Selecting data with MultiIndex in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Selecting rows by level value in MultiIndex

What is the output of this code snippet that selects rows where the first level of the MultiIndex is 'B'?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1), ('B', 2)])
data = pd.DataFrame({'value': [10, 20, 30, 40]}, index=index)

result = data.loc['B']
print(result)

A

   value
0     30
1     40

B

       value
B 1     30
  2     40

C KeyError: 'B'

D

   value
1     30
2     40

💡 Hint

Use .loc with the first level key to select all rows under that level.

Using .loc['B'] on a MultiIndex DataFrame selects all rows where the first level index is 'B'. The resulting DataFrame has the second level index as its index.

❓ data_output

intermediate

Selecting data with .xs() method

Given the MultiIndex DataFrame below, what is the output of selecting data with .xs(2, level=1)?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1), ('B', 2)])
data = pd.DataFrame({'value': [10, 20, 30, 40]}, index=index)

result = data.xs(2, level=1)
print(result)

A KeyError: 2

B

   value
A     20
B     40

C

   value
A 2   20
B 2   40

D

   value
1     20
2     40

💡 Hint

The .xs() method selects data at a particular level value across the other level.

Using .xs(2, level=1) selects all rows where the second level index is 2, returning a DataFrame indexed by the first level.

🔧 Debug

advanced

Why does this MultiIndex selection raise an error?

What error does this code raise and why?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('X', 'a'), ('X', 'b'), ('Y', 'a'), ('Y', 'b')])
data = pd.DataFrame({'val': [1, 2, 3, 4]}, index=index)

result = data.loc[('X',)]
print(result)

A Returns rows with first level 'X' and all second level values

B TypeError: unhashable type: 'tuple'

C KeyError: ('X',)

D IndexError: too many indices

💡 Hint

Check how tuples are used to select MultiIndex rows with .loc.

Using .loc with a tuple containing only one element ('X',) looks for an exact match of that tuple in the index, which does not exist, causing a KeyError.

🚀 Application

advanced

Selecting multiple levels with .loc using slice

Given this MultiIndex DataFrame, which option correctly selects all rows where the first level is 'A' and the second level is between 2 and 3 inclusive?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('A', 3), ('B', 2), ('B', 3)])
data = pd.DataFrame({'val': [10, 20, 30, 40, 50]}, index=index)

# Select rows here

A data.loc[('A', slice(2, 3))]

B data.loc[('A', [2, 3])]

C data.loc['A', 2:3]

D data.loc[('A', 2:3)]

💡 Hint

Use slice objects inside .loc for range selection on MultiIndex levels.

Using data.loc[('A', slice(2, 3))] correctly selects rows with first level 'A' and second level between 2 and 3 inclusive.

🧠 Conceptual

expert

Understanding MultiIndex partial indexing behavior

Which statement best describes the behavior of partial indexing with .loc on a MultiIndex DataFrame?

A Partial indexing with .loc selects all rows matching the provided prefix of index levels.

B Partial indexing with .loc requires specifying all levels of the MultiIndex exactly.

C Partial indexing with .loc always returns a Series, never a DataFrame.

D Partial indexing with .loc raises an error if fewer levels than the MultiIndex are specified.

💡 Hint

Think about how .loc behaves when given fewer keys than the number of index levels.

Partial indexing allows selecting all rows that match the given prefix of the MultiIndex levels, returning all matching rows.

Practice

1. What does df.loc[('A', 1)] do when df has a MultiIndex with two levels?

easy

A. Selects the row where the first level is 'A' and the second level is 1.

B. Selects all rows where the first level is 'A' regardless of the second level.

C. Selects all rows where the second level is 1 regardless of the first level.

D. Raises an error because tuples cannot be used with MultiIndex.

Solution

1. Step 1: Understand MultiIndex selection with tuples

   Using df.loc with a tuple selects rows matching each level in the MultiIndex exactly.

2. Step 2: Apply tuple to select specific row

   The tuple ('A', 1) matches the first level 'A' and second level 1, selecting that exact row.

3. Final Answer:

   Selects the row where the first level is 'A' and the second level is 1. -> Option A

4. Quick Check:

   Tuple in loc = exact MultiIndex row [OK]

Hint: Use full tuple in loc to select exact MultiIndex row [OK]

Common Mistakes:

• Thinking partial tuple selects exact row
• Confusing level order in tuple
• Believing tuple usage causes error

2. Which of the following is the correct tuple syntax to select all rows where the first level of a MultiIndex is 'B' using df.loc?

easy

A. df.loc[('B')]

B. df.loc[('B',)]

C. df.loc['B']

D. df.loc[['B']]

Solution

1. Step 1: Understand tuple syntax for single element

   To specify a single-element tuple in Python, a comma is needed: ('B',).

2. Step 2: Use tuple in df.loc to select first level

   Using df.loc[('B',)] selects all rows where the first level is 'B'.

3. Final Answer:

   df.loc[('B',)] -> Option B

4. Quick Check:

   Single-element tuple needs comma [OK]

Hint: Use comma for single-element tuple in loc [OK]

Common Mistakes:

• Using parentheses without comma for tuple
• Using list instead of tuple
• Omitting tuple and causing key error

3. Given the DataFrame df with MultiIndex levels 'Letter' and 'Number':

import pandas as pd
index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1), ('B', 2)], names=['Letter', 'Number'])
data = [10, 20, 30, 40]
df = pd.DataFrame(data, index=index, columns=['Value'])
print(df.loc['A'])

What is the output?

medium

A. Value\nLetter Number\nA 1 10\n 2 20

B. Value\nLetter Number\nB 1 30\n 2 40

C. KeyError because 'A' is not a valid index

D. Value\nNumber\n1 10\n2 20

Solution

1. Step 1: Understand partial selection with single label

   Using df.loc['A'] selects all rows where the first level is 'A'. The result is a DataFrame indexed by the second level only.

2. Step 2: Check output structure

   The output shows 'Value' column with index 'Number' 1 and 2, values 10 and 20 respectively.

3. Final Answer:

   Value Number 1 10 2 20 -> Option D

4. Quick Check:

   Partial index selection drops first level [OK]

Hint: Partial loc drops selected level from index [OK]

Common Mistakes:

• Expecting full MultiIndex in output
• Confusing which levels remain after selection
• Thinking KeyError occurs for valid label

4. Consider this code snippet:

import pandas as pd
index = pd.MultiIndex.from_tuples([('X', 1), ('X', 2), ('Y', 1)])
data = [100, 200, 300]
df = pd.DataFrame(data, index=index, columns=['Score'])

# Attempt to select all rows where first level is 'X'
result = df.loc[('X')]
print(result)

What is the issue and how to fix it?

medium

A. SyntaxError; fix by removing parentheses: df.loc['X'].

B. Raises KeyError; fix by using df.loc[('X',)] to specify single-element tuple.

C. No issue; code runs correctly and prints rows with first level 'X'.

D. Raises TypeError; fix by converting 'X' to list: df.loc[['X']].

Solution

1. Step 1: Analyze the tuple usage in df.loc

   Here, df.loc[('X')] is equivalent to df.loc['X'] because parentheses around a string do not create a tuple.

2. Step 2: Confirm code behavior

   This selects all rows where the first level is 'X' correctly, so no error occurs.

3. Final Answer:

   No issue; code runs correctly and prints rows with first level 'X'. -> Option C

4. Quick Check:

   Parentheses alone don't make tuple [OK]

Hint: Parentheses alone don't create tuple; string stays string [OK]

Common Mistakes:

• Assuming ('X') is a tuple
• Expecting KeyError for valid label
• Confusing tuple syntax with parentheses

5. You have a MultiIndex DataFrame df with levels 'City', 'Year', and 'Month'. How do you select all data for 'New York' in 2023 regardless of month using a tuple that specifies all three levels?

hard

A. df.loc[('New York', 2023, slice(None))]

B. df.loc[('New York', 2023)]

C. df.loc[('New York', 2023,)]

D. df.loc['New York', 2023]

Solution

1. Step 1: Understand MultiIndex with 3 levels

   The MultiIndex has three levels: 'City', 'Year', and 'Month'. To select all months for 'New York' and 2023, we must specify all three levels.

2. Step 2: Use slice(None) to select all in third level

   Using slice(None) in the third position selects all months. So df.loc[('New York', 2023, slice(None))] selects all rows matching city and year, any month.

3. Final Answer:

   df.loc[('New York', 2023, slice(None))] -> Option A

4. Quick Check:

   Use slice(None) for full level selection [OK]

Hint: Use slice(None) for full selection in MultiIndex level [OK]

Common Mistakes:

• Omitting third level causing KeyError
• Using incomplete tuple without slice
• Using comma-separated loc without tuple