Binning with cut() and qcut() in Pandas - Mini Project: Build & Apply

Binning with cut() and qcut()

📖 Scenario: You work in a retail company. You have a list of customer ages and want to group them into age categories to understand your customer base better.

🎯 Goal: Group the customer ages into bins using pandas.cut() and pandas.qcut() to create meaningful age categories and quantile-based groups.

📋 What You'll Learn

Create a pandas Series called ages with the exact values given.

Create a list called age_bins with the exact bin edges.

Use pandas.cut() with ages and age_bins to create a categorical variable called age_groups.

Use pandas.qcut() with ages to create 4 quantile-based groups called quantile_groups.

Print the age_groups and quantile_groups to see the binning results.

💡 Why This Matters

🌍 Real World

Binning ages helps companies understand customer segments for marketing and product planning.

💼 Career

Data analysts and scientists often use binning to simplify continuous data into categories for reports and models.

1

Create the ages data

Create a pandas Series called ages with these exact values: [22, 25, 47, 35, 46, 55, 52, 23, 43, 51].

Pandas

import pandas as pd
# Create the pandas Series called ages with the exact values
# Your code here

Need a hint?

Use pd.Series() to create the series with the given list of ages.

2

Create age bins

Create a list called age_bins with these exact edges: [20, 30, 40, 50, 60].

Pandas

import pandas as pd
ages = pd.Series([22, 25, 47, 35, 46, 55, 52, 23, 43, 51])
# Create the list age_bins with the exact edges
# Your code here

Need a hint?

Make a list with the exact numbers for the bin edges.

3

Create bins using cut() and qcut()

Use pandas.cut() with ages and age_bins to create age_groups. Then use pandas.qcut() with ages and 4 quantiles to create quantile_groups.

Pandas

import pandas as pd
ages = pd.Series([22, 25, 47, 35, 46, 55, 52, 23, 43, 51])
age_bins = [20, 30, 40, 50, 60]
# Use pd.cut() to create age_groups
# Use pd.qcut() to create quantile_groups
# Your code here

Need a hint?

Use pd.cut() with the bins=age_bins argument. Use pd.qcut() with q=4 for four quantile groups.

4

Print the binned groups

Print the age_groups and quantile_groups to see the binning results.

Pandas

import pandas as pd
ages = pd.Series([22, 25, 47, 35, 46, 55, 52, 23, 43, 51])
age_bins = [20, 30, 40, 50, 60]
age_groups = pd.cut(ages, bins=age_bins)
quantile_groups = pd.qcut(ages, q=4)
# Print age_groups and quantile_groups
# Your code here

Need a hint?

Use two print() statements to show the two grouped results.