# How to Normalize Data in Python

**Normalization** means rescaling of real-valued numeric attributes into a 0 to 1 range.

**Data normalization** is used in machine learning to make model training less sensitive to the scale of features. This allows our model to converge to better weights and, in turn, leads to a more accurate model.

Python provides the `preprocessing`

library, which contains the `normalize`

function to normalize the data. It takes an array in as an input and normalizes its values between 0 and 1. It then returns an output array with the same dimensions as the input.

from sklearn import preprocessing import numpy as np a = np.random.random((2, 3)) a = a*20 print("Data = ", a) # normalize the data attributes normalized = preprocessing.normalize(a) print("Normalized Data = ", normalized)

Output:

Data = [[10.01766389 4.76582892 2.4596835 ] [10.59777787 3.13478939 13.15589421]] Normalized Data = [[0.88160724 0.41941807 0.21646512] [0.6167993 0.18244729 0.76568375]]

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