Setting a column as index in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of setting a column as index?

Given the DataFrame:

import pandas as pd
df = pd.DataFrame({"Name": ["Alice", "Bob"], "Age": [25, 30]})
df2 = df.set_index("Name")
print(df2)

What will be printed?

Pandas

import pandas as pd
df = pd.DataFrame({"Name": ["Alice", "Bob"], "Age": [25, 30]})
df2 = df.set_index("Name")
print(df2)

A

   Name  Age
0  Alice   25
1    Bob   30

B

   Age
0   25
1   30

C

Name  Age
Alice 25
Bob 30

D

       Age
Name       
Alice    25
Bob      30

💡 Hint

Setting a column as index moves that column to the row labels.

Using set_index("Name") makes the 'Name' column the index (row labels). The DataFrame shows 'Age' as the only column with 'Name' as the index.

📝 Syntax

intermediate

Which code correctly sets 'ID' as index in pandas?

Choose the code snippet that correctly sets the 'ID' column as the index of DataFrame df.

Pandas

import pandas as pd
df = pd.DataFrame({"ID": [1, 2], "Value": [100, 200]})

A df.set_index('ID')

B df.set_index(ID)

C df.set_index(['ID'])

D df.set_index(ID, inplace=True)

💡 Hint

The column name must be a string inside quotes.

Option A correctly passes the column name as a string. Option A and D use ID without quotes causing NameError. Option A passes a list which works but returns a DataFrame with a single-level index, not a MultiIndex.

❓ optimization

advanced

How to set a column as index modifying the original DataFrame?

You want to set the 'Date' column as index in DataFrame df and keep the change without creating a new DataFrame. Which option does this efficiently?

Pandas

import pandas as pd
df = pd.DataFrame({"Date": ["2023-01-01", "2023-01-02"], "Sales": [200, 250]})

A df.index = df.set_index('Date')

B df = df.set_index('Date')

C df.set_index('Date', inplace=True)

D df.index = df['Date']

💡 Hint

Look for the parameter that changes the DataFrame in place.

Option C uses inplace=True to modify df directly. Option C creates a new DataFrame but does not modify df in place. Option C sets index but does not remove the 'Date' column. Option C assigns a DataFrame to index which is invalid.

🔧 Debug

advanced

Why does this code raise an error?

Consider this code:

import pandas as pd
df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
df.set_index(A)

Why does it raise an error?

Pandas

import pandas as pd
df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
df.set_index(A)

A TypeError because set_index expects a list

B NameError because A is not defined as a variable

C KeyError because column 'A' does not exist

D SyntaxError due to missing quotes

💡 Hint

Check if the column name is passed as a string or variable.

The code uses A without quotes, so Python looks for a variable named A which is not defined, causing NameError.

🧠 Conceptual

expert

What happens if you set a column as index but it contains duplicate values?

If you run df.set_index('Category') where 'Category' column has duplicate values, what is the effect on the DataFrame index?

A The index will allow duplicates, so multiple rows can share the same index label

B The DataFrame converts the index to a MultiIndex to handle duplicates

C Duplicates are automatically removed from the index

D It raises a ValueError because index must be unique

💡 Hint

Think about whether pandas requires index labels to be unique.

Pandas allows index labels to be duplicated. Setting a column with duplicates as index does not raise errors and keeps all rows.