# NumPy Normalize Matrix

This tutorial will discuss the method to normalize a matrix in Python.

## Normalize Matrix With the `numpy.linalg.norm()` Method in Python

The `numpy.linalg` library contains methods related to linear algebra in Python. The `norm()` method inside the `numpy.linalg` calculates the norm of a matrix. We can then use these norm values to normalize a matrix. The following code example shows us how we can normalize a matrix with the `norm()` method inside the `numpy.linalg` library.

``````import numpy as np

matrix = np.array([[1,2],[3,4]])

norms = np.linalg.norm(matrix, axis=1)
print(matrix/norms)
``````

Output:

``````[[0.4472136  0.4       ]
[1.34164079 0.8       ]]
``````

We first created our matrix in the form of a 2D array with the `np.array()` method. We then calculated the norm and stored the results inside the `norms` array with `norms = np.linalg.norm(matrix)`. In the end, we normalized the `matrix` by dividing it with the `norms` and printed the results.

The `norm()` method performs an operation equivalent to `np.sqrt(1**2 + 2**2)` and `np.sqrt(3**2 + 4**2)` on the first and second row of our matrix, respectively. It then allocates two values to our `norms` array, which are `[2.23606798 5.0]`. The matrix is then normalized by dividing each row of the `matrix` by each element of `norms`.

Write for us
Indexing One dimension NumPy arrays
DelftStack articles are written by software geeks like you. If you also would like to contribute to DelftStack by writing paid articles, you can check the write for us page.

## Related Article - NumPy Matrix

• NumPy Matrix Indexing
• NumPy Matrix Subtraction
• NumPy Matrix Vector Multiplication