Matrix multiplication in Python: a step-by-step guide 2023 - Dropout Developer
Matrix multiplication in Python

Matrix multiplication in Python: a step-by-step guide 2023

Matrix multiplication is an essential mathematical operation in a variety of fields, including physics, computer science, and engineering. In Python, matrix multiplication is a straightforward process that can be achieved using just a few lines of code. In this article, we will explore the topic of matrix multiplication in Python, including what it is, how it works, and how to perform it using the NumPy library.

What is Matrix Multiplication in Python?

Matrix multiplication is a fundamental operation in linear algebra that involves multiplying two matrices together to produce a third matrix. The resulting matrix is the product of the first two matrices and has a size equal to the number of rows in the first matrix and the number of columns in the second matrix. Matrix multiplication is not commutative, meaning that the order of the matrices matters when performing the operation.

How Does Matrix Multiplication Work?

To perform matrix multiplication, we multiply the elements of each row of the first matrix by the corresponding elements of each column in the second matrix and add up the results. For example, suppose we have two matrices, A and B, where A has dimensions m x n and B has dimensions n x p. We can represent the element in the ith row and jth column of the resulting matrix, C, as follows:

C[i,j] = sum(A[i,k] * B[k,j] for k in range(n))

This equation represents the dot product of the ith row of A and the jth column of B. We compute the dot product for each element in the resulting matrix C to obtain the final product of A and B.

Performing Matrix Multiplication in Python

In Python, we can perform matrix multiplication using the NumPy library, which provides a convenient and efficient way to work with arrays and matrices. Here is an example of how to perform matrix multiplication in Python using NumPy:

import numpy as np

# define the first matrix
matrix1 = np.array([[1, 2, 3],
                    [4, 5, 6],
                    [7, 8, 9]])

# define the second matrix
matrix2 = np.array([[1, 0, 0],
                    [0, 1, 0],
                    [0, 0, 1]])

# perform matrix multiplication
result =, matrix2)

# print the result

In this example, we first define two matrices, matrix1 and matrix2, using NumPy arrays. We then perform matrix multiplication using the dot() function, which takes the two matrices as input and returns their product. The resulting matrix is stored in the result variable and printed to the console.


In conclusion, matrix multiplication is a crucial operation in linear algebra and is used in a variety of fields. In Python, matrix multiplication is a simple process that can be performed using just a few lines of code with the help of the NumPy library. By understanding the underlying principles of matrix multiplication and how to perform it in Python, you can enhance your skills in various domains and tackle more complex problems.

Here are some additional resources that can be helpful for learning more about matrix multiplication in Python:

  1. The official NumPy documentation provides an extensive guide to using the library, including matrix multiplication:
  2. Real Python is a website that provides tutorials and articles on a variety of Python topics, including matrix multiplication:
  3. The Datacamp blog features a tutorial on matrix multiplication in Python, as well as other linear algebra topics:
  4. The book “Python for Data Science Handbook” by Jake VanderPlas includes a comprehensive guide to using NumPy for matrix multiplication:

By utilizing these resources, you can deepen your understanding of matrix multiplication in Python and expand your knowledge in this essential mathematical operation.

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