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.