Resetting MultiIndex to columns in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of resetting MultiIndex to columns

What is the output DataFrame after resetting the MultiIndex to columns in the following code?

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)
result = df.reset_index()

A [[('A', 1), 10], [('A', 2), 20], [('B', 1), 30]] with columns ['index', 'Value']

B [['A', 1, 10], ['A', 2, 20], ['B', 1, 30]] with columns ['level_0', 'level_1', 'Value']

C [[0, 10], [1, 20], [2, 30]] with columns ['index', 'Value']

D [['A', 10], ['B', 20], ['C', 30]] with columns ['level_0', 'Value']

💡 Hint

Resetting MultiIndex moves index levels to columns named after the index levels or default names.

reset_index() converts each level of the MultiIndex into a separate column. The columns are named after the index levels or default names like 'level_0', 'level_1'. The original index is replaced by a default integer index.

❓ data_output

intermediate

Number of columns after resetting MultiIndex

Given a DataFrame with a MultiIndex of 3 levels and 2 data columns, how many columns will the DataFrame have after resetting the MultiIndex?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('X', 'Y', 'Z'), ('X', 'Y', 'W')])
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=index)
result = df.reset_index()

A 2 columns

B 3 columns

C 5 columns

D 4 columns

💡 Hint

Each MultiIndex level becomes a column plus the original data columns remain.

The MultiIndex has 3 levels, so resetting it adds 3 columns. The original 2 data columns remain. Total columns = 3 + 2 = 5.

🔧 Debug

advanced

Error when resetting MultiIndex with drop=True

What error will this code raise and why? import pandas as pd index = pd.MultiIndex.from_tuples([('a', 1), ('b', 2)]) df = pd.DataFrame({'val': [100, 200]}, index=index) result = df.reset_index(drop=True)

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('a', 1), ('b', 2)])
df = pd.DataFrame({'val': [100, 200]}, index=index)
result = df.reset_index(drop=True)

A No error; result is DataFrame with default integer index and no index columns

B TypeError because drop=True is invalid for MultiIndex

C ValueError because MultiIndex cannot be dropped

D KeyError because index columns are missing

💡 Hint

drop=True removes the index instead of converting it to columns.

reset_index(drop=True) removes the index and replaces it with a default integer index. This works fine even with MultiIndex.

🚀 Application

advanced

Using reset_index to prepare data for CSV export

You have a DataFrame with a MultiIndex and want to save it to CSV with all index levels as columns. Which code snippet correctly prepares the DataFrame before saving?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('cat', 'small'), ('dog', 'large')])
df = pd.DataFrame({'weight': [5, 20]}, index=index)

A df.reset_index().to_csv('output.csv', index=False)

B df.to_csv('output.csv', index=True)

C df.reset_index(drop=True).to_csv('output.csv', index=False)

D df.to_csv('output.csv', index=False)

💡 Hint

To save index levels as columns, reset the index first without dropping it.

reset_index() moves MultiIndex levels to columns. Then saving with index=False avoids saving the old index again.

🧠 Conceptual

expert

Effect of reset_index on MultiIndex with named levels

Consider a DataFrame with a MultiIndex whose levels are named 'City' and 'Year'. After calling reset_index(), what will be the names of the new columns created from the index?

A Columns named 'level_0' and 'level_1'

B No new columns are created

C Columns named 'index_0' and 'index_1'

D Columns named 'City' and 'Year'

💡 Hint

Named index levels become columns with the same names after reset_index.

When MultiIndex levels have names, reset_index() uses those names as column names instead of default 'level_0', 'level_1'.