Renaming columns in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to rename the column 'old' to 'new' in the DataFrame df.

Pandas

df.rename(columns=[1], inplace=True)

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

A{'old': 'new'}

B['old', 'new']

C('old', 'new')

D'old:new'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list or tuple instead of a dictionary.

Passing a string instead of a dictionary.

The rename method requires a dictionary mapping old column names to new ones. So {'old': 'new'} is correct.

2fill in blank

medium

Complete the code to rename multiple columns: 'A' to 'Alpha' and 'B' to 'Beta'.

Pandas

df.rename(columns=[1], inplace=True)

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

A'A:Alpha, B:Beta'

B['A', 'Alpha', 'B', 'Beta']

C{'A': 'Alpha', 'B': 'Beta'}

D('A', 'Alpha', 'B', 'Beta')

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list or tuple instead of a dictionary.

Forgetting to set inplace=True to modify the DataFrame.

To rename multiple columns, pass a dictionary with all old-to-new mappings to columns.

3fill in blank

hard

Fix the error in the code to rename column 'score' to 'points' without changing the original DataFrame.

Pandas

new_df = df.rename(columns=[1])

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

A('score', 'points')

B['score', 'points']

C'score:points'

D{'score': 'points'}

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list or tuple instead of a dictionary.

Using inplace=True which modifies the original DataFrame.

The rename method returns a new DataFrame if inplace is not set. The columns argument must be a dictionary.

4fill in blank

hard

Fill both blanks to rename columns 'x' to 'X' and 'y' to 'Y' and assign the result to df_new.

Pandas

df_new = df.rename(columns=[1], inplace=[2])

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

A{'x': 'X', 'y': 'Y'}

BTrue

CFalse

D{'X': 'x', 'Y': 'y'}

Attempts:

3 left

💡 Hint

Common Mistakes

Setting inplace=True which modifies the original DataFrame.

Using the wrong dictionary mapping.

To rename columns and keep the original DataFrame unchanged, pass the mapping dictionary and set inplace=False (or omit it).

5fill in blank

hard

Fill all three blanks to rename columns 'a' to 'alpha', 'b' to 'beta', and 'c' to 'gamma' and save the result in df2 without changing df.

Pandas

df2 = df.rename(columns=[1], inplace=[2], errors=[3])

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

A{'a': 'alpha', 'b': 'beta', 'c': 'gamma'}

BFalse

C'ignore'

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Setting inplace=True which modifies the original DataFrame.

Not handling errors when columns might be missing.

Use a dictionary for columns, set inplace=False to keep original DataFrame, and errors='ignore' to avoid errors if columns are missing.

Practice

1. What is the main purpose of renaming columns in a pandas DataFrame?

easy

A. To delete unwanted columns

B. To make the data easier to read and understand

C. To sort the data by column values

D. To change the data type of columns

Solution

1. Step 1: Understand the role of column names

   Column names help identify what data each column holds.

2. Step 2: Purpose of renaming

   Renaming columns improves clarity and makes the data easier to work with.

3. Final Answer:

   To make the data easier to read and understand -> Option B

4. Quick Check:

   Renaming columns = better readability [OK]

Hint: Renaming columns improves clarity and usability [OK]

Common Mistakes:

• Confusing renaming with deleting columns
• Thinking renaming changes data values
• Assuming renaming sorts data

2. Which of the following is the correct syntax to rename columns in a pandas DataFrame df using a dictionary mapping old names to new names?

easy

A. df.rename(columns={'old_name': 'new_name'}, inplace=True)

B. df.rename({'old_name': 'new_name'}, axis=0)

C. df.rename(columns=['old_name', 'new_name'])

D. df.rename(columns='old_name:new_name')

Solution

1. Step 1: Identify correct method parameters

   The rename method requires a dictionary for columns with keys as old names and values as new names.

2. Step 2: Check syntax correctness

   df.rename(columns={'old_name': 'new_name'}, inplace=True) uses columns= with a dictionary and inplace=True, which is correct syntax.

3. Final Answer:

   df.rename(columns={'old_name': 'new_name'}, inplace=True) -> Option A

4. Quick Check:

   Use rename(columns=dict) with inplace=True [OK]

Hint: Use rename(columns={old:new}) with inplace=True to rename [OK]

Common Mistakes:

• Omitting 'columns=' keyword
• Passing list instead of dictionary
• Using axis=0 for column renaming

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({
  'A': [1, 2],
  'B': [3, 4]
})
df_renamed = df.rename(columns={'A': 'Alpha', 'B': 'Beta'})
print(df_renamed.columns.tolist())

What will be the output?

medium

A. ['Alpha', 'Beta']

B. ['A', 'B']

C. ['Alpha', 'B']

D. Error: rename requires inplace=True

Solution

1. Step 1: Understand rename without inplace

   Calling rename without inplace=True returns a new DataFrame with renamed columns.

2. Step 2: Check the printed columns

   The new DataFrame df_renamed has columns renamed to 'Alpha' and 'Beta'.

3. Final Answer:

   ['Alpha', 'Beta'] -> Option A

4. Quick Check:

   rename() returns new DataFrame with renamed columns [OK]

Hint: rename() returns new DataFrame unless inplace=True [OK]

Common Mistakes:

• Assuming original df columns change without inplace=True
• Expecting error without inplace=True
• Confusing columns list output

4. What is wrong with this code snippet?

df.rename({'old': 'new'}, inplace=True)

medium

A. The 'inplace' parameter is not valid for rename

B. The rename method cannot rename columns

C. The dictionary keys and values should be lists, not strings

D. The dictionary should be passed to the 'columns' parameter

Solution

1. Step 1: Check rename method parameters

   The rename method requires the dictionary to be passed with the keyword 'columns=' to rename columns.

2. Step 2: Identify the error in the code

   Passing the dictionary without 'columns=' causes pandas to treat it as index renaming, not columns.

3. Final Answer:

   The dictionary should be passed to the 'columns' parameter -> Option D

4. Quick Check:

   Use rename(columns=dict) to rename columns [OK]

Hint: Always use columns= when renaming columns [OK]

Common Mistakes:

• Passing dictionary without columns= keyword
• Confusing index renaming with column renaming
• Thinking inplace=False causes error

5. You have a DataFrame df with columns ['name', 'age', 'city']. You want to rename 'name' to 'full_name' and 'city' to 'location' without changing 'age'. Which code correctly renames only these columns and keeps the rest unchanged?

hard

A. df.rename(columns=['full_name', 'age', 'location'], inplace=True)

B. df.columns = ['full_name', 'age', 'location']

C. df.rename(columns={'name': 'full_name', 'city': 'location'}, inplace=True)

D. df.rename({'name': 'full_name', 'city': 'location'}, inplace=True)

Solution

1. Step 1: Understand selective renaming

   Using rename with a dictionary for columns changes only specified columns, leaving others unchanged.

2. Step 2: Check each option

   df.rename(columns={'name': 'full_name', 'city': 'location'}, inplace=True) uses columns= with a dictionary for selective renaming, leaving 'age' unchanged. In contrast, assigning to df.columns replaces all column names and requires the list to match the exact current order. Passing the dictionary directly without columns= attempts to rename the index instead of columns. Using a list instead of a dictionary for columns= will raise a TypeError.

3. Final Answer:

   df.rename(columns={'name': 'full_name', 'city': 'location'}, inplace=True) -> Option C

4. Quick Check:

   Use rename(columns=dict) for selective renaming [OK]

Hint: Use rename(columns=dict) to rename selected columns only [OK]

Common Mistakes:

• Replacing all columns instead of selective renaming
• Omitting columns= keyword
• Using list instead of dictionary for columns