NumPy — Linear Algebra

Matrix multiplication

import numpy as np

A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

# Element-wise
C = A * B

# Matrix multiplication
C = A @ B
C = np.dot(A, B)

Transpose

arr = np.array([[1, 2], [3, 4]])
print(arr.T)

Inverse

A = np.array([[1, 2], [3, 4]])
A_inv = np.linalg.inv(A)
print(A_inv)

Determinant

A = np.array([[1, 2], [3, 4]])
det = np.linalg.det(A)
print(f"Determinant: {det}")

Solve linear system

A = np.array([[3, 1], [1, 2]])
b = np.array([9, 8])
x = np.linalg.solve(A, b)
print(f"Solution: {x}")

Eigenvalues

A = np.array([[4, 2], [1, 3]])
eigenvalues, eigenvectors = np.linalg.eig(A)
print(f"Eigenvalues: {eigenvalues}")

Mini Practice

1. Multiply matrices
2. Find inverse
3. Calculate determinant
4. Solve linear system

Up Next

Continue with Broadcasting - Array operations.