# 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`.

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## Related Article - Numpy Matrix

• NumPy Matrix Indexing
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