Resetting index in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to reset the index of the DataFrame df.

Pandas

df = df.[1]()

Drag options to blanks, or click blank then click option'

Areset_index

Bset_index

Cdrop

Dsort_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using set_index() instead of reset_index().

Trying to use drop() which removes columns, not reset index.

The reset_index() method resets the index of the DataFrame to the default integer index.

2fill in blank

medium

Complete the code to reset the index of df and drop the old index.

Pandas

df = df.reset_index([1]=True)

Drag options to blanks, or click blank then click option'

Adrop

Bappend

Clevel

Dinplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using inplace=True which modifies the DataFrame in place but does not drop the old index.

Using append=True which adds the old index as a new level.

The drop=True argument removes the old index instead of adding it as a column.

3fill in blank

hard

Fix the error in the code to reset the index of df without adding the old index as a column.

Pandas

df.reset_index([1]=True)

Drag options to blanks, or click blank then click option'

Alevel

Binplace

Cdrop

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using inplace=True alone does not drop the old index column.

Using append=True adds the old index as a new level.

To avoid adding the old index as a column, use drop=True in reset_index().

4fill in blank

hard

Fill both blanks to reset the index of df in place and drop the old index.

Pandas

df.reset_index([1]=True, [2]=True)

Drag options to blanks, or click blank then click option'

Ainplace

Bdrop

Cappend

Dlevel

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing append with drop.

Forgetting to set inplace=True when wanting to modify the DataFrame directly.

Use inplace=True to modify df directly and drop=True to remove the old index column.

5fill in blank

hard

Fill all three blanks to reset the index of df, drop the old index, and keep the changes in place.

Pandas

df.reset_index([1]=True, [2]=True, [3]=False)

Drag options to blanks, or click blank then click option'

Ainplace

Bdrop

Cverify_integrity

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using append=True which adds the old index as a new column.

Not understanding the purpose of verify_integrity argument.

Use inplace=True to modify df directly, drop=True to remove the old index, and verify_integrity=False to skip index integrity check.

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