np.linalg.solve() for linear systems in NumPy - Mini Project: Build & Apply

Solving Linear Systems with np.linalg.solve()

📖 Scenario: You are working as a data analyst and need to solve a system of linear equations to find unknown values. This is common in many real-world problems like budgeting, resource allocation, or physics calculations.

🎯 Goal: Learn how to use np.linalg.solve() to find the solution of a system of linear equations represented by matrices.

📋 What You'll Learn

Create a matrix A representing the coefficients of the system

Create a vector b representing the constants on the right side

Use np.linalg.solve() to find the solution vector x

Print the solution vector x

💡 Why This Matters

🌍 Real World

Solving linear systems is essential in engineering, physics, economics, and data science to find unknown values from multiple equations.

💼 Career

Data scientists and analysts often solve linear systems when modeling relationships, optimizing resources, or performing regression analysis.

1

Create the coefficient matrix A

Create a 2x2 numpy array called A with these exact values: [[3, 1], [1, 2]].

NumPy

import numpy as np
# Create the matrix A with values [[3, 1], [1, 2]]
# Your code here

Need a hint?

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

2

Create the constants vector b

Create a numpy array called b with these exact values: [9, 8].

NumPy

import numpy as np
A = np.array([[3, 1], [1, 2]])
# Create the vector b with values [9, 8]
# Your code here

Need a hint?

Use np.array() to create the vector with the exact values.

3

Solve the system using np.linalg.solve()

Use np.linalg.solve() with A and b to create a variable called x that holds the solution vector.

NumPy

import numpy as np
A = np.array([[3, 1], [1, 2]])
b = np.array([9, 8])
# Solve the system and store the result in x
# Your code here

Need a hint?

Call np.linalg.solve() with A and b as arguments.

4

Print the solution vector x

Print the variable x to display the solution of the system.

NumPy

import numpy as np
A = np.array([[3, 1], [1, 2]])
b = np.array([9, 8])
x = np.linalg.solve(A, b)
# Print the solution vector x
# Your code here

Need a hint?

Use print(x) to show the solution vector.