cut() and qcut() for binning in Data Analysis Python - Mini Project: Build & Apply

Using cut() and qcut() for Binning Data

📖 Scenario: You work in a retail company. You have sales data for different products. You want to group the sales amounts into categories to understand sales distribution better.

🎯 Goal: Learn how to use cut() and qcut() from pandas to divide sales data into bins based on fixed ranges and quantiles.

📋 What You'll Learn

Create a pandas Series with sales data

Create a variable for bin edges for fixed binning

Use pd.cut() to bin sales into fixed ranges

Use pd.qcut() to bin sales into quantiles

Print the binned results

💡 Why This Matters

🌍 Real World

Binning sales data helps businesses understand sales distribution and customer segments better.

💼 Career

Data analysts and scientists often use binning to prepare data for reports, visualizations, and machine learning.

1

Create sales data as a pandas Series

Create a pandas Series called sales with these exact values: [100, 150, 200, 250, 300, 350, 400, 450, 500]

Data Analysis Python

import pandas as pd

# Create the sales Series with given values
# Your code here

Hint

Use pd.Series() and pass the list of sales values inside.

2

Create bin edges for fixed range binning

Create a list called bins with these exact values: [0, 200, 400, 600] to define the edges for fixed range bins

Data Analysis Python

import pandas as pd

sales = pd.Series([100, 150, 200, 250, 300, 350, 400, 450, 500])

# Create the bins list with edges 0, 200, 400, 600
# Your code here

Hint

Make a list named bins with the numbers 0, 200, 400, and 600 in order.

3

Use cut() and qcut() to bin the sales data

Use pd.cut() with sales and bins to create sales_cut. Then use pd.qcut() with sales and 3 to create sales_qcut

Data Analysis Python

import pandas as pd

sales = pd.Series([100, 150, 200, 250, 300, 350, 400, 450, 500])
bins = [0, 200, 400, 600]

# Use pd.cut() with sales and bins to create sales_cut
# Use pd.qcut() with sales and 3 bins to create sales_qcut
# Your code here

Hint

Use pd.cut() with the bins list to group sales by fixed ranges. Use pd.qcut() with 3 to group sales into three equal-sized groups.

4

Print the binned sales results

Print sales_cut and then print sales_qcut to display the binning results

Data Analysis Python

import pandas as pd

sales = pd.Series([100, 150, 200, 250, 300, 350, 400, 450, 500])
bins = [0, 200, 400, 600]

sales_cut = pd.cut(sales, bins)
sales_qcut = pd.qcut(sales, 3)

# Print sales_cut
# Print sales_qcut
# Your code here

Hint

Use two print statements: print(sales_cut) and print(sales_qcut).