How to Standardize Data in Python

In this article, we will discuss standardization of data with scikit-learn.

Often when working with datasets for data science, we may need to standardize our dataset before fitting a machine learning model to it.

Standardization refers to shifting the distribution of each attribute to have a mean of zero and a standard deviation of one (unit variance)

# Standardize the data attributes for the sample dataset.
# here iris dataset is used

from sklearn.datasets import load_iris
from sklearn import preprocessing

# load the Iris dataset
iris = load_iris()
print(iris.data.shape)

# separate the data and target attributes
X = iris.data
y = iris.target

# standardize the data attributes
standardized_X = preprocessing.scale(X)