Let’s say we have a column in a DataFrame that contains numerical values, e.g., age. Now, we want to discretize or bin the age to various age groups. For example, those aged 0 to 5 years should be labeled as toddlers. Those aged 5 to 18 years should be labeled as young. Those who are more than 18, but less than 60 years should be labeled as adults. And the rest should be labeled as a senior.

We can use the cut() function to discretize or bin numerical values in a column using the pandas Python library. Let’s have a look at this with an example.

Let’s read the titanic dataset from a CSV file and have a look at the first few lines of the dataset.

import pandas

df = pandas.read_csv("titanic.csv")
print(df.head())

The output will be:

     survived  pclass     sex   age  ...  deck  embark_town  alive  alone
886         0       2    male  27.0  ...   NaN  Southampton     no   True
887         1       1  female  19.0  ...     B  Southampton    yes   True
888         0       3  female   NaN  ...   NaN  Southampton     no  False
889         1       1    male  26.0  ...     C    Cherbourg    yes   True
890         0       3    male  32.0  ...   NaN   Queenstown     no   True

So, there is a column called age that contains numerical values. Let’s discretize the ages as per the age groups as mentioned previously. We can use the following Python code for that purpose: