stack() and unstack() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of stacking a DataFrame with MultiIndex columns?

Given the following DataFrame, what will be the result of df.stack()?

Pandas

import pandas as pd

cols = pd.MultiIndex.from_tuples([('A', 'one'), ('A', 'two'), ('B', 'one'), ('B', 'two')])
data = [[1, 2, 3, 4], [5, 6, 7, 8]]
df = pd.DataFrame(data, columns=cols)
result = df.stack()

A

A  one  0    1
          1    5
   two  0    2
          1    6
B  one  0    3
          1    7
   two  0    4
          1    8
dtype: int64

B

0  A  one    1
      two    2
   B  one    3
      two    4
1  A  one    5
      two    6
   B  one    7
      two    8
dtype: int64

C

A    one  0    1
           1    5
     two  0    2
           1    6
B    one  0    3
           1    7
     two  0    4
           1    8
dtype: int64

D {0: {('A', 'one'): 1, ('A', 'two'): 2, ('B', 'one'): 3, ('B', 'two'): 4}, 1: {('A', 'one'): 5, ('A', 'two'): 6, ('B', 'one'): 7, ('B', 'two'): 8}}

💡 Hint

stack() moves the innermost column level to become the innermost row index level.

stack() converts the columns into a new inner row index level, preserving the outer row index. The result is a Series with a MultiIndex of the original row index and the stacked column levels.

❓ data_output

intermediate

What is the shape of the DataFrame after unstacking a Series with MultiIndex?

Consider a Series with MultiIndex of two levels and 6 elements. After applying unstack() on the inner level, what will be the shape of the resulting DataFrame?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2), ('c', 1), ('c', 2)])
data = pd.Series(range(6), index=index)
result = data.unstack()

A (6, 1)

B (3, 2)

C (1, 6)

D (2, 3)

💡 Hint

unstack() pivots the inner index level to columns.

The original Series has 3 outer index values ('a','b','c') and 2 inner index values (1,2). unstack() moves the inner level to columns, resulting in a DataFrame with 3 rows and 2 columns.

🔧 Debug

advanced

Why does this unstack() call raise a KeyError?

Given the DataFrame below, why does df.unstack(level=2) raise a KeyError?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([(1, 'a'), (1, 'b'), (2, 'a'), (2, 'b')], names=['num', 'char'])
df = pd.DataFrame({'value': [10, 20, 30, 40]}, index=index)
result = df.unstack(level=2)

A KeyError because the DataFrame is empty.

B KeyError because the DataFrame has no columns to unstack.

C KeyError because unstack() requires a column MultiIndex, not row MultiIndex.

D KeyError because level 2 does not exist in the index; only levels 0 and 1 exist.

💡 Hint

Check the number of levels in the DataFrame's index.

The MultiIndex has only two levels: 0 and 1. Asking to unstack level 2 causes a KeyError because that level does not exist.

🚀 Application

advanced

Transform a DataFrame with stacked sales data back to wide format

You have a DataFrame with sales data stacked by 'Store' and 'Product' as row indices and 'Month' as columns. Which code correctly unstacks the 'Product' level to columns?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('Store1', 'ProdA'), ('Store1', 'ProdB'), ('Store2', 'ProdA'), ('Store2', 'ProdB')], names=['Store', 'Product'])
data = pd.DataFrame({'Jan': [100, 150, 200, 250], 'Feb': [110, 160, 210, 260]}, index=index)

# Which code unstacks 'Product' to columns?

A data.stack(level='Store')

B data.stack(level='Product')

C data.unstack(level='Product')

D data.unstack(level='Store')

💡 Hint

unstack moves a row index level to columns.

To move the 'Product' level from rows to columns, use unstack(level='Product'). stack() does the opposite.

🧠 Conceptual

expert

Understanding the effect of stack() and unstack() on DataFrame shape

If a DataFrame has shape (4, 3) with a simple index and columns, what will be the shape after applying stack() and then unstack()?

A After stack(): (12,), after unstack(): (4, 3)

B After stack(): (4, 3), after unstack(): (12,)

C After stack(): (3, 4), after unstack(): (4, 3)

D After stack(): (12, 1), after unstack(): (3, 4)

💡 Hint

stack() converts columns into a new inner row index level, unstack() reverses this.

stack() converts each row's columns into a stacked Series with length rows * columns (4*3=12). unstack() reverses this back to the original shape (4,3).