Data scaling and normalization

A generic dataset (we assume here that it is always numerical) is made up of different values that can be drawn from different distributions, having different scales and, sometimes, there are also outliers. A machine learning algorithm isn't naturally able to distinguish among these various situations, and therefore, it's always preferable to standardize datasets before processing them. A very common problem derives from having a non zero mean and a variance greater than 1. In the following graph, there's a comparison between a raw dataset and the same dataset scaled and centered:

Original dataset (left) and the scaled one (right)

This result can be achieved by using the StandardScaler class (which implements feature-wise scaling):

from sklearn.preprocessing import StandardScaler

ss = StandardScaler()
scaled_data = ss.fit_transform(data)

It's possible to specify if the scaling process must include both mean and standard deviation by using the with_mean=True/False...