Resetting index in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of resetting index without dropping?

Given the DataFrame df with index labels [10, 20, 30] and a column value as [100, 200, 300], what is the result of df.reset_index()?

Pandas

import pandas as pd

df = pd.DataFrame({'value': [100, 200, 300]}, index=[10, 20, 30])
result = df.reset_index()

A [{'index': 0, 'value': 100}, {'index': 1, 'value': 200}, {'index': 2, 'value': 300}]

B [{'value': 100}, {'value': 200}, {'value': 300}]

C [{'index': 10, 'value': 100}, {'index': 20, 'value': 200}, {'index': 30, 'value': 300}]

D [{'index': 10, 'value': 100}, {'index': 20, 'value': 200}, {'index': 30, 'value': 300}, {'index': 40, 'value': 400}]

💡 Hint

Resetting index without dropping keeps the old index as a column.

When you reset the index without dropping, the old index becomes a new column named 'index'. The DataFrame rows keep their original order and values.

❓ query_result

intermediate

What happens when resetting index with drop=True?

Given the same DataFrame df as before, what is the output of df.reset_index(drop=True)?

Pandas

import pandas as pd

df = pd.DataFrame({'value': [100, 200, 300]}, index=[10, 20, 30])
result = df.reset_index(drop=True)

A [{'value': 100}, {'value': 200}, {'value': 300}] with default index [0,1,2]

B [{'index': 10, 'value': 100}, {'index': 20, 'value': 200}, {'index': 30, 'value': 300}]

C [{'value': 100}, {'value': 200}, {'value': 300}] with index [10,20,30]

D [{'index': 0, 'value': 100}, {'index': 1, 'value': 200}, {'index': 2, 'value': 300}]

💡 Hint

Dropping the index means it is removed and replaced by default integer index.

Using drop=True removes the old index completely and replaces it with a new default integer index starting at 0.

📝 Syntax

advanced

Which option causes a syntax error when resetting index?

Which of the following code snippets will cause a syntax error?

A df.reset_index(inplace=True)

B df.reset_index(drop==True)

C df.reset_index(drop=True, inplace=True)

D df.reset_index()

💡 Hint

Check the syntax of keyword arguments in function calls.

Option B uses 'drop==True' which is invalid syntax for keyword arguments. It should be 'drop=True'.

❓ optimization

advanced

How to reset index efficiently without creating a copy?

Which option resets the index of DataFrame df in place without creating a new DataFrame?

A df.reset_index(inplace=True)

B df = df.reset_index()

C df.reset_index(drop=True)

D df.reset_index(drop=True, inplace=False)

💡 Hint

Look for the parameter that modifies the DataFrame in place.

Using inplace=True modifies the original DataFrame without returning a new one, which is more memory efficient.

🧠 Conceptual

expert

What is the effect of resetting a MultiIndex DataFrame without drop?

Given a DataFrame df with a MultiIndex of two levels, what happens when you call df.reset_index() without any arguments?

A Only the first level of the MultiIndex becomes a column, the rest remain as index

B The DataFrame raises an error because reset_index does not support MultiIndex

C The MultiIndex is preserved and no columns are added

D Both index levels become columns and the index is reset to default integer index

💡 Hint

Resetting index without drop moves all index levels to columns.

reset_index() on a MultiIndex moves all index levels into columns and resets the index to default integers.