stack() and unstack() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to stack the columns of the DataFrame into a Series.

Pandas

stacked = df.[1]()

Drag options to blanks, or click blank then click option'

Aunstack

Bmelt

Cpivot

Dstack

Attempts:

3 left

💡 Hint

Common Mistakes

Using unstack() instead of stack()

Using pivot() which reshapes differently

Using melt() which creates a different format

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

2fill in blank

medium

Complete the code to unstack the last level of the index in the Series.

Pandas

unstacked = s.[1]()

Drag options to blanks, or click blank then click option'

Apivot_table

Bstack

Cunstack

Dreset_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using stack() instead of unstack()

Using reset_index() which flattens the index

Using pivot_table() which aggregates data

The unstack() method pivots the innermost index level to columns, reversing stack().

3fill in blank

hard

Fix the error in the code to correctly unstack the level named 'month'.

Pandas

result = df.stack().[1]('month')

Drag options to blanks, or click blank then click option'

Aunstack

Bstack

Cpivot

Dmelt

Attempts:

3 left

💡 Hint

Common Mistakes

Using stack() again causing deeper stacking

Using pivot() which requires different parameters

Using melt() which reshapes differently

To move the 'month' index level back to columns, use unstack('month').

4fill in blank

hard

Fill both blanks to stack the DataFrame while dropping NaN values.

Pandas

stacked = df.[1]([2]=True)

Drag options to blanks, or click blank then click option'

Astack

Bdropna

Cunstack

Dlevel

Attempts:

3 left

💡 Hint

Common Mistakes

Using unstack() instead of stack()

Using level=True which doesn't drop NaNs

Setting dropna=False explicitly

The stack(dropna=True) method stacks columns into rows while dropping any resulting NaN values.

5fill in blank

hard

Fill all three blanks to stack the DataFrame and then unstack the outermost index level (level 0).

Pandas

result = df.[1]().[2]([3])

Drag options to blanks, or click blank then click option'

Astack

Bunstack

C0

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-1' for innermost level instead of '0'

Using 'swaplevel' which only swaps levels

Omitting the level parameter

Chaining stack() followed by unstack(0) stacks columns to index then pivots the outermost index level back to columns.

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