Renaming columns in Pandas - 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?

Pandas

import pandas as pd

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

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

A

   Alpha  Beta
0      1     3
1      2     4

B

   A  B
0  1  3
1  2  4

C

   Alpha  B
0      1  3
1      2  4

D

   A  Beta
0  1     3
1  2     4

💡 Hint

Look at how the rename method uses the dictionary keys as old names and values as new names.

The rename method changes column names based on the dictionary. 'A' becomes 'Alpha' and 'B' becomes 'Beta'.

❓ data_output

intermediate

Result of renaming columns with inplace=True

What is the DataFrame after renaming columns inplace?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [5, 6],
    'Y': [7, 8]
})
df.rename(columns={'X': 'x_coord', 'Y': 'y_coord'}, inplace=True)
print(df)

A

   X  y_coord
0  5        7
1  6        8

B

   X  Y
0  5  7
1  6  8

C

   x_coord  Y
0        5  7
1        6  8

D

   x_coord  y_coord
0        5        7
1        6        8

💡 Hint

Using inplace=True changes the original DataFrame columns.

With inplace=True, the original DataFrame columns are renamed directly to 'x_coord' and 'y_coord'.

🔧 Debug

advanced

Identify the error in renaming columns

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

Pandas

import pandas as pd

df = pd.DataFrame({
    'col1': [1, 2],
    'col2': [3, 4]
})

# Incorrect rename syntax
new_df = df.rename(columns=['col1', 'new_col1'])
print(new_df)

A ValueError: columns must be a dict or a function

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

C No error, prints DataFrame with renamed columns

D TypeError: unhashable type: 'list'

💡 Hint

Check the type of argument passed to columns parameter in rename.

The rename method expects a dictionary or function for columns, not a list. Passing a list causes ValueError.

🚀 Application

advanced

Rename columns using a function

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

Pandas

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.lower()

B lambda x: x.upper()

C {'name': 'NAME', 'age': 'AGE'}

D ['NAME', 'AGE']

💡 Hint

The rename method can take a function to apply to each column name.

Passing a function like lambda x: x.upper() renames all columns to uppercase.

🧠 Conceptual

expert

Effect of rename with copy=False

What is the effect of using rename with copy=False on the original DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({
    'a': [1, 2],
    'b': [3, 4]
})

new_df = df.rename(columns={'a': 'alpha'}, copy=False)
new_df.loc[0, 'alpha'] = 100
print(df)

A Raises TypeError because copy=False is not allowed

B Original DataFrame df remains unchanged with columns 'a' and 'b'

C Original DataFrame df is modified and shows 'a' column with updated value 100

D Original DataFrame df has columns 'alpha' and 'b' but values unchanged

💡 Hint

copy=False means no new DataFrame is created, changes affect original.

With copy=False, rename returns a view on the original DataFrame. Changes to new_df affect df.