# 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`

.