# Normalizing Observations

## Preliminaries

```
# Load libraries
from sklearn.preprocessing import Normalizer
import numpy as np
```

## Create Feature Matrix

```
# Create feature matrix
X = np.array([[0.5, 0.5],
[1.1, 3.4],
[1.5, 20.2],
[1.63, 34.4],
[10.9, 3.3]])
```

## Normalize Observations

`Normalizer`

rescales the values on individual observations to have unit norm (the sum of their lengths is one).

```
# Create normalizer
normalizer = Normalizer(norm='l2')
# Transform feature matrix
normalizer.transform(X)
```

```
array([[ 0.70710678, 0.70710678],
[ 0.30782029, 0.95144452],
[ 0.07405353, 0.99725427],
[ 0.04733062, 0.99887928],
[ 0.95709822, 0.28976368]])
```