Renaming columns in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of renaming columns with a dictionary

What is the output DataFrame after renaming columns using the given dictionary?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2],
    'B': [3, 4],
    'C': [5, 6]
})

new_df = df.rename(columns={'A': 'X', 'B': 'Y'})
print(new_df)

A

   X  Y  C
0  1  3  5
1  2  4  6

B

   A  B  C
0  1  3  5
1  2  4  6

C

   X  B  C
0  1  3  5
1  2  4  6

D

   X  Y
0  1  3
1  2  4

💡 Hint

Check which columns are renamed and which remain unchanged.

The rename method changes only the columns specified in the dictionary. Columns 'A' and 'B' become 'X' and 'Y'. Column 'C' stays the same.

❓ data_output

intermediate

Result of renaming columns with inplace=True

What is the DataFrame after renaming columns with inplace=True?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'one': [10, 20],
    'two': [30, 40]
})
df.rename(columns={'one': 'first', 'two': 'second'}, inplace=True)
print(df)

A

   one  two
0   10   30
1   20   40

B

   first  second
0     10      30
1     20      40

C

   first  two
0     10    30
1     20    40

D

   one  second
0   10     30
1   20     40

💡 Hint

inplace=True changes the original DataFrame.

Using inplace=True modifies the original DataFrame columns directly.

🔧 Debug

advanced

Identify the error in renaming columns code

What error does this code raise when trying to rename columns?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
new_df = df.rename(columns=['A', 'B'])

A TypeError: unhashable type: 'list'

B No error, code runs successfully

C TypeError: rename() got an unexpected keyword argument 'columns' with list

D TypeError: rename() argument 'columns' must be a dict or callable

💡 Hint

Check the expected type for the columns parameter in rename.

The rename method expects a dictionary or callable for columns, not a list.

🚀 Application

advanced

Rename columns using a function

Which option correctly renames all columns to uppercase using a function?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [25, 30]
})

new_df = df.rename(columns=??? )
print(new_df)

A lambda x: x.upper()

B {col: col.upper() for col in df.columns}

C str.upper

D map(str.upper, df.columns)

💡 Hint

The columns parameter can accept a function applied to each column name.

Passing str.upper applies the function to each column name to rename them.

🧠 Conceptual

expert

Effect of renaming columns on DataFrame indexing

After renaming columns in a DataFrame, which statement is true about the DataFrame's index?

A Renaming columns does not affect the DataFrame's index.

B Renaming columns resets the index to default integer index.

C Renaming columns changes the DataFrame's index values.

D Renaming columns removes the index entirely.

💡 Hint

Think about what columns and index represent separately.

Renaming columns only changes column labels, not the row index.

Practice

1. What does the df.rename(columns={'old': 'new'}) function do in pandas?

easy

A. It sorts the DataFrame by the column 'old'.

B. It deletes the column named 'old' from the DataFrame.

C. It adds a new column named 'new' with values from 'old'.

D. It changes the column name from 'old' to 'new' in the DataFrame.

Solution

1. Step 1: Understand the rename function

   The rename method changes column names based on a dictionary mapping old names to new names.

2. Step 2: Identify the effect of the dictionary

   The dictionary {'old': 'new'} means the column named 'old' will be renamed to 'new'.

3. Final Answer:

   It changes the column name from 'old' to 'new' in the DataFrame. -> Option D

4. Quick Check:

   Rename columns = change names [OK]

Hint: Rename columns using a dictionary with old:new pairs [OK]

Common Mistakes:

• Thinking rename deletes columns
• Confusing rename with adding columns
• Assuming rename sorts data

2. Which of the following is the correct syntax to rename column 'A' to 'Alpha' in a pandas DataFrame df?

easy

A. df.rename('A' to 'Alpha')

B. df.rename(columns={'A': 'Alpha'}, inplace=True)

C. df.rename_columns('A', 'Alpha')

D. df.columns.rename('A', 'Alpha')

Solution

1. Step 1: Recall pandas rename syntax

   The correct syntax uses df.rename(columns={old_name: new_name}, inplace=True) to rename columns.

2. Step 2: Match the syntax to options

   df.rename(columns={'A': 'Alpha'}, inplace=True) matches the correct syntax exactly, including inplace=True to change the original DataFrame.

3. Final Answer:

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

4. Quick Check:

   Use rename with columns dict and inplace [OK]

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

Common Mistakes:

• Using wrong method names like rename_columns
• Missing columns= keyword
• Forgetting inplace=True to apply changes

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({'X': [1,2], 'Y': [3,4]})
df.rename(columns={'X': 'A', 'Y': 'B'}, inplace=True)
print(df.columns.tolist())

What is the output?

medium

A. ['A', 'B']

B. ['X', 'Y']

C. ['X', 'B']

D. ['A', 'Y']

Solution

1. Step 1: Understand the rename operation

   The rename changes 'X' to 'A' and 'Y' to 'B' in the DataFrame columns.

2. Step 2: Check inplace=True effect

   Since inplace=True, the original DataFrame columns are updated.

3. Final Answer:

   ['A', 'B'] -> Option A

4. Quick Check:

   Renamed columns list = ['A', 'B'] [OK]

Hint: inplace=True updates columns immediately [OK]

Common Mistakes:

• Ignoring inplace=True and expecting old names
• Partial renaming confusion
• Printing DataFrame instead of columns

4. What is wrong with this code snippet?

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

medium

A. The dictionary keys and values are reversed.

B. inplace=True is not a valid argument for rename.

C. The dictionary should be passed to the columns parameter.

D. The rename method cannot rename columns.

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

   The code passes the dictionary as a positional argument without specifying columns=, so it will not rename columns.

3. Final Answer:

   The dictionary should be passed to the columns parameter. -> Option C

4. Quick Check:

   Use columns= to rename columns [OK]

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

Common Mistakes:

• Passing dictionary without columns= keyword
• Confusing inplace argument
• Thinking rename can't change columns

5. You have a DataFrame df with columns ['temp', 'humidity', 'pressure']. You want to rename only 'temp' to 'temperature' without changing other columns. Which code correctly does this and keeps the change in df?

hard

A. df.rename(columns={'temp': 'temperature'}, inplace=True)

B. df.columns = ['temperature', 'humidity', 'pressure', 'wind']

C. df.rename({'temp': 'temperature'})

D. df.rename(columns={'temp': 'temperature'})

Solution

1. Step 1: Identify correct rename syntax with inplace

   To rename a single column and keep changes in the original DataFrame, use rename(columns={...}, inplace=True).

2. Step 2: Check other options

   df.columns = ['temperature', 'humidity', 'pressure', 'wind'] replaces all columns, which is risky. Options C and D do not use inplace=True, so changes won't persist in df.

3. Final Answer:

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

4. Quick Check:

   Use inplace=True to keep rename changes [OK]

Hint: Use inplace=True to save rename changes in original DataFrame [OK]

Common Mistakes:

• Replacing all columns instead of renaming one
• Forgetting inplace=True to save changes
• Using rename without columns= keyword