Stack and unstack in Data Analysis Python

Introduction

Stack and unstack help you reshape data tables easily. They let you switch between wide and long formats to see data differently.

You want to turn columns into rows to analyze data better.

You have grouped data in rows and want to spread it into columns.

You want to prepare data for charts that need a specific shape.

You need to clean data by moving hierarchical indexes between rows and columns.

Syntax

Data Analysis Python

import pandas as pd

# Stack moves columns to rows
stacked_df = df.stack()

# Unstack moves rows to columns
unstacked_df = df.unstack()

Stack works on columns and turns them into a new inner row index.

Unstack works on row indexes and turns one level into columns.

Examples

Stacking a simple DataFrame moves columns A and B into rows under a new inner index.

Data Analysis Python

import pandas as pd

data = {'A': [1, 2], 'B': [3, 4]}
df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

stacked = df.stack()
print('\nStacked DataFrame:')
print(stacked)

Unstacking a MultiIndex DataFrame moves the 'second' level of the row index into columns.

Data Analysis Python

import pandas as pd

# DataFrame with MultiIndex rows
arrays = [['bar', 'bar', 'baz', 'baz'], ['one', 'two', 'one', 'two']]
index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}
df = pd.DataFrame(data, index=index)

print('Original DataFrame with MultiIndex:')
print(df)

unstacked = df.unstack(level='second')
print('\nUnstacked DataFrame:')
print(unstacked)

Stacking an empty DataFrame returns an empty Series without error.

Data Analysis Python

import pandas as pd

df_empty = pd.DataFrame()
print('Stacking empty DataFrame:')
print(df_empty.stack())

Stacking a DataFrame with one row and one column creates a Series with one element.

Data Analysis Python

import pandas as pd

df_single = pd.DataFrame({'A': [10]})
print('Original single-element DataFrame:')
print(df_single)

stacked_single = df_single.stack()
print('\nStacked single-element DataFrame:')
print(stacked_single)

Sample Program

This program creates a DataFrame with two levels in rows and columns. It stacks the first level of columns into rows, then unstacks the second level of rows back into columns. This shows how stacking and unstacking reshape data.

Data Analysis Python

import pandas as pd

# Create a DataFrame with MultiIndex rows and columns
arrays_rows = [['group1', 'group1', 'group2', 'group2'], ['A', 'B', 'A', 'B']]
index = pd.MultiIndex.from_arrays(arrays_rows, names=['group', 'subgroup'])

arrays_cols = [['score', 'score', 'rank', 'rank'], ['math', 'english', 'math', 'english']]
columns = pd.MultiIndex.from_arrays(arrays_cols)

data = [[90, 80, 1, 2], [85, 88, 2, 1], [78, 75, 3, 4], [82, 79, 4, 3]]
df = pd.DataFrame(data, index=index, columns=columns)

print('Original DataFrame:')
print(df)

# Stack the columns to rows (move inner column level to rows)
stacked_df = df.stack(level=0)
print('\nDataFrame after stacking columns (level=0):')
print(stacked_df)

# Unstack the rows to columns (move inner row level to columns)
unstacked_df = stacked_df.unstack(level='subgroup')
print('\nDataFrame after unstacking rows (level="subgroup"):')
print(unstacked_df)

Important Notes

Stack and unstack have time complexity roughly O(n), where n is the number of elements in the DataFrame.

They use extra memory to create reshaped copies, so space complexity is also O(n).

A common mistake is forgetting which index level to stack or unstack, causing confusing results.

Use stack when you want to make data longer (more rows), and unstack when you want to make data wider (more columns).

Summary

Stack moves columns into rows, making data longer.

Unstack moves rows into columns, making data wider.

They help reshape data for easier analysis and visualization.

Practice

1. What does the stack() method do to a DataFrame in pandas?

easy

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

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

C. It sorts the DataFrame by index.

D. It removes duplicate rows from the DataFrame.

Solution

1. Step 1: Understand the stack operation

   The stack() method compresses columns into a single column, turning columns into rows.

2. Step 2: Compare with unstack

   Unlike unstack(), which moves rows to columns, stack() makes data longer by stacking columns.

3. Final Answer:

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

4. Quick Check:

   Stack = columns to rows [OK]

Hint: Stack compresses columns into rows, making data longer [OK]

Common Mistakes:

• Confusing stack with unstack direction
• Thinking stack sorts data
• Assuming stack removes duplicates

2. Which of the following is the correct syntax to unstack the last level of a MultiIndex DataFrame df?

easy

A. df.stack(axis=0)

B. df.stack(level=-1)

C. df.unstack(level=-1)

D. df.unstack(axis=1)

Solution

1. Step 1: Recall unstack syntax

   The unstack() method by default unstacks the last level of the index, and level=-1 explicitly specifies this.

2. Step 2: Differentiate from stack

   stack() is the opposite operation, so options A and B are incorrect. unstack() does not take an axis parameter, so df.unstack(axis=1) is invalid.

3. Final Answer:

   df.unstack(level=-1) -> Option C

4. Quick Check:

   Unstack last level = df.unstack(level=-1) [OK]

Hint: Use unstack(level=-1) to unstack last index level [OK]

Common Mistakes:

• Using stack instead of unstack
• Passing axis parameter to unstack
• Not specifying level when needed

3. Given the DataFrame df below:

import pandas as pd
d = {('A', 'x'): 1, ('A', 'y'): 2, ('B', 'x'): 3, ('B', 'y'): 4}
df = pd.Series(d).unstack()

What is the output of df.stack()?

medium

A. A DataFrame with columns 'A' and 'B' stacked

B. A Series with MultiIndex where second level is stacked into rows

C. A DataFrame with columns 'x' and 'y' unchanged

D. An error because stack cannot be applied here

Solution

1. Step 1: Understand the initial DataFrame

   The unstack() converts the Series with MultiIndex into a DataFrame with columns 'x' and 'y' and index 'A' and 'B'.

2. Step 2: Apply stack() on the DataFrame

   Calling stack() moves the columns 'x' and 'y' back into the inner level of the row index, producing a Series with MultiIndex.

3. Final Answer:

   A Series with MultiIndex where second level is stacked into rows -> Option B

4. Quick Check:

   Stack reverses unstack, making columns into row index [OK]

Hint: Stack reverses unstack, turning columns back into row index [OK]

Common Mistakes:

• Expecting a DataFrame output instead of Series
• Confusing which index level is stacked
• Thinking stack causes error here

4. You run the code below and get an error:

df = pd.DataFrame({
  'A': [1, 2],
  'B': [3, 4]
})
stacked = df.stack(level=1)

What is the cause of the error?

medium

A. The DataFrame index is missing.

B. Stack requires numeric data only.

C. Stack cannot be used on DataFrames, only Series.

D. DataFrame columns have no MultiIndex, so level=1 is invalid.

Solution

1. Step 1: Check DataFrame columns

   The DataFrame has simple columns 'A' and 'B', which are not a MultiIndex, so specifying level=1 is invalid.

2. Step 2: Understand stack level parameter

   The level parameter in stack() refers to the level of the columns MultiIndex to stack. Without MultiIndex columns, level=1 causes an error.

3. Final Answer:

   DataFrame columns have no MultiIndex, so level=1 is invalid. -> Option D

4. Quick Check:

   Stack level requires MultiIndex columns [OK]

Hint: Stack level needs MultiIndex columns; simple columns cause error [OK]

Common Mistakes:

• Assuming stack only works on Series
• Thinking stack requires numeric data
• Ignoring the column index type

5. You have a DataFrame df with a MultiIndex on rows (levels: 'City', 'Year') and columns as years (e.g., 2020, 2021). You want to reshape it so that the 'Year' level from rows becomes columns, and the columns years become a new row level. Which sequence of operations correctly achieves this?

hard

A. First unstack the 'Year' level from rows, then stack the columns.

B. First stack the columns, then unstack the 'City' level from rows.

C. Use only stack on the columns without unstacking rows.

D. Use only unstack on the 'City' level.

Solution

1. Step 1: Unstack the 'Year' level from rows

   Unstacking the 'Year' level moves it from the row index to columns, widening the DataFrame.

2. Step 2: Stack the columns

   Stacking the columns moves the columns years into a new row index level, making the data longer again but reshaped as desired.

3. Final Answer:

   First unstack the 'Year' level from rows, then stack the columns. -> Option A

4. Quick Check:

   Unstack rows then stack columns reshapes MultiIndex correctly [OK]

Hint: Unstack row level first, then stack columns to reshape MultiIndex [OK]

Common Mistakes:

• Stacking before unstacking causes wrong shape
• Unstacking wrong index level
• Ignoring the need to reshape both rows and columns