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.

Practice

1. What does the reset_index() method do in pandas?

easy

A. It turns the current index into a normal column and creates a new default index.

B. It deletes the DataFrame completely.

C. It sorts the DataFrame by the index values.

D. It changes all column names to uppercase.

Solution

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

   The reset_index() method moves the current index into a new column in the DataFrame.

2. Step 2: Recognize the creation of a new default index

   After moving the old index to a column, pandas creates a new default integer index starting from 0.

3. Final Answer:

   It turns the current index into a normal column and creates a new default index. -> Option A

4. Quick Check:

   reset_index() moves index to column + new default index [OK]

Hint: Resetting index moves old index to column and adds new default index [OK]

Common Mistakes:

• Thinking reset_index deletes the DataFrame
• Confusing reset_index with sorting
• Assuming reset_index changes column names

2. Which of the following is the correct syntax to reset the index of a DataFrame df and drop the old index?

easy

A. df.reset_index(drop=0)

B. df.reset_index(drop=False)

C. df.reset_index(inplace=False)

D. df.reset_index(drop=True)

Solution

1. Step 1: Identify the parameter to drop old index

   The parameter drop=True tells pandas to remove the old index instead of keeping it as a column.

2. Step 2: Confirm correct syntax usage

   The correct syntax is df.reset_index(drop=True) to reset index and drop old index.

3. Final Answer:

   df.reset_index(drop=True) -> Option D

4. Quick Check:

   Use drop=True to remove old index column [OK]

Hint: Use drop=True to remove old index column when resetting [OK]

Common Mistakes:

• Using drop=False which keeps old index as column
• Passing drop=0 instead of drop=True
• Confusing inplace parameter with drop

3. Given the DataFrame df with index labels ["a", "b", "c"] and a column value as follows:

   value
 a     10
 b     20
 c     30

What will be the output of df.reset_index()?

medium

A. value
0 10
1 20
2 30

B. index value
a 0 10
b 1 20
c 2 30

C. index value
0 a 10
1 b 20
2 c 30

D. index value
0 10 a
1 20 b
2 30 c

Solution

1. Step 1: Understand reset_index() output

   The old index labels ["a", "b", "c"] become a new column named index.

2. Step 2: Check the new default index

   A new integer index 0, 1, 2 is created for the rows.

3. Final Answer:

   index value 0 a 10 1 b 20 2 c 30 -> Option C

4. Quick Check:

   Old index becomes column 'index' + new default index [OK]

Hint: Old index becomes column named 'index' after reset_index() [OK]

Common Mistakes:

• Expecting old index labels to remain as index
• Thinking reset_index removes old index column
• Confusing column order in output

4. You run the code df.reset_index(drop=True, inplace=True) but notice the old index column still appears in your DataFrame. What is the likely cause?

medium

A. You assigned the result to a new variable instead of using inplace=True.

B. The DataFrame has a MultiIndex which reset_index() does not handle by default.

C. You forgot to set drop=True, so the old index was kept as a column.

D. The inplace parameter does not work with drop=True.

Solution

1. Step 1: Role of drop parameter

   drop=True discards the old index without adding it as a column. Default is drop=False.

2. Step 2: MultiIndex handling

   If the DataFrame has a MultiIndex, reset_index() by default adds all index levels as columns, which may appear as old index columns.

3. Final Answer:

   The DataFrame has a MultiIndex which reset_index() does not handle by default. -> Option B

4. Quick Check:

   MultiIndex causes old index columns to appear despite drop=True [OK]

Hint: MultiIndex requires special handling when resetting index [OK]

Common Mistakes:

• Forgetting to set drop=True
• Confusing inplace usage with assignment
• Believing inplace=False causes this issue

5. You have a DataFrame df with a custom index and want to reset the index but keep the old index as a column named old_index. Which code snippet achieves this?

hard

A. df.reset_index().rename(columns={'index': 'old_index'})

B. df.reset_index(drop=True).rename(columns={'index': 'old_index'})

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

D. df.reset_index(inplace=True).rename(columns={'index': 'old_index'})

Solution

1. Step 1: Reset index without dropping old index

   Using df.reset_index() moves the old index to a column named index.

2. Step 2: Rename the new column to 'old_index'

   Use rename(columns={'index': 'old_index'}) to change the column name.

3. Final Answer:

   df.reset_index().rename(columns={'index': 'old_index'}) -> Option A

4. Quick Check:

   Reset index + rename 'index' column to 'old_index' [OK]

Hint: Reset index then rename 'index' column to desired name [OK]

Common Mistakes:

• Using drop=True removes old index column
• Trying to rename before resetting index
• Using inplace=True with drop=True removes old index