transform() for group-level operations in Pandas - Mini Project: Build & Apply

Using transform() for Group-Level Operations in pandas

📖 Scenario: You work in a retail company analyzing sales data. You want to understand how each product's sales compare to the average sales of its category.

🎯 Goal: Build a pandas DataFrame with sales data, then use transform() to calculate the average sales per category and add it as a new column.

📋 What You'll Learn

Create a pandas DataFrame named sales_data with columns 'Category' and 'Sales' using the exact data provided.

Create a variable named group_column and set it to the string 'Category'.

Use groupby() on sales_data with group_column and apply transform() with 'mean' to calculate average sales per category, storing the result in a new column 'Average_Sales'.

Print the sales_data DataFrame to display the final result.

💡 Why This Matters

🌍 Real World

Retail analysts often compare individual product sales to category averages to identify strong or weak performers.

💼 Career

Data scientists and analysts use group-level operations like transform() to create features and insights for reports and machine learning.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with two columns: 'Category' and 'Sales'. Use these exact entries: 'Category': ['Electronics', 'Electronics', 'Clothing', 'Clothing', 'Clothing', 'Furniture'], 'Sales': [200, 150, 100, 120, 90, 300].

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing the two columns and their exact lists.

2

Set the group column variable

Create a variable called group_column and set it to the string 'Category'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Category': ['Electronics', 'Electronics', 'Clothing', 'Clothing', 'Clothing', 'Furniture'],
    'Sales': [200, 150, 100, 120, 90, 300]
})
# Create the group_column variable with value 'Category'
# Your code here

Need a hint?

Just assign the string 'Category' to the variable group_column.

3

Calculate average sales per category using transform()

Use groupby() on sales_data with group_column and apply transform('mean') on the 'Sales' column. Assign the result to a new column in sales_data called 'Average_Sales'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Category': ['Electronics', 'Electronics', 'Clothing', 'Clothing', 'Clothing', 'Furniture'],
    'Sales': [200, 150, 100, 120, 90, 300]
})
group_column = 'Category'
# Use groupby and transform to create 'Average_Sales' column
# Your code here

Need a hint?

Use sales_data.groupby(group_column)['Sales'].transform('mean') to get average sales per category and assign it to sales_data['Average_Sales'].

4

Print the final DataFrame

Print the sales_data DataFrame to display the original sales and the new 'Average_Sales' column.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Category': ['Electronics', 'Electronics', 'Clothing', 'Clothing', 'Clothing', 'Furniture'],
    'Sales': [200, 150, 100, 120, 90, 300]
})
group_column = 'Category'
sales_data['Average_Sales'] = sales_data.groupby(group_column)['Sales'].transform('mean')
# Print the sales_data DataFrame
# Your code here

Need a hint?

Use print(sales_data) to show the DataFrame with the new column.