In machine learning, Normalizing is a must. It is a technique in data preprocessing to change the value of the numerical columns in the dataset to a common scale. Its mostly require when the features of the datasets have different ranges. In this entire tutorial, I will show you how to normalize a NumPy array.

## Methods to Normalize a Numpy Array

In order to get a complete understanding of this concept execute the steps that I have defined here. I am doing all the work on Pycharm IDE.

## Step 1: Import the necessary libraries

The most important step is to import all the required libraries before continuing the execution.

import numpy as np from sklearn.preprocessing import normalize import transformations as tr

## Step 2: Create a Numpy array

Here for the demonstration purpose, I am creating a random NumPy array. You can get different values of the array in your computer.

array = np.random.rand(50) * 5

*method has been used to generates the number and each value is multiplied by 5. The output is below.*

**random.rand()**## Step 3: Use the Methods defined here

### Method 1: Using the Numpy Python Library

To use this method you have to divide the NumPy array with the * numpy.linalg.norm() *method. It returns the norm of the matrix form. You can read more about the Numpy norm.

normalize1 = array / np.linalg.norm(array) print(normalize1)

### Method 2: Using the sci-kit learn Python Module

The second method to normalize a NumPy array is through the sci-kit python module. Here you have to import normalize object from the * sklearn. preprocessing* and pass your array as an argument to it. I have already imported it step 1.

normalize2 = normalize(array[:, np.newaxis], axis=0).ravel() print(normalize2)

Here * np.newaxis* is used to increase the dimension of the array. That is if the array is 1D then it will make it to 2D and so on.

And also passing axis = 0 to do all the tasks along rows. The * ravel() *method returns the contiguous flattened array. You can read more about it on numpy ravel official documentation.

### Method 3: Using the Transformation Module

The third method to normalize a NumPy array is using transformations. You can easily transform the NumPy array to the unit vector using the * unit_vector()* method. Use the code below.

normalize3 = tr.unit_vector(array) print(normalize3)

These are the best method to normalize a NumPy array. I will keep adding the new methods I will find. If you have any other methods to normalize a NumPy array then you can contact us to review and add here.

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