Let’s learn about how to normalize an array in Numpy Python library. We will use linalg norm function for that purpose.

## Steps to normalize an array in Numpy

To normalize an array in Numpy you need to divide your array by np.linalg.norm of your array. Just take a look at below example or normalization.

import numpy as np my_array = np.array([[1, 3, 5], [7, 9, 11], [13, 15, 17]]) print(f"My array: \n{my_array}") my_normalized_array = my_array / np.linalg.norm(my_array) print(f"My normalized array: \n{my_normalized_array}")

Python returned normalized array as an output.

In this code, we start with the my_array and use the np.linalg.norm function to calculate the L2 norm of the array. We then divide each element in my_array by this L2 norm to obtain the normalized array, my_normalized_array.

Running this code results in a normalized array where the values are scaled to have a magnitude of 1.0.

By normalizing your data, you can ensure that all features have the same scale, which is often a crucial step in various machine learning algorithms and data analysis tasks. NumPy’s np.linalg.norm function makes this process efficient and straightforward.