Renaming columns in Pandas - Mini Project: Build & Apply

Renaming columns

📖 Scenario: You have a small table of sales data with column names that are not very clear. You want to rename the columns to make them easier to understand.

🎯 Goal: Rename the columns of a pandas DataFrame to more descriptive names.

📋 What You'll Learn

Create a pandas DataFrame with specific columns and data

Create a dictionary to map old column names to new column names

Use the pandas rename method with the dictionary to rename columns

Print the resulting DataFrame

💡 Why This Matters

🌍 Real World

Renaming columns is common when working with data from different sources that use unclear or inconsistent column names.

💼 Career

Data scientists and analysts often rename columns to make data easier to understand and to prepare it for analysis or reporting.

1

Create the initial DataFrame

Create a pandas DataFrame called df with these columns and data: 'prd' with values [101, 102, 103], 'qty' with values [5, 10, 15], and 'amt' with values [100, 200, 300].

Pandas

import pandas as pd
# Create the DataFrame df with columns 'prd', 'qty', 'amt' and the given values
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Create the renaming dictionary

Create a dictionary called rename_dict that maps the old column names to new names: 'prd' to 'ProductID', 'qty' to 'Quantity', and 'amt' to 'Amount'.

Pandas

import pandas as pd

df = pd.DataFrame({
    'prd': [101, 102, 103],
    'qty': [5, 10, 15],
    'amt': [100, 200, 300]
})
# Create the dictionary rename_dict to map old column names to new names
# Your code here

Hint

Create a dictionary with old column names as keys and new column names as values.

3

Rename the columns using the dictionary

Use the pandas rename method on df with the columns=rename_dict argument and inplace=True to rename the columns.

Pandas

import pandas as pd

df = pd.DataFrame({
    'prd': [101, 102, 103],
    'qty': [5, 10, 15],
    'amt': [100, 200, 300]
})

rename_dict = {'prd': 'ProductID', 'qty': 'Quantity', 'amt': 'Amount'}
# Rename the columns of df using rename_dict with inplace=True
# Your code here

Hint

Use df.rename(columns=rename_dict, inplace=True) to rename columns directly in the DataFrame.

4

Print the renamed DataFrame

Print the DataFrame df to see the new column names.

Pandas

import pandas as pd

df = pd.DataFrame({
    'prd': [101, 102, 103],
    'qty': [5, 10, 15],
    'amt': [100, 200, 300]
})

rename_dict = {'prd': 'ProductID', 'qty': 'Quantity', 'amt': 'Amount'}

df.rename(columns=rename_dict, inplace=True)
# Print the DataFrame df
# Your code here

Hint

Use print(df) to display the DataFrame with renamed columns.

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