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# How to Normalize a NumPy Matrix (With Examples)

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