Hands-On Exploratory Data Analysis with Python

Discretization and binning

Often when working with continuous datasets, we need to convert them into discrete or interval forms. Each interval is referred to as a bin, and hence the name binning comes into play:

1. Let's say we have data on the heights of a group of students as follows:

height = [120, 122, 125, 127, 121, 123, 137, 131, 161, 145, 141, 132]

And we want to convert that dataset into intervals of 118 to 125, 126 to 135, 136 to 160, and finally 160 and higher.

2. To convert the preceding dataset into intervals, we can use the cut() method provided by the pandas library:

bins = [118, 125, 135, 160, 200]category = pd.cut(height, bins)category

The output of the preceding code is as follows:

[(118, 125], (118, 125], (118, 125], (125, ...