Python - Matrix creation of n*n

Matrices are fundamental structures in programming and are widely used in various domains including mathematics, machine learning, image processing, and simulations. Creating an n×n matrix efficiently is an important step for these applications. This article will explore multiple ways to create such matrices in Python.

Using Nested List Comprehension

List comprehension is a concise way to create a matrix where each element is generated using a loop. It is suitable for creating matrices with uniform values or patterns.

Example:

#specify the size of matrix
n = 4

#creates a matrix filled with 0s
matrix = [[0 for _ in range(n)] for _ in range(n)]
print(matrix)

[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]

• Here, the outer list comprehension creates rows and the inner list comprehension generates columns.

Let's see some other methods on how to create n*n matrix

Using Loops

A nested loop allows more control over how each element of the matrix is generated, making this method ideal for generating custom matrices. It is mostly used when we have to generate dynamic or custom values for matrix elements.

Example:

# n*n matrix with incrementing numbers
n = 3

# Initialize an empty list to store the matrix
matrix = []

# Outer loop to create 'n' rows
count = 1
for i in range(n):
  
  # Initialize an empty list for each row
    row = []
    
    # Inner loop to fill each row with 'n' incrementing numbers
    for j in range(n):
      
        # Add the current value of 'count' to the row
        row.append(count)
        
         # Increment the count for the next number
        count += 1
    matrix.append(row)

print(matrix)

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

Using numpy

The numpy library is designed for numerical computations and provides efficient ways to create matrices. It optimizes memory and computation for large datasets making it the best choice for high performance applications.

Example:

import numpy as np

#specify the size of the matrix
n = 4

#creates an n*n matrix 
matrix = np.zeros((n, n), dtype=int)
print(matrix)

[[0 0 0 0]
 [0 0 0 0]
 [0 0 0 0]
 [0 0 0 0]]

Using pandas DataFrame

pandas is a library for data manipulation and provides DataFrame for creating labeled 2D structures. It adds context to the matrix, making it more readable and useful for data analysis. It is Ideal for creating labeled matrices for data analysis tasks.

Example:

import pandas as pd

# Create a 3x3 matrix with labeled rows and columns
n = 3
matrix = pd.DataFrame([[i * n + j + 1 for j in range(n)] for i in range(n)],
                      columns=['A', 'B', 'C'])
print(matrix)

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9