We can use NumPy to implement various types of matrices. We mostly use np.array() for every operation in this section.

Import the library:

import numpy as np

• Rectangular Matrix (or Classic Matrix)

A matrix with size m x n (m represents the number of rows, and n represents the number of columns).

# Rectangular Matrices
matrix1 = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [8, 9, 9, 9]])
print("Matrix 1:\\n", matrix1)
print("Shape:", matrix1.shape)

matrix2 = np.array([[1, 2, 3], [5, 6, 7], [8, 9, 9], [10, 11, 12]])
print("Matrix 2:\\n", matrix2)
print("Shape:", matrix2.shape)

Output:

Matrix 1:
 [[1 2 3 4]
 [5 6 7 8]
 [8 9 9 9]]
Shape: (3, 4)
Matrix 2:
 [[ 1  2  3]
 [ 5  6  7]
 [ 8  9  9]
 [10 11 12]]
Shape: (4, 3)

• Square Matrix

The most common type of matrices. A matrix with size m x n where m = n → Matrix of size n (or Matrix of order n).

A square matrix is also a special type of a rectangular matrix.

# Square Matrices
matrix1 = np.array([[1, 2, 3], [5, 6, 7], [8, 9, 9]])
print("Matrix 1:\\n", matrix1)
print("Shape:", matrix1.shape)

Output:

Matrix 1:
 [[1 2 3]
 [5 6 7]
 [8 9 9]]
Shape: (3, 3)

Optional: Check if the matrix is a square matrix by simply comparing the number of rows and the number of columns.

# Check if the matrix is square
if matrix1.shape[0] == matrix1.shape[1]:
    print("The matrix is square.")
else:
    print("The matrix is not square.")