Stack and unstack in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the stack() method do in a DataFrame?

It pivots the columns of a DataFrame into the index, turning columns into rows and creating a Series with a MultiIndex.

beginner

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

It pivots the innermost level of the index back into columns, turning a Series or DataFrame from a stacked format to a wider format.

intermediate

How does stack() affect the shape of a DataFrame?

It reduces the number of columns by moving them into the row index, increasing the number of rows accordingly.

intermediate

What happens if you use unstack(level=1) on a MultiIndex Series?

It moves the second level of the index (level 1) into columns, reshaping the data to a wider format based on that level.

beginner

Why are stack() and unstack() useful in data analysis?

They help reshape data for easier analysis, visualization, or aggregation by switching between wide and long formats.

What does the stack() method do to a DataFrame?

A Deletes missing values

B Turns rows into columns

C Turns columns into rows by moving them into the index

D Sorts the DataFrame by index

stack() moves columns into the row index, turning columns into rows.

Which method reverses the effect of stack()?

A unstack()

B pivot()

C melt()

D groupby()

unstack() pivots the index back into columns, reversing stack().

If a DataFrame has shape (3, 4), what will be the shape after stack()?

A (1, 12)

B (3, 4)

C (4, 3)

D (12, 1)

stack() moves 4 columns into rows, so rows become 3*4=12 and columns become 1.

What argument can you pass to unstack() to specify which index level to pivot?

A level

B axis

C columns

D index

The level argument specifies which index level to unstack.

Which of these is a common use case for stack() and unstack()?

A Changing data types

B Reshaping data between wide and long formats

C Filtering rows

D Sorting columns

They are used to reshape data, switching between wide and long formats.

Explain how stack() and unstack() work in pandas and why they are useful.

Think about how data layout changes between rows and columns.

Describe a real-life example where you might use stack() and unstack() to prepare data for analysis.

Imagine you have survey data with multiple answers per person.