How To Categorize Age Group In Python

In this tutorial, we are going to learn how to categorize age group in Python. This is useful in various applications, such as data analysis, machine learning, and statistical modeling, where grouping similar age individuals together can provide valuable insights. We will use the popular pandas library to handle our data and accomplish our goal.

Step 1: Import pandas library

First, make sure you have the pandas library installed in your Python environment. If you don’t have it, you can install it using pip:
bash
pip install pandas
Now, we will import the pandas library.

1	import pandas as pd

Step 2: Create a sample dataset

We will create a sample dataset containing the ages of different individuals. You can replace this dataset with your own data if needed.

data = {'Name': ['John', 'Paul', 'George', 'Ringo', 'Mick', 'Keith', 'Charlie', 'Ronnie'],

'Age': [40, 77, 58, 80, 77, 78, 80, 74]}

df = pd.DataFrame(data)

Our sample dataset looks like this:

       Name  Age
0      John   40
1      Paul   77
2    George   58
3     Ringo   80
4      Mick   77
5     Keith   78
6   Charlie   80
7    Ronnie   74

Step 3: Define age categories and create a new column

Next, we need to define the age categories that we want to group our data into. In this example, we will use the following categories:
– Youth: 0 to 17 years
– Adult: 18 to 64 years
– Senior: 65 years and above

We can use the pd.cut() function from pandas to categorize our age data into the defined categories.

bins = [0, 17, 64, float('inf')]

labels = ['Youth', 'Adult', 'Senior']

df['AgeGroup'] = pd.cut(df['Age'], bins=bins, labels=labels)

Step 4: Display the updated dataset

Our dataset will now have an additional column showing the age group each individual belongs to. Let’s display the updated dataset.

print(df)

The output will look like this:

       Name  Age AgeGroup
0      John   40    Adult
1      Paul   77   Senior
2    George   58    Adult
3     Ringo   80   Senior
4      Mick   77   Senior
5     Keith   78   Senior
6   Charlie   80   Senior
7    Ronnie   74   Senior

Now, our dataset has been categorized based on age groups.

Full code

Below is the full code for this tutorial:

import pandas as pd