Binning continuous variables in Data Analysis Python - Mini Project: Build & Apply

Binning continuous variables

📖 Scenario: You work as a data analyst for a retail company. You have a list of customer ages and want to group these ages into categories like 'Young', 'Middle-aged', and 'Senior' to better understand customer segments.

🎯 Goal: Build a Python program that bins a list of customer ages into three categories using defined age ranges.

📋 What You'll Learn

Create a list of customer ages with exact values

Define bin edges for age groups

Use a list comprehension to assign each age to a bin label

Print the list of age group labels

💡 Why This Matters

🌍 Real World

Binning continuous variables helps simplify data analysis by grouping continuous data into meaningful categories, like age groups for marketing.

💼 Career

Data analysts and scientists often bin continuous data to prepare it for reports, visualizations, or machine learning models.

1

Create the list of customer ages

Create a list called ages with these exact values: 22, 35, 58, 45, 18, 67, 30, 50.

Data Analysis Python

# Create the list of ages
# Your code here

Hint

Use square brackets to create a list and separate values with commas.

2

Define the bin edges and labels

Create a list called bins with values 0, 30, 60, and 100. Then create a list called labels with strings 'Young', 'Middle-aged', and 'Senior'.

Data Analysis Python

ages = [22, 35, 58, 45, 18, 67, 30, 50]
# Define bins and labels
# Your code here

Hint

Bins define the edges of age groups. Labels are the names for each group.

3

Assign each age to a bin label using list comprehension

Create a list called age_groups using a list comprehension. For each age in ages, assign the label 'Young' if age < 30, 'Middle-aged' if 30 <= age < 60, and 'Senior' if age >= 60.

Data Analysis Python

ages = [22, 35, 58, 45, 18, 67, 30, 50]
bins = [0, 30, 60, 100]
labels = ['Young', 'Middle-aged', 'Senior']
# Create age_groups list using list comprehension
# Your code here

Hint

Use nested if-else inside the list comprehension to assign labels based on age.

4

Print the list of age group labels

Write a print statement to display the age_groups list.

Data Analysis Python

ages = [22, 35, 58, 45, 18, 67, 30, 50]
bins = [0, 30, 60, 100]
labels = ['Young', 'Middle-aged', 'Senior']
age_groups = [
    'Young' if age < 30 else 'Middle-aged' if age < 60 else 'Senior'
    for age in ages
]
# Print the age_groups list
# Your code here

Hint

Use print(age_groups) to show the result.