GroupBy with transform for normalization in Pandas - Mini Project: Build & Apply

GroupBy with transform for normalization

📖 Scenario: You work in a company that collects sales data from different stores. Each store has sales numbers for several products. You want to compare sales within each store by normalizing the sales numbers.

🎯 Goal: You will create a pandas DataFrame with sales data, then use groupby and transform to normalize sales within each store. Finally, you will print the normalized sales.

📋 What You'll Learn

Create a pandas DataFrame named sales_data with columns 'Store' and 'Sales' using exact values

Create a variable mean_sales that stores the mean sales per store using groupby and transform

Create a new column 'Normalized_Sales' in sales_data by subtracting mean_sales from 'Sales'

Print the sales_data DataFrame to show the normalized sales

💡 Why This Matters

🌍 Real World

Normalizing sales data within groups helps compare performance fairly across stores by removing store-level differences.

💼 Career

Data analysts and data scientists often use groupby and transform in pandas to prepare and normalize data for analysis and reporting.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with two columns: 'Store' and 'Sales'. Use these exact values: 'Store': ['A', 'A', 'B', 'B', 'B', 'C'], 'Sales': [100, 150, 200, 210, 190, 300].

Pandas

import pandas as pd
# Create the sales_data DataFrame with Store and Sales columns
# Your code here

Need a hint?

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

2

Calculate mean sales per store using groupby and transform

Create a variable called mean_sales that stores the mean sales for each store. Use sales_data.groupby('Store')['Sales'].transform('mean') to calculate it.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Store': ['A', 'A', 'B', 'B', 'B', 'C'],
    'Sales': [100, 150, 200, 210, 190, 300]
})

# Calculate mean sales per store using groupby and transform
# Your code here

Need a hint?

Use groupby('Store')['Sales'].transform('mean') to get the mean sales for each row's store.

3

Create normalized sales column

Create a new column in sales_data called 'Normalized_Sales' by subtracting mean_sales from the 'Sales' column.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Store': ['A', 'A', 'B', 'B', 'B', 'C'],
    'Sales': [100, 150, 200, 210, 190, 300]
})
mean_sales = sales_data.groupby('Store')['Sales'].transform('mean')

# Create 'Normalized_Sales' column by subtracting mean_sales from Sales
# Your code here

Need a hint?

Subtract the mean_sales Series from the 'Sales' column and assign it to a new column.

4

Print the normalized sales DataFrame

Print the sales_data DataFrame to display the original sales and the normalized sales values.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Store': ['A', 'A', 'B', 'B', 'B', 'C'],
    'Sales': [100, 150, 200, 210, 190, 300]
})
mean_sales = sales_data.groupby('Store')['Sales'].transform('mean')
sales_data['Normalized_Sales'] = sales_data['Sales'] - mean_sales

# Print the sales_data DataFrame
# Your code here

Need a hint?

Use print(sales_data) to show the DataFrame with normalized sales.