Feature engineering basics in Pandas - Mini Project: Build & Apply

Feature engineering basics

📖 Scenario: You work as a data analyst for a retail company. You have sales data with product prices and quantities sold. You want to create new features to better understand the total sales value for each product.

🎯 Goal: Create a new feature called total_sales by multiplying price and quantity for each product in the sales data.

📋 What You'll Learn

Use pandas to create and manipulate data.

Create a DataFrame with product sales data.

Add a new column total_sales by multiplying price and quantity.

Print the updated DataFrame to see the new feature.

💡 Why This Matters

🌍 Real World

Feature engineering helps improve data quality and model performance by creating meaningful new columns from existing data.

💼 Career

Data scientists and analysts often create new features to help machine learning models understand data better and make better predictions.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values: product with ['A', 'B', 'C'], price with [10, 20, 15], and quantity with [5, 3, 8].

Pandas

import pandas as pd
# Create the sales_data DataFrame with product, price, and quantity columns
# Your code here

Need a hint?

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

2

Set up a variable for the new feature name

Create a variable called new_feature and set it to the string 'total_sales'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'product': ['A', 'B', 'C'],
    'price': [10, 20, 15],
    'quantity': [5, 3, 8]
})
# Create a variable new_feature and set it to 'total_sales'
# Your code here

Need a hint?

Just assign the string 'total_sales' to the variable new_feature.

3

Create the new feature by multiplying price and quantity

Add a new column to sales_data with the name stored in new_feature. Set this column to the product of the price and quantity columns.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'product': ['A', 'B', 'C'],
    'price': [10, 20, 15],
    'quantity': [5, 3, 8]
})
new_feature = 'total_sales'
# Create the new_feature column by multiplying price and quantity
# Your code here

Need a hint?

Use sales_data[new_feature] to create the new column and multiply the two existing columns.

4

Print the updated DataFrame

Print the sales_data DataFrame to display the new total_sales feature.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'product': ['A', 'B', 'C'],
    'price': [10, 20, 15],
    'quantity': [5, 3, 8]
})
new_feature = 'total_sales'
sales_data[new_feature] = sales_data['price'] * sales_data['quantity']
# Print the sales_data DataFrame
# Your code here

Need a hint?

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