MultiIndex (hierarchical indexing) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this MultiIndex selection?

Given the DataFrame with a MultiIndex, what will be the output of selecting data.loc['B']?

Data Analysis Python

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
1     30
2     40

B

Value
1    30
2    40

C

Value
B    30
B    40

D KeyError: 'B'

💡 Hint

Remember that selecting with a level value returns a DataFrame or Series indexed by the remaining level(s).

When selecting data.loc['B'], pandas returns all rows where the first level of the MultiIndex is 'B'. The resulting DataFrame is indexed by the second level (1 and 2) with the 'Value' column.

❓ data_output

intermediate

How many rows are in the DataFrame after slicing with .loc?

Given the MultiIndex DataFrame below, how many rows remain after slicing with data.loc[('A', slice(1,2))]?

Data Analysis Python

import pandas as pd

index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('A', 3), ('B', 1)])
data = pd.DataFrame({'Value': [10, 20, 30, 40]}, index=index)
sliced = data.loc[('A', slice(1,2))]
print(len(sliced))

A 1

B 2

C 3

D 4

💡 Hint

Slice selects rows where the second level is between 1 and 2 inclusive.

The slice selects rows with first level 'A' and second level 1 or 2. There are exactly two such rows.

❓ visualization

advanced

Which plot shows the correct aggregation by first level of MultiIndex?

Given a MultiIndex DataFrame with sales data, which plot correctly shows the total sales summed by the first level of the index?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

index = pd.MultiIndex.from_tuples([('Store1', 'Jan'), ('Store1', 'Feb'), ('Store2', 'Jan'), ('Store2', 'Feb')])
data = pd.DataFrame({'Sales': [100, 150, 200, 250]}, index=index)

agg = data.groupby(level=0).sum()
agg.plot(kind='bar')
plt.show()

A Scatter plot with points for each sale value

B Line plot with four points labeled Store1-Jan, Store1-Feb, Store2-Jan, Store2-Feb

C Pie chart with four slices for each month

D Bar plot with two bars labeled Store1 and Store2 showing heights 250 and 450

💡 Hint

Grouping by level=0 sums sales per store.

The groupby(level=0).sum() aggregates sales by the first index level (store). The bar plot shows total sales per store.

🔧 Debug

advanced

What error does this MultiIndex slicing code raise?

What error will this code raise when trying to slice a MultiIndex DataFrame?

Data Analysis Python

import pandas as pd

index = pd.MultiIndex.from_tuples([('X', 1), ('X', 2), ('Y', 1)])
data = pd.DataFrame({'Value': [5, 10, 15]}, index=index)

result = data.loc['X', 1:2]
print(result)

A IndexingError: Too many indexers

B KeyError: 1

C TypeError: unhashable type: 'slice'

D No error, prints the sliced DataFrame

💡 Hint

Check how .loc handles multiple indexers with MultiIndex.

Using data.loc['X', 1:2] tries to index with two separate arguments, but .loc expects a tuple for MultiIndex slicing. This causes an IndexingError.

🚀 Application

expert

What is the resulting DataFrame after swapping levels and sorting?

Given a MultiIndex DataFrame, what is the output after swapping the index levels and sorting by the new index?

Data Analysis Python

import pandas as pd

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

swapped = data.swaplevel(0, 1).sort_index()
print(swapped)

A

       Score
A 1    10
B 3    20
C 2    30

B

       Score
1     10
2     30
3     20

C

       Score
1 A     10
2 C     30
3 B     20

D KeyError: 0

💡 Hint

Swapping levels changes the order of index levels, then sorting arranges by the new first level.

swaplevel(0,1) switches the two index levels. Sorting by index then orders by the new first level (previously second level). The output shows index with first level 1,2,3 and second level A,C,B respectively.

Practice

1. What is the main purpose of a MultiIndex in pandas?

easy

A. To create plots automatically

B. To label data with multiple levels for better organization

C. To convert data into a list

D. To speed up numerical calculations

Solution

1. Step 1: Understand MultiIndex concept

   MultiIndex allows multiple levels of labels on rows or columns, organizing complex data.

2. Step 2: Compare options

   Only To label data with multiple levels for better organization correctly describes MultiIndex's purpose; others describe unrelated features.

3. Final Answer:

   To label data with multiple levels for better organization -> Option B

4. Quick Check:

   MultiIndex = multiple levels labeling [OK]

Hint: MultiIndex means multiple levels of labels [OK]

Common Mistakes:

• Thinking MultiIndex speeds up calculations
• Confusing MultiIndex with plotting tools
• Believing MultiIndex converts data to lists

2. Which of the following is the correct way to create a MultiIndex from tuples in pandas?

easy

A. pd.MultiIndex.build_tuples([('A', 1), ('B', 2)])

B. pd.MultiIndex.create_tuples([('A', 1), ('B', 2)])

C. pd.MultiIndex.make_tuples([('A', 1), ('B', 2)])

D. pd.MultiIndex.from_tuples([('A', 1), ('B', 2)])

Solution

1. Step 1: Recall pandas MultiIndex creation methods

   The correct method to create MultiIndex from tuples is from_tuples.

2. Step 2: Check method names

   Only pd.MultiIndex.from_tuples([('A', 1), ('B', 2)]) uses the correct method name; others are invalid pandas methods.

3. Final Answer:

   pd.MultiIndex.from_tuples([('A', 1), ('B', 2)]) -> Option D

4. Quick Check:

   Use from_tuples to create MultiIndex from tuples [OK]

Hint: Use from_tuples to create MultiIndex from tuple list [OK]

Common Mistakes:

• Using non-existent methods like create_tuples
• Confusing method names with make_tuples or build_tuples
• Forgetting to import pandas as pd

3. Given the code:

import pandas as pd
index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1)])
data = pd.Series([10, 20, 30], index=index)
print(data.loc['A'])

What is the output?

medium

A. A 10
A 20
dtype: int64

B. 10
20

C. 1 10
2 20
dtype: int64

D. KeyError

Solution

1. Step 1: Understand MultiIndex and selection

   The MultiIndex has two levels: first level 'A' or 'B', second level numbers. Selecting data.loc['A'] returns all entries where first level is 'A'.

2. Step 2: Check values for 'A'

   Entries with first level 'A' are ('A', 1):10 and ('A', 2):20, so output shows index 1 and 2 with values 10 and 20.

3. Final Answer:

   1 10 2 20 dtype: int64 -> Option C

4. Quick Check:

   data.loc['A'] returns second level index and values [OK]

Hint: Selecting first level returns second level index and values [OK]

Common Mistakes:

• Expecting full tuple index in output
• Getting KeyError by wrong indexing
• Confusing index labels with values

4. What is wrong with this code snippet?

import pandas as pd
index = pd.MultiIndex.from_product([['X', 'Y'], [1, 2]])
data = pd.Series([5, 10, 15], index=index)
print(data)

medium

A. The length of data does not match the MultiIndex length

B. from_product is not a valid MultiIndex method

C. Series cannot have MultiIndex

D. The index should be a list, not MultiIndex

Solution

1. Step 1: Check MultiIndex length

   from_product creates all combinations: 2 (X,Y) * 2 (1,2) = 4 index entries.

2. Step 2: Check data length

   Data has length 3, which does not match 4 index entries, causing a ValueError.

3. Final Answer:

   The length of data does not match the MultiIndex length -> Option A

4. Quick Check:

   Data length must match MultiIndex length [OK]

Hint: Data length must equal MultiIndex length [OK]

Common Mistakes:

• Thinking from_product is invalid
• Believing Series can't have MultiIndex
• Assuming index must be list, not MultiIndex

5. You have sales data for two stores over two years stored as a MultiIndex DataFrame with levels ['Store', 'Year']. How do you select sales data for Store 'B' in year 2023?

hard

A. df.loc[('B', 2023)]

B. df['B'][2023]

C. df.loc[2023, 'B']

D. df.loc['B', [2023]]

Solution

1. Step 1: Understand MultiIndex selection syntax

   To select a row with multiple index levels, use a tuple inside loc with all levels specified.

2. Step 2: Analyze options

   df.loc[('B', 2023)] correctly specifies both levels as a tuple. df.loc[2023, 'B'] uses incorrect order of levels (year before store). df['B'][2023] attempts column access first, causing KeyError. df.loc['B', [2023]] uses a list which pandas interprets as column slice, causing KeyError if no such columns.

3. Final Answer:

   df.loc[('B', 2023)] -> Option A

4. Quick Check:

   Use tuple in loc for MultiIndex row selection [OK]

Hint: Use tuple inside loc to select MultiIndex row [OK]

Common Mistakes:

• Reversing the order of index levels
• Using df['B'] to access rows instead of loc with tuple
• Passing list to loc causing column slice misinterpretation