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.