To **normalize** a matrix means to scale the values such that that the range of the row or column values is between 0 and 1.

The easiest way to normalize the values of a NumPy matrix is to use the normalize() function from the sklearn package, which uses the following basic syntax:

from sklearn.preprocessing import normalize #normalize rows of matrix normalize(x, axis=1, norm='l1') #normalize columns of matrix normalize(x, axis=0, norm='l1')

The following examples show how to use this syntax in practice.

**Example 1: Normalize Rows of NumPy Matrix**

Suppose we have the following NumPy matrix:

import numpy as np #create matrix x = np.arange(0, 36, 4).reshape(3,3) #view matrix print(x) [[ 0 4 8] [12 16 20] [24 28 32]]

The following code shows how to normalize the rows of the NumPy matrix:

**from sklearn.preprocessing import normalize
#normalize matrix by rows
x_normed = normalize(x, axis=1, norm='l1')
#view normalized matrix
print(x_normed)
[[0. 0.33333333 0.66666667]
[0.25 0.33333333 0.41666667]
[0.28571429 0.33333333 0.38095238]]**

Notice that the values in each row now sum to one.

- Sum of first row: 0 + 0.33 + 0.67 =
**1** - Sum of second row: 0.25 + 0.33 + 0.417 =
**1** - Sum of third row: 0.2857 + 0.3333 + 0.3809 =
**1**

**Example 2: Normalize Columns of NumPy Matrix**

Suppose we have the following NumPy matrix:

import numpy as np #create matrix x = np.arange(0, 36, 4).reshape(3,3) #view matrix print(x) [[ 0 4 8] [12 16 20] [24 28 32]]

The following code shows how to normalize the rows of the NumPy matrix:

**from sklearn.preprocessing import normalize
#normalize matrix by columns
x_normed = normalize(x, axis=0, norm='l1')
#view normalized matrix
print(x_normed)
[[0. 0.08333333 0.13333333]
[0.33333333 0.33333333 0.33333333]
[0.66666667 0.58333333 0.53333333]]**

Notice that the values in each column now sum to one.

- Sum of first column: 0 + 0.33 + 0.67 =
**1** - Sum of second column: 0.083 + 0.333 + 0.583 =
**1** - Sum of third column: 0.133 + 0.333 + 0.5333 =
**1**

**Additional Resources**

The following tutorials explain how to perform other common operations in Python:

How to Normalize Arrays in Python

How to Normalize Columns in a Pandas DataFrame