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

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

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

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

Hint

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

Practice

1. What does the stack() function do in pandas?

easy

A. It filters rows based on a condition.

B. It moves rows into columns, making the data wider.

C. It moves columns into rows, making the data longer.

D. It sorts the DataFrame by index.

Solution

1. Step 1: Understand the purpose of stack()

   stack() takes the columns of a DataFrame and stacks them into the row index, making the data longer.

2. Step 2: Compare with other options

   unstack() does the opposite by moving rows into columns. Sorting and filtering are unrelated to stack().

3. Final Answer:

   It moves columns into rows, making the data longer. -> Option C

4. Quick Check:

   stack() = columns to rows [OK]

Hint: Remember: stack makes data longer by moving columns down [OK]

Common Mistakes:

• Confusing stack() with unstack()
• Thinking stack() sorts data
• Assuming stack() filters rows

2. Which of the following is the correct syntax to unstack a DataFrame named df?

easy

A. df.stack()

B. df.unstack()

C. df.unstacked()

D. df.stack(unstack=True)

Solution

1. Step 1: Recall the method names

   The method to move rows into columns is unstack(), so df.unstack() is correct.

2. Step 2: Check other options

   df.stack() stacks columns into rows. unstacked() does not exist. stack(unstack=True) is invalid syntax.

3. Final Answer:

   df.unstack() -> Option B

4. Quick Check:

   Unstack method is unstack() [OK]

Hint: Use df.unstack() to widen data by moving rows to columns [OK]

Common Mistakes:

• Using stack() instead of unstack()
• Calling a non-existent method unstacked()
• Passing wrong arguments to stack()

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({
  'A': [1, 2],
  'B': [3, 4]
}, index=['x', 'y'])
stacked = df.stack()

What is the output of stacked?

medium

A. A Series with index ['A', 'B'] and values [1, 2]

B. A DataFrame with columns 'x' and 'y' and rows 'A' and 'B'

C. A DataFrame with columns 'A' and 'B' and rows 'x' and 'y'

D. A Series with MultiIndex: ('x', 'A')=1, ('x', 'B')=3, ('y', 'A')=2, ('y', 'B')=4

Solution

1. Step 1: Understand stacking on df

   Stack moves columns into rows, creating a Series with a MultiIndex combining the original index and column labels.

2. Step 2: Check the resulting Series

   The index pairs are ('x', 'A')=1, ('x', 'B')=3, ('y', 'A')=2, ('y', 'B')=4, matching A Series with MultiIndex: ('x', 'A')=1, ('x', 'B')=3, ('y', 'A')=2, ('y', 'B')=4.

3. Final Answer:

   A Series with MultiIndex: ('x', 'A')=1, ('x', 'B')=3, ('y', 'A')=2, ('y', 'B')=4 -> Option D

4. Quick Check:

   stack() output is MultiIndex Series [OK]

Hint: Stack creates MultiIndex Series combining rows and columns [OK]

Common Mistakes:

• Expecting a DataFrame output instead of Series
• Confusing index and columns after stacking
• Ignoring MultiIndex structure

4. You run the code:

df = pd.DataFrame({
  'X': [10, 20],
  'Y': [30, 40]
}, index=['a', 'b'])
result = df.unstack(level=2)

What error will occur?

medium

A. KeyError: Level 2 not found

B. TypeError: unstack() takes no arguments

C. ValueError: Cannot unstack with duplicate entries

D. No error, returns unstacked DataFrame

Solution

1. Step 1: Check the index levels of df

   df has a single index level ('a', 'b'), so level 2 does not exist.

2. Step 2: Understand unstack(level=2) behavior

   Requesting level 2 causes a KeyError because that level is missing.

3. Final Answer:

   KeyError: Level 2 not found -> Option A

4. Quick Check:

   unstack(level) must match existing index levels [OK]

Hint: Check index levels before unstacking by level number [OK]

Common Mistakes:

• Assuming unstack() accepts any level number
• Expecting no error on invalid level
• Confusing level number with label

5. You have a DataFrame with a MultiIndex on rows:

import pandas as pd
index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1)])
df = pd.DataFrame({'val': [10, 20, 30]}, index=index)

How can you reshape df so that the second level of the index becomes columns?

hard

A. Use df.unstack(level=1) to move second index level to columns.

B. Use df.stack(level=1) to move second index level to columns.

C. Use df.unstack(level=0) to move second index level to columns.

D. Use df.stack() without arguments.

Solution

1. Step 1: Identify the MultiIndex levels

   The DataFrame has two index levels: level 0 ('A', 'B') and level 1 (1, 2).

2. Step 2: Use unstack(level=1) to move second level to columns

   Unstacking level 1 moves that index level into columns, reshaping the data as required.

3. Final Answer:

   Use df.unstack(level=1) to move second index level to columns. -> Option A

4. Quick Check:

   unstack(level=1) moves second index level to columns [OK]

Hint: Unstack the desired index level number to widen data [OK]

Common Mistakes:

• Using stack() instead of unstack()
• Unstacking wrong level number
• Ignoring MultiIndex structure