# sklearn.preprocessing.normalize in Python

In this tutorial, you will learn how to normalize the given set of data in Python. Normalization is a process of scaling individual samples to have unit norm. We will also see an example code to understand the use of this operation.

### Introduction to Scikit-Learn

In this section, you’ll get a summary of the scikit-learn library. Scikit-learn is a machine learning package in python. In the scikit package, all the functions are written in optimized code, it is a very simple and efficient tool for data analysis and data mining. Before using sklearn package you have got to put in it by using the subsequent command in command prompt(cmd)

`pip install sklearn`

### normalize function

normalize is a function present in sklearn. preprocessing package. Normalization is used for scaling input data set on a scale of 0 to 1 to have unit norm. Norm is nothing but calculating the magnitude of the vector.

#### Syntax:

`sklearn.preprocessing.normalize(data,norm)`

#### Parameter:

data:- like input array or matrix of the data set.

norm:- type of norm we use.

### EXAMPLE OF NORMALIZE FUNCTION

STEP 1:- import clean module

`from sklearn import *`

In the above code, we import all the functions of the sklearn module. * means all the functions.

STEP 2:-provide the input data set

```inpt_data = [[1,2,3],
[4,5,6],
[7,8,9]]```

Here we provide data set in the form of the matrix. and stored it in variable inpt_data.

STEP 3:-Use normalize function to normalized the input data

`data_normalized = preprocessing.normalize(inpt_data,norm='l2)')`

In the above code, we use norm l2, you can also use norm l1. and we import all function of sklearn so here no need to write sklearn

STEP 4:-Print the normalized data

`data_normalized`

output:-

```array([[0.26726124, 0.53452248, 0.80178373],
[0.45584231, 0.56980288, 0.68376346],
[0.50257071, 0.57436653, 0.64616234]])```