stack() and unstack() in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the stack() function do in pandas?

stack() moves the columns of a DataFrame into the row index, turning columns into a new level of the row index. It "compresses" the DataFrame horizontally into a Series with a MultiIndex.

beginner

What is the purpose of the unstack() function in pandas?

unstack() does the opposite of stack(). It moves a level of the row index into columns, "widening" the DataFrame or Series back to a wider shape.

intermediate

If you have a DataFrame with a MultiIndex on rows, how can you convert one index level into columns?

You can use unstack(level) where level is the index level you want to move to columns.

beginner

What happens if you call stack() on a DataFrame with no missing values?

The DataFrame columns become a new inner level of the row index, and the data is returned as a Series with a MultiIndex.

intermediate

How do stack() and unstack() help in reshaping data?

They help by changing the shape of data between 'wide' and 'long' formats, making it easier to analyze or visualize data depending on the task.

What does stack() do to a DataFrame?

A Sorts the DataFrame

B Moves rows into columns

C Deletes missing values

D Moves columns into the row index

stack() moves columns into the row index, creating a MultiIndex Series.

Which function reverses the effect of stack()?

A unstack()

B groupby()

C melt()

D pivot()

unstack() moves a level of the row index back into columns, reversing stack().

If a DataFrame has a MultiIndex on rows, which method moves one index level to columns?

A reset_index()

B stack()

C unstack()

D set_index()

unstack() moves a row index level to columns.

What type of object does stack() return when applied to a DataFrame?

A DataFrame

B Series with MultiIndex

C List

D Dictionary

stack() returns a Series with a MultiIndex.

Why use stack() and unstack() in data analysis?

A To reshape data between wide and long formats

B To remove duplicates

C To sort data alphabetically

D To merge datasets

They reshape data, making it easier to analyze or visualize.

Explain in your own words what stack() does to a DataFrame and give a simple example.

Think about turning columns into rows.

Describe how unstack() can be used to reshape data and why this might be useful.

Consider the opposite of stacking.

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