MultiIndex (hierarchical indexing) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a MultiIndex from two lists.

Data Analysis Python

import pandas as pd
levels = [['A', 'B'], [1, 2]]
index = pd.MultiIndex.from_product([1])
print(index)

Drag options to blanks, or click blank then click option'

A[1, 2]

B['A', 'B']

Clevels

Dpd.Index(levels)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single list instead of a list of lists.

Passing only one level instead of both.

We use pd.MultiIndex.from_product with a list of levels. Here, levels is the list of lists representing each level.

2fill in blank

medium

Complete the code to create a MultiIndex from tuples.

Data Analysis Python

import pandas as pd
arrays = [('A', 1), ('A', 2), ('B', 1), ('B', 2)]
index = pd.MultiIndex.from_tuples([1])
print(index)

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Aarrays

Barrays[0]

Ctuple(arrays)

Dlist(arrays)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single tuple instead of a list of tuples.

Converting the list unnecessarily.

The from_tuples method expects a list of tuples. Here, arrays is already a list of tuples.

3fill in blank

hard

Fix the error in the code to access the second level of the MultiIndex.

Data Analysis Python

import pandas as pd
index = pd.MultiIndex.from_tuples([('A', 1), ('B', 2)])
second_level = index.[1][1]
print(second_level)

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Alevels

Bcodes

Clabels

Dvalues

Attempts:

3 left

💡 Hint

Common Mistakes

Using labels which is deprecated.

Trying to access levels which gives unique values, not codes.

In pandas 1.0+, codes holds the integer codes for each level. Accessing index.codes[1] gives the second level codes.

4fill in blank

hard

Fill both blanks to create a DataFrame with a MultiIndex and select data for 'B'.

Data Analysis Python

import pandas as pd
index = pd.MultiIndex.from_tuples([('A', 1), ('B', 2), ('B', 3)])
data = pd.DataFrame({'value': [10, 20, 30]}, index=[1])
selected = data.loc[[2]]
print(selected)

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Aindex

B'B'

C'A'

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using the DataFrame itself as index.

Selecting with the wrong label.

The DataFrame uses index as its index. To select rows where the first level is 'B', use data.loc['B'].

5fill in blank

hard

Fill all three blanks to create a MultiIndex DataFrame and filter rows where second level is greater than 1.

Data Analysis Python

import pandas as pd
index = pd.MultiIndex.from_tuples([('X', 1), ('X', 2), ('Y', 3)])
data = pd.DataFrame({'score': [5, 10, 15]}, index=[1])
filtered = data[[[2] for [3] in data.index if [2] > 1]]
print(filtered)

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Aindex

Bx[1]

Cx

Ddata.index

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names in the list comprehension.

Accessing the wrong level of the MultiIndex.

The DataFrame uses index as its index. The list comprehension iterates over data.index with variable x. To filter rows where the second level (index position 1) is greater than 1, use x[1] > 1.

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