In NumPy, the **@** operator means matrix multiplication.

For instance, let’s multiply two NumPy arrays that represent 2 x 2 matrices:

import numpy as np A = np.array([[1, 2], [3, 4]]) B = np.array([[5, 6], [7, 8]]) product = A @ B print(product)

Output:

[[19 22] [43 50]]

If you are familiar with matrix multiplication, I’m sure this answers your questions.

However, if you do not know what matrix multiplication means, or if you are interested in how the **@** operator works under the hood, please stick around.

## What Is Matrix Multiplication

A matrix is an array of numbers. It is a really popular data structure in data science and mathematics.

*If you are unfamiliar with matrices, it is way too early to talk about matrix multiplication!*

Multiplying a matrix by a single number (scalar) is straightforward. Simply multiply each element in the matrix by the multiplier.

For example, let’s multiply a matrix by 2:

When you multiply a matrix by another matrix, things get a bit trickier.

To multiply two matrices, take the dot product between each row on the left-hand side matrix and the column on the right-hand side matrix.

Here are all the calculations made to obtain the result matrix:

- 2 x 3 + 0 x 4 =
**6** - 2 x 9 + 0 x 7 =
**18** - 1 x 3 + 9 x 4 =
**39** - 1 x 9 + 9 x 7 =
**72**

For a comprehensive explanation, feel free to check a more thorough guide on matrix multiplication here.

To keep it short, let’s move on to matrix multiplication in Python.

## Matrix Multiplication in Python

To write a Python program that multiplies matrices, you need to implement a matrix multiplication algorithm.

Here is the pseudocode algorithm for matrix multiplication for matrices **A** and **B** of size** N x M** and **M x P**.

- Input matrices
**A**and**B** - Specify a result matrix
**C**of the appropriate size - For
**i**from**1**to**N**:- For
**j**from**1**to**P**:- Let
**sum = 0** - For
**k**from**1**to**M**:- Set
**sum**←**sum +***A*×_{ik}*B*_{kj}

- Set
- Set
←*C*_{ij}**sum**

- Let

- For
- Return
**C**

Let’s implement this logic in our Python program where a nested list represents a matrix.

In this example, we multiply a 3 x 3 matrix by a 3 x 4 matrix to get a 3 x 4 result matrix.

# 3 x 3 matrix A = [ [12,7,3], [4 ,5,6], [7 ,8,9] ] # 3 x 4 matrix B = [ [5,8,1,2], [6,7,3,0], [4,5,9,1] ] N = len(A) M = len(A[0]) P = len(B[0]) # Pre-fill the result matrix with 0s. # The size of the result is 3 x 4 (N x P). result = [] for i in range(N): row = [0] * P result.append(row) for i in range(N): for j in range(P): for k in range(M): result[i][j] += A[i][k] * B[k][j] for r in result: print(r)

Output:

[114, 160, 60, 27] [74, 97, 73, 14] [119, 157, 112, 23]

As you might already know, matrix multiplication is quite a common operation performed on matrices.

Thus, it would be a waste of time to implement this logic in each project where you need matrix multiplication.

This is where the **@** operator comes to the rescue.

### The @ Operator in Python

As of Python 3.5, it has been possible to specify a matrix multiplication operator **@** to a custom class.

This happens by overriding the special method called **__matmul__**.

The idea is that when you call **@** for two custom objects, the **__matmul__** method gets triggered to calculate the result of matrix multiplication.

For instance, let’s create a custom class **Matrix**, and override the matrix multiplication method to it:

class Matrix(list): # Matrix multiplication A @ B def __matmul__(self, B): self = A N = len(A) M = len(A[0]) P = len(B[0]) result = [] for i in range(N): row = [0] * P result.append(row) for i in range(N): for j in range(P): for k in range(M): result[i][j] += A[i][k] * B[k][j] return result # Example A = Matrix([[2, 0],[1, 9]]) B = Matrix([[3, 9],[4, 7]]) print(A @ B)

Output:

[[6, 18], [39, 72]]

As you can see, now it is possible to call **@** between two matrix objects to multiply them.

And by the way, you could also directly call the **__matmul__** method instead of using the **@** shorthand.

# Example A = Matrix([[2, 0],[1, 9]]) B = Matrix([[3, 9],[4, 7]]) print(A.__matmul__(B))

Output:

[[6, 18], [39, 72]]

Awesome. Now you understand how matrix multiplication works, and how to override the **@** operator in your custom class.

Finally, let’s take a look at multiplying matrices with NumPy using the **@** operator.

## Matrix Multiplication with NumPy: A @ B

In data science, NumPy arrays are commonly used to represent matrices.

Because matrix multiplication is such a common operation to do, a NumPy array supports it by default.

This happens via the **@** operator.

In other words, somewhere in the implementation of the NumPy array, there is a method called **__matmul__** that implements matrix multiplication.

For example, let’s matrix-multiply two NumPy arrays:

import numpy as np A = np.array([[1, 2], [3, 4]]) B = np.array([[5, 6], [7, 8]]) product = A @ B print(product)

Output:

[[19 22] [43 50]]

This concludes our example in matrix multiplication and @ operator in Python and NumPy.

## Conclusion

Today you learned what is the** @** operator in NumPy and Python.

To recap, as of Python 3.5 it has been possible to multiply matrices using the **@** operator.

For instance, a NumPy array supports matrix multiplication with the **@** operator.

To override/implement the behavior of the **@** operator for a custom class, implement the **__matmul__** method to the class. The **__matmul__** method is called under the hood when calling **@** between two objects.

Thanks for reading. Happy coding!

## Further Reading

Best Python Data Science Courses

How to Transpose a NumPy Matrix

#### About the Author

- I'm an entrepreneur and a blogger from Finland. My goal is to make coding and tech easier for you with comprehensive guides and reviews.

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