transform() for group-level operations in Data Analysis Python - Mini Project: Build & Apply

Using transform() for Group-Level Operations in Data Science

📖 Scenario: Imagine you work for a retail company. You have sales data for different stores and want to analyze how each store's daily sales compare to the average sales of that store.

🎯 Goal: You will create a small sales dataset, calculate the average sales per store, and then use transform() to add a new column showing how each day's sales compare to the store's average.

📋 What You'll Learn

Create a pandas DataFrame with sales data for multiple stores

Create a variable to hold the store names to group by

Use transform() to calculate the average sales per store and add it as a new column

Print the final DataFrame showing daily sales and average sales per store

💡 Why This Matters

🌍 Real World

Retail companies often analyze sales data by store to understand performance and identify trends.

💼 Career

Data analysts and data scientists use group-level operations like transform() to prepare data for reports and decision-making.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Store': ['Store A', 'Store A', 'Store B', 'Store B', 'Store C', 'Store C']
'Day': ['Monday', 'Tuesday', 'Monday', 'Tuesday', 'Monday', 'Tuesday']
'Sales': [200, 220, 150, 180, 300, 310]

Data Analysis Python

# Create the sales_data DataFrame with Store, Day, and Sales columns
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the exact keys and lists of values.

2

Create the group variable

Create a variable called group_column and set it to the string 'Store' to specify the column to group by.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Store': ['Store A', 'Store A', 'Store B', 'Store B', 'Store C', 'Store C'],
    'Day': ['Monday', 'Tuesday', 'Monday', 'Tuesday', 'Monday', 'Tuesday'],
    'Sales': [200, 220, 150, 180, 300, 310]
})

# Create the group_column variable with value 'Store'
# Your code here

Hint

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

3

Calculate average sales per store using transform()

Use transform() on sales_data.groupby(group_column)['Sales'] to calculate the average sales per store. Add the result as a new column called 'Average_Sales' in sales_data.

Data Analysis Python

sales_data['Average_Sales'] = sales_data.groupby(group_column)['Sales'].transform('mean')

Hint

Use groupby() on group_column, select 'Sales', then apply transform('mean').

4

Print the final DataFrame

Write a print() statement to display the sales_data DataFrame with the new 'Average_Sales' column.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Store': ['Store A', 'Store A', 'Store B', 'Store B', 'Store C', 'Store C'],
    'Day': ['Monday', 'Tuesday', 'Monday', 'Tuesday', 'Monday', 'Tuesday'],
    'Sales': [200, 220, 150, 180, 300, 310]
})

group_column = 'Store'

sales_data['Average_Sales'] = sales_data.groupby(group_column)['Sales'].transform('mean')

# Print the sales_data DataFrame
# Your code here

Hint

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