stack() and unstack() in Pandas - Mini Project: Build & Apply

Using stack() and unstack() in pandas

📖 Scenario: You work in a small company that tracks sales data for different products across several months. The data is stored in a table format, but sometimes you need to change how the data is arranged to better understand it.

🎯 Goal: You will learn how to use pandas stack() and unstack() methods to reshape data. This helps you switch between wide and long formats of your sales data.

📋 What You'll Learn

Create a pandas DataFrame with sales data for products over months

Create a variable to select a specific month

Use stack() to convert columns into rows

Use unstack() to convert rows back into columns

Print the final reshaped data

💡 Why This Matters

🌍 Real World

In real companies, sales data often comes in wide tables. Reshaping data with stack and unstack helps prepare data for reports and charts.

💼 Career

Data analysts and scientists frequently reshape data to clean and analyze it. Knowing stack and unstack is a basic skill for data manipulation.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with the following data: products as the index 'Product A', 'Product B', 'Product C' and columns as months 'Jan', 'Feb', 'Mar' with these values exactly:
Jan: 100, 150, 200
Feb: 110, 160, 210
Mar: 120, 170, 220

Pandas

# Create the sales DataFrame with products as index and months as columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary for columns and index for product names.

2

Select the month to analyze

Create a variable called month and set it to the string 'Feb' to select the sales data for February.

Pandas

import pandas as pd

sales = pd.DataFrame({
    'Jan': [100, 150, 200],
    'Feb': [110, 160, 210],
    'Mar': [120, 170, 220]
}, index=['Product A', 'Product B', 'Product C'])

# Create a variable month and set it to 'Feb'
# Your code here

Need a hint?

Just assign the string 'Feb' to the variable month.

3

Use stack() and unstack() to reshape data

Use stack() on the sales DataFrame and save the result in a variable called stacked_sales. Then use unstack() on stacked_sales and save it in a variable called unstacked_sales.

Pandas

import pandas as pd

sales = pd.DataFrame({
    'Jan': [100, 150, 200],
    'Feb': [110, 160, 210],
    'Mar': [120, 170, 220]
}, index=['Product A', 'Product B', 'Product C'])

month = 'Feb'

# Use stack() on sales and assign to stacked_sales
# Use unstack() on stacked_sales and assign to unstacked_sales
# Your code here

Need a hint?

Call stack() on sales and assign it to stacked_sales. Then call unstack() on stacked_sales and assign it to unstacked_sales.

4

Print the reshaped data

Print the stacked_sales variable, then print the unstacked_sales variable.

Pandas

import pandas as pd

sales = pd.DataFrame({
    'Jan': [100, 150, 200],
    'Feb': [110, 160, 210],
    'Mar': [120, 170, 220]
}, index=['Product A', 'Product B', 'Product C'])

month = 'Feb'

stacked_sales = sales.stack()
unstacked_sales = stacked_sales.unstack()

# Print stacked_sales
# Print unstacked_sales
# Your code here

Need a hint?

Use two print statements: print(stacked_sales) and print(unstacked_sales.loc['Product A']).