Swapping index levels in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output after swapping index levels?

Given a pandas DataFrame with a MultiIndex, what will be the index order after swapping the two levels using df.swaplevel()?

Pandas

import pandas as pd

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

A [('A', 1), ('A', 2), ('B', 1)]

B [('B', 1), ('A', 2), ('A', 1)]

C [(1, 'B'), (2, 'A'), (1, 'A')]

D [(1, 'A'), (2, 'A'), (1, 'B')]

💡 Hint

Think about what swapping index levels means: the first level becomes second and the second becomes first.

The swaplevel() method switches the positions of the two index levels. So the original index [('A',1), ('A',2), ('B',1)] becomes [(1,'A'), (2,'A'), (1,'B')].

📝 Syntax

intermediate

Which code correctly swaps index levels in pandas?

Identify the correct syntax to swap the two levels of a MultiIndex in a pandas DataFrame named df.

A df.swaplevel(0, 1)

B df.swaplevel(level1=0, level2=1)

C df.swaplevel(levels=(0,1))

D df.swap_level(0, 1)

💡 Hint

Check the exact method name and parameter names in pandas documentation.

The correct method is swaplevel with positional arguments for the two levels to swap. The other options use incorrect method names or parameter names.

❓ optimization

advanced

How to efficiently swap index levels and sort the DataFrame?

You want to swap the two levels of a MultiIndex in a DataFrame df and then sort the DataFrame by the new index order. Which option achieves this efficiently?

A df.swaplevel(levels=(1,0)).sort_values()

B df.sort_index().swaplevel()

C df.swaplevel().sort_index()

D df.sort_values().swaplevel()

💡 Hint

Remember that sorting by index should happen after swapping the index levels.

Swapping the levels first and then sorting by index ensures the DataFrame is ordered by the new index. Sorting before swapping does not sort by the new index order.

🔧 Debug

advanced

Why does this swaplevel call raise an error?

Consider this code snippet:

df.swaplevel(level=0, level=1)

Why does it raise a SyntaxError or TypeError?

Pandas

df.swaplevel(level=0, level=1)

A Because swaplevel requires positional arguments, not keywords.

B Because the method does not accept keyword arguments named 'level' twice.

C Because the index levels do not exist in the DataFrame.

D Because swaplevel only works on single-level indexes.

💡 Hint

Check the function call syntax and parameter names carefully.

The code uses the same keyword argument 'level' twice, which is invalid syntax in Python. The correct way is to use positional arguments or keywords i=0 and j=1.

🧠 Conceptual

expert

What happens to the index names after swapping levels?

If a MultiIndex has names ['first', 'second'], what will be the names after calling df.swaplevel() without arguments?

Pandas

import pandas as pd
index = pd.MultiIndex.from_tuples([('x', 1), ('y', 2)], names=['first', 'second'])
df = pd.DataFrame({'val': [5, 10]}, index=index)
swapped = df.swaplevel()

A ['second', 'first']

B ['first', 'second']

C ['first_second', 'second_first']

D [None, None]

💡 Hint

Swapping index levels also swaps their names in the same order.

The swaplevel() method swaps both the index levels and their names. So the names change from ['first', 'second'] to ['second', 'first'].

Practice

1. What does the swaplevel() function do in a pandas DataFrame with a multi-level index?

easy

A. It deletes one of the index levels permanently.

B. It switches the positions of two index levels without changing the data.

C. It sorts the DataFrame by the index levels.

D. It converts the multi-level index into columns.

Solution

1. Step 1: Understand the purpose of swaplevel()

   The swaplevel() function is designed to reorder the levels of a multi-level index by swapping two specified levels.

2. Step 2: Confirm it does not alter data values

   Swapping index levels changes only the order of the index labels, not the underlying data or its values.

3. Final Answer:

   It switches the positions of two index levels without changing the data. -> Option B

4. Quick Check:

   swaplevel() swaps index levels [OK]

Hint: Remember: swaplevel() only changes index order, not data [OK]

Common Mistakes:

• Thinking swaplevel() deletes index levels
• Confusing swaplevel() with sorting functions
• Assuming swaplevel() changes data values

2. Which of the following is the correct syntax to swap the first and second levels of a DataFrame's multi-level index named df?

easy

A. df.swaplevel()

B. df.swaplevel(1, 2)

C. df.swaplevel('level1', 'level3')

D. df.swaplevel(0, 1)

Solution

1. Step 1: Identify index levels by position

   Index levels are zero-based, so the first level is 0 and the second is 1.

2. Step 2: Use swaplevel() with correct positions

   Calling df.swaplevel(0, 1) swaps the first and second levels correctly.

3. Final Answer:

   df.swaplevel(0, 1) -> Option D

4. Quick Check:

   swaplevel(0, 1) swaps first two levels [OK]

Hint: Use zero-based positions to swap levels: 0 and 1 for first two [OK]

Common Mistakes:

• Using incorrect level numbers like 1 and 2 for first two levels
• Calling swaplevel() without arguments
• Using level names that don't exist

3. Given the following DataFrame df with a multi-level index:

import pandas as pd
index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1), ('B', 2)], names=['letter', 'number'])
df = pd.DataFrame({'value': [10, 20, 30, 40]}, index=index)
print(df.swaplevel('letter', 'number'))

What will be the first row's index after swapping?

medium

A. (1, 'A')

B. ('A', 1)

C. ('number', 'letter')

D. (10, 20)

Solution

1. Step 1: Understand original index order

   The original index is [('A',1), ('A',2), ('B',1), ('B',2)] with levels 'letter' then 'number'.

2. Step 2: Swap levels 'letter' and 'number'

   After swapping, the order becomes 'number' then 'letter'. So the first index becomes (1, 'A').

3. Final Answer:

   (1, 'A') -> Option A

4. Quick Check:

   Swapped index order changes ('A',1) to (1,'A') [OK]

Hint: Swapping levels flips their order in the index tuple [OK]

Common Mistakes:

• Assuming index values stay in same order
• Confusing index values with column values
• Mixing up level names and positions

4. You try to swap index levels in a DataFrame using df.swaplevel('first', 'second') but get a KeyError. What is the most likely cause?

medium

A. The level names 'first' and 'second' do not exist in the index.

B. swaplevel() requires integer positions, not names.

C. The DataFrame has no multi-level index.

D. You must reset the index before swapping levels.

Solution

1. Step 1: Check index level names

   swaplevel() accepts level names, but if the names given do not exist, a KeyError occurs.

2. Step 2: Confirm cause of KeyError

   The error means 'first' and 'second' are not valid level names in the DataFrame's index.

3. Final Answer:

   The level names 'first' and 'second' do not exist in the index. -> Option A

4. Quick Check:

   KeyError means invalid level names [OK]

Hint: Verify level names exist before swapping [OK]

Common Mistakes:

• Assuming swaplevel() only accepts integers
• Not checking if DataFrame has multi-level index
• Resetting index unnecessarily

5. You have a DataFrame df with a multi-level index named ['city', 'year', 'month']. You want to swap the 'city' and 'month' levels and then sort the DataFrame by the new index order. Which code snippet correctly does this?

hard

A. df_swapped = df.swaplevel('year', 'month').sort_index()

B. df_swapped = df.swaplevel(0, 1).sort_values()

C. df_swapped = df.swaplevel('city', 'month').sort_index()

D. df_swapped = df.swaplevel('city', 'month').reset_index()

Solution

1. Step 1: Identify correct levels to swap

   The levels to swap are 'city' and 'month', which are the first and third levels.

2. Step 2: Use swaplevel() with level names and sort_index()

   Calling swaplevel('city', 'month') swaps these levels. Then sort_index() sorts the DataFrame by the new index order.

3. Final Answer:

   df_swapped = df.swaplevel('city', 'month').sort_index() -> Option C

4. Quick Check:

   Swap specified levels then sort index [OK]

Hint: Swap by names, then sort_index() to reorder rows [OK]

Common Mistakes:

• Swapping wrong levels by position
• Using sort_values() instead of sort_index()
• Resetting index instead of sorting