Setting columns as MultiIndex in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a MultiIndex from two lists and set it as the DataFrame columns.

Pandas

import pandas as pd

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

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

Apd.MultiIndex(arrays)

Barrays

Cpd.Index(arrays)

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original arrays list instead of the MultiIndex object.

Trying to create a new MultiIndex inside the DataFrame constructor.

The index variable holds the MultiIndex object created from the arrays. Setting columns=index applies the MultiIndex as columns.

2fill in blank

medium

Complete the code to set the columns of the DataFrame to a MultiIndex created from tuples.

Pandas

import pandas as pd

cols = [('A', 'one'), ('A', 'two'), ('B', 'one'), ('B', 'two')]
mi = pd.MultiIndex.from_tuples(cols, names=['first', 'second'])
df = pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]])
df.columns = [1]
print(df)

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

Ami

Bpd.MultiIndex(cols)

Cpd.Index(cols)

Dcols

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning the list of tuples directly to df.columns.

Using pd.Index instead of pd.MultiIndex.

The variable mi holds the MultiIndex created from tuples. Assigning it to df.columns sets the MultiIndex as columns.

3fill in blank

hard

Fix the error in the code to correctly set a MultiIndex as DataFrame columns.

Pandas

import pandas as pd

arrays = [['X', 'X', 'Y', 'Y'], ['a', 'b', 'a', 'b']]
mi = pd.MultiIndex.from_arrays(arrays)
df = pd.DataFrame([[10, 20, 30, 40], [50, 60, 70, 80]])
df.columns = [1]
print(df)

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

Apd.Index(arrays)

Barrays

Cmi

Darrays.T

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning the original arrays list instead of the MultiIndex.

Trying to transpose arrays which is not valid here.

The MultiIndex object mi must be assigned to df.columns. Assigning arrays or other forms causes errors.

4fill in blank

hard

Fill both blanks to create a MultiIndex from product of two lists and set it as DataFrame columns.

Pandas

import pandas as pd

levels = [['A', 'B'], ['one', 'two']]
mi = pd.MultiIndex.[1](levels)
df = pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]])
df.columns = mi.[2](['first', 'second'])
print(df)

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

Afrom_product

Bfrom_tuples

Cset_names

Drename

Attempts:

3 left

💡 Hint

Common Mistakes

Using from_tuples instead of from_product.

Using rename instead of set_names.

pd.MultiIndex.from_product creates a MultiIndex from the Cartesian product of the lists. Then set_names assigns names to the levels.

5fill in blank

hard

Fill all three blanks to create a MultiIndex from zipped lists, set names, and assign as DataFrame columns.

Pandas

import pandas as pd

arrays = list(zip(['X', 'X', 'Y', 'Y'], ['a', 'b', 'a', 'b']))
mi = pd.MultiIndex.from_tuples([1])
mi = mi.[2](['level1', 'level2'])
df = pd.DataFrame([[10, 20, 30, 40], [50, 60, 70, 80]])
df.columns = [3]
print(df)

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

Aarrays

Bset_names

Cmi

Drename

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning the zipped list directly to columns without creating MultiIndex.

Using rename instead of set_names.

Assigning the wrong variable to df.columns.

The zipped list arrays is used to create the MultiIndex with from_tuples. Then set_names assigns level names. Finally, mi is assigned to df.columns.

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