np.linalg.inv() for matrix inverse in NumPy - Mini Project: Build & Apply

Calculate Matrix Inverse Using np.linalg.inv()

📖 Scenario: Imagine you are working with a small business that needs to solve a system of linear equations to optimize their resources. One way to do this is by finding the inverse of a matrix representing their data.

🎯 Goal: You will create a matrix using NumPy, set up a configuration variable, calculate the inverse of the matrix using np.linalg.inv(), and then print the result.

📋 What You'll Learn

Create a 2x2 NumPy array called matrix with values [[4, 7], [2, 6]]

Create a variable called identity_check to store the product of matrix and its inverse

Use np.linalg.inv() to calculate the inverse of matrix and store it in inverse_matrix

Print the inverse_matrix and identity_check to verify the inverse calculation

💡 Why This Matters

🌍 Real World

Matrix inversion is used in many fields like engineering, physics, computer graphics, and economics to solve systems of equations and model real-world problems.

💼 Career

Understanding matrix operations and using NumPy for linear algebra is essential for data scientists, machine learning engineers, and researchers working with numerical data.

1

Create the matrix

Create a 2x2 NumPy array called matrix with the exact values [[4, 7], [2, 6]].

NumPy

import numpy as np
# Create a 2x2 NumPy array called matrix with values [[4, 7], [2, 6]]
# Your code here

Need a hint?

Use np.array() to create the matrix with the given values.

2

Set up identity check variable

Create a variable called identity_check and set it to None for now. This will later store the product of the matrix and its inverse.

NumPy

import numpy as np
matrix = np.array([[4, 7], [2, 6]])
# Create a variable called identity_check and set it to None
# Your code here

Need a hint?

Initialize identity_check with None as a placeholder.

3

Calculate the inverse matrix

Use np.linalg.inv() to calculate the inverse of matrix and store it in a variable called inverse_matrix. Then calculate the product of matrix and inverse_matrix and store it in identity_check.

NumPy

import numpy as np
matrix = np.array([[4, 7], [2, 6]])
identity_check = None
# Calculate the inverse of matrix using np.linalg.inv() and store in inverse_matrix
# Calculate the product of matrix and inverse_matrix and store in identity_check
# Your code here

Need a hint?

Use np.linalg.inv(matrix) to get the inverse. Use np.dot() to multiply matrices.

4

Print the inverse and identity check

Print the variables inverse_matrix and identity_check to see the inverse matrix and verify the product is close to the identity matrix.

NumPy

import numpy as np
matrix = np.array([[4, 7], [2, 6]])
identity_check = None
inverse_matrix = np.linalg.inv(matrix)
identity_check = np.dot(matrix, inverse_matrix)
# Print inverse_matrix and identity_check
# Your code here

Need a hint?

Use two print() statements, one for inverse_matrix and one for identity_check.