Setting columns as MultiIndex in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code when setting MultiIndex columns?

Consider the following pandas DataFrame and code that sets MultiIndex columns. What will be the output of print(df.columns)?

Pandas

import pandas as pd

cols = [('A', 'foo'), ('A', 'bar'), ('B', 'baz')]
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=pd.MultiIndex.from_tuples(cols))
print(df.columns)

A Index(['A', 'foo', 'A', 'bar', 'B', 'baz'], dtype='object')

B MultiIndex([('A', 'foo'), ('A', 'bar'), ('B', 'baz')], )

C MultiIndex([('foo', 'A'), ('bar', 'A'), ('baz', 'B')], )

D Index(['foo', 'bar', 'baz'], dtype='object')

💡 Hint

Look at how pd.MultiIndex.from_tuples creates a MultiIndex from a list of tuples.

The columns are set as a MultiIndex with levels 'A' and 'B' as the first level and 'foo', 'bar', 'baz' as the second level. So the output shows a MultiIndex with tuples as column labels.

❓ data_output

intermediate

How many columns does the DataFrame have after setting MultiIndex columns?

Given the code below, how many columns does the DataFrame df have?

Pandas

import pandas as pd

arrays = [['one', 'one', 'two', 'two'], ['A', 'B', 'A', 'B']]
cols = pd.MultiIndex.from_arrays(arrays, names=['level_1', 'level_2'])
df = pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=cols)
print(len(df.columns))

A 2

B 1

C 8

D 4

💡 Hint

Count the number of tuples in the MultiIndex columns.

The MultiIndex columns have 4 tuples: ('one', 'A'), ('one', 'B'), ('two', 'A'), ('two', 'B'). So the DataFrame has 4 columns.

🔧 Debug

advanced

What error does this code raise when setting MultiIndex columns incorrectly?

What error will this code raise?

import pandas as pd
cols = [('A', 'foo'), ('B',)]
df = pd.DataFrame([[1, 2], [3, 4]], columns=pd.MultiIndex.from_tuples(cols))

A ValueError: All tuples must be the same length

B TypeError: Cannot create MultiIndex from tuples

C IndexError: tuple index out of range

D No error, runs successfully

💡 Hint

Check if all tuples have the same length when creating MultiIndex.

MultiIndex requires all tuples to have the same length. Here, one tuple has length 2, the other length 1, so it raises a ValueError.

❓ visualization

advanced

Which option correctly creates a DataFrame with MultiIndex columns and displays it?

Which code snippet will create a DataFrame with MultiIndex columns and display it correctly?

A

import pandas as pd
cols = pd.MultiIndex.from_arrays([['X', 'X', 'Y'], ['a', 'b']])
df = pd.DataFrame([[1,2,3],[4,5,6]], columns=cols)
print(df)

B

import pandas as pd
cols = pd.Index([('X', 'a'), ('X', 'b'), ('Y', 'a')])
df = pd.DataFrame([[1,2,3],[4,5,6]], columns=cols)
print(df)

C

import pandas as pd
cols = pd.MultiIndex.from_tuples([('X', 'a'), ('X', 'b'), ('Y', 'a')])
df = pd.DataFrame([[1,2,3],[4,5,6]], columns=cols)
print(df)

D

import pandas as pd
cols = [('X', 'a'), ('X', 'b'), ('Y', 'a')]
df = pd.DataFrame([[1,2,3],[4,5,6]], columns=cols)
print(df)

💡 Hint

Check how MultiIndex is created and that arrays have matching lengths.

Option C correctly creates a MultiIndex from tuples of equal length and assigns it as columns. Option C uses a list of tuples but not as MultiIndex, so columns are treated as normal tuples, causing error. Option C uses pd.Index with tuples, which does not create MultiIndex. Option C uses from_arrays with arrays of different lengths, causing error.

🚀 Application

expert

After setting MultiIndex columns, what is the value at ('B', 'two') for row 1?

Given the DataFrame below, what is the value at the column ('B', 'two') for the second row (index 1)?

Pandas

import pandas as pd

arrays = [['A', 'A', 'B', 'B'], ['one', 'two', 'one', 'two']]
cols = pd.MultiIndex.from_arrays(arrays)
data = [[10, 20, 30, 40], [50, 60, 70, 80]]
df = pd.DataFrame(data, columns=cols)
value = df.loc[1, ('B', 'two')]
print(value)

A 80

B 70

C 60

D 40

💡 Hint

Look at the column labels and the row index carefully.

The column ('B', 'two') corresponds to the fourth column. For row index 1, the value is 80.

Practice

1. What is the main purpose of setting columns as a MultiIndex in a pandas DataFrame?

easy

A. To sort columns alphabetically

B. To convert columns into rows

C. To remove duplicate columns

D. To organize columns with multiple header levels for better grouping

Solution

1. Step 1: Understand MultiIndex columns

   MultiIndex columns allow multiple levels of headers, grouping related columns together.

2. Step 2: Identify the purpose

   This organization helps in better data analysis and clarity by grouping related columns under common headers.

3. Final Answer:

   To organize columns with multiple header levels for better grouping -> Option D

4. Quick Check:

   MultiIndex = multiple header levels [OK]

Hint: MultiIndex groups columns under multiple headers [OK]

Common Mistakes:

• Confusing MultiIndex with row indexing
• Thinking MultiIndex removes duplicates
• Assuming MultiIndex sorts columns automatically

2. Which of the following is the correct syntax to set MultiIndex columns from a list of tuples cols in pandas?

easy

A. df.columns = pd.MultiIndex.from_tuples(cols)

B. df.columns = pd.MultiIndex(cols)

C. df.columns = MultiIndex.from_tuples(cols)

D. df.columns = pd.MultiIndex.tuples(cols)

Solution

1. Step 1: Recall the correct method

   The pandas method to create MultiIndex from tuples is pd.MultiIndex.from_tuples().

2. Step 2: Match syntax

   Assigning df.columns = pd.MultiIndex.from_tuples(cols) correctly sets MultiIndex columns.

3. Final Answer:

   df.columns = pd.MultiIndex.from_tuples(cols) -> Option A

4. Quick Check:

   Use from_tuples() to create MultiIndex [OK]

Hint: Use pd.MultiIndex.from_tuples() to set MultiIndex columns [OK]

Common Mistakes:

• Using pd.MultiIndex() directly without from_tuples
• Missing pd prefix for MultiIndex
• Using a non-existent method like tuples()

3. Given the code:

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

What will be the output?

medium

A. A DataFrame with MultiIndex columns showing levels 'A' and 'B' with sub-levels 'one' and 'two'

B. A DataFrame with single-level columns named 0,1,2,3

C. A DataFrame with columns named ('A', 'one'), ('A', 'two'), ('B', 'one'), ('B', 'two') as strings

D. SyntaxError due to incorrect MultiIndex assignment

Solution

1. Step 1: Create MultiIndex columns

   The list cols contains tuples representing two levels: first level 'A' or 'B', second level 'one' or 'two'.

2. Step 2: Assign MultiIndex to df.columns

   Using pd.MultiIndex.from_tuples(cols) sets these tuples as hierarchical column headers.

3. Final Answer:

   A DataFrame with MultiIndex columns showing levels 'A' and 'B' with sub-levels 'one' and 'two' -> Option A

4. Quick Check:

   MultiIndex columns display hierarchical headers [OK]

Hint: MultiIndex columns show grouped headers from tuples [OK]

Common Mistakes:

• Expecting flat column names instead of MultiIndex
• Confusing tuple strings with MultiIndex tuples
• Assuming syntax error without checking code

4. What is wrong with this code snippet for setting MultiIndex columns?

import pandas as pd
data = [[1, 2], [3, 4]]
df = pd.DataFrame(data)
cols = [('X', 'a'), ('Y', 'b')]
df.columns = pd.MultiIndex(cols)

medium

A. The list cols should be a list of strings, not tuples

B. DataFrame data shape does not match columns length

C. pd.MultiIndex() cannot take a list of tuples directly; use from_tuples()

D. MultiIndex columns must be set during DataFrame creation

Solution

1. Step 1: Check MultiIndex creation method

   The code uses pd.MultiIndex(cols), but this constructor does not accept a list of tuples directly.

2. Step 2: Correct method usage

   The correct way is to use pd.MultiIndex.from_tuples(cols) to create MultiIndex from tuples.

3. Final Answer:

   pd.MultiIndex() cannot take a list of tuples directly; use from_tuples() -> Option C

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 pd.MultiIndex() directly with tuples
• Ignoring error messages about constructor arguments
• Trying to set MultiIndex columns without matching data shape

5. You have sales data for two regions and two products:

data = [[100, 200, 150, 250], [110, 210, 160, 260]]
cols = [('Region1', 'ProductA'), ('Region1', 'ProductB'), ('Region2', 'ProductA'), ('Region2', 'ProductB')]
df = pd.DataFrame(data)

How do you set the columns as a MultiIndex to group products under regions, and then select all sales for Region1?

hard

A. Set columns with pd.MultiIndex.from_product(cols) and select with df.loc[:, 'Region1']

B. Set columns with pd.MultiIndex.from_tuples(cols) and select with df.loc[:, 'Region1']

C. Set columns with pd.MultiIndex.from_tuples(cols) and select with df['Region1']

D. Set columns with pd.MultiIndex.from_tuples(cols) and select with df['ProductA']

Solution

1. Step 1: Set MultiIndex columns correctly

   Use pd.MultiIndex.from_tuples(cols) to create MultiIndex with regions as first level and products as second.

2. Step 2: Select all sales for Region1

   Use df.loc[:, 'Region1'] to select all columns under the 'Region1' level.

3. Final Answer:

   Set columns with pd.MultiIndex.from_tuples(cols) and select with df.loc[:, 'Region1'] -> Option B

4. Quick Check:

   Use from_tuples() and df.loc[:, region] to select [OK]

Hint: Use from_tuples() and df.loc[:, region] to select grouped columns [OK]

Common Mistakes:

• Using from_product() instead of from_tuples()
• Selecting columns with df['Region1'] which may fail for MultiIndex
• Selecting wrong level like 'ProductA' instead of region