTBexqT4 Rank Matrix: https://youtu.be/bzJthYkqO2o Inverse Matrix: https://youtu.be/wfHaKrmMbFk Eigenanalysis: https://youtu.be/XmOOfij4Fhk In this linear algebra tutorial, we dive deep into augmented matrices and show you exactly how to build and manipulate them using Python. An augmented matrix combines the coefficients and constants of a linear system of equations into a single matrix, making it easier to find solutions and determine the number of solutions using rank operations. We start by explaining the background of augmented matrices and where you can apply them in solving systems of equations. Then, we walk through two complete manual calculations to show you how to construct an augmented matrix from scratch. After covering the theory, we jump straight into Python coding with NumPy to automate the process and make your life much easier. Throughout this video, you'll learn how to define coefficient matrices and constants matrices, merge them together using np.column_stack, and perform essential row operations including swapping rows, scaling rows by a constant, and adding or subtracting rows from each other. Each operation is demonstrated with real examples so you can see exactly how these transformations work in practice. By the end of this tutorial, you'll be comfortable creating augmented matrices in Python and applying row operations to solve linear systems. Whether you're a student learning linear algebra or a data scientist brushing up on fundamentals, this video gives you practical coding skills you can use immediately. Don't forget to subscribe for more data science and linear algebra tutorials uploaded weekly! TIMESTAMPS 00:00 Introduction to Augmented Matrix 01:22 Understanding the Coefficient Matrix 02:17 Creating the Constants Matrix 02:47 Combining into Augmented Matrix 03:14 Setting Up Python Code 04:00 Defining Matrices in NumPy 05:20 Building Example Two (3x3 Matrix) 06:40 Creating Augmented Matrices with Column Stack 07:22 Row Operations: Swapping Rows 08:17 Row Operations: Scalar Multiplication 09:00 Row Operations: Addition 09:57 Row Operations: Subtraction 11:17 Recap and Conclusion OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.