apply() with lambda functions in Pandas - Mini Project: Build & Apply

Using apply() with lambda functions in pandas

📖 Scenario: You work in a small bookstore. You have a list of books with their prices and quantities sold. You want to calculate the total sales for each book.

🎯 Goal: Build a pandas DataFrame with book data, then use apply() with a lambda function to calculate total sales for each book.

📋 What You'll Learn

Create a pandas DataFrame called books with columns 'Title', 'Price', and 'Quantity' using the exact data provided.

Create a variable called calculate_sales that holds a lambda function to multiply Price and Quantity.

Use apply() with the calculate_sales lambda function on the DataFrame rows to create a new column 'TotalSales'.

Print the books DataFrame to show the new 'TotalSales' column.

💡 Why This Matters

🌍 Real World

Calculating total sales for products is a common task in retail and business analytics to understand revenue.

💼 Career

Data analysts and data scientists often use pandas apply() with lambda functions to quickly transform and analyze tabular data.

1

Create the books DataFrame

Import pandas as pd and create a DataFrame called books with these exact columns and data: 'Title' with values 'Book A', 'Book B', 'Book C'; 'Price' with values 10.0, 15.5, 8.75; and 'Quantity' with values 5, 3, 10.

Pandas

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

Need a hint?

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

2

Create a lambda function to calculate sales

Create a variable called calculate_sales and assign it a lambda function that takes a row and returns the product of the 'Price' and 'Quantity' columns from that row.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10.0, 15.5, 8.75],
    'Quantity': [5, 3, 10]
})
# Create the lambda function calculate_sales
# Your code here

Need a hint?

The lambda function should take one argument (a row) and return row['Price'] * row['Quantity'].

3

Use apply() with the lambda function

Use the apply() method on the books DataFrame with the calculate_sales lambda function and axis=1 to apply it row-wise. Assign the result to a new column called 'TotalSales' in the books DataFrame.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10.0, 15.5, 8.75],
    'Quantity': [5, 3, 10]
})

calculate_sales = lambda row: row['Price'] * row['Quantity']
# Use apply() with calculate_sales to create 'TotalSales' column
# Your code here

Need a hint?

Remember to use axis=1 to apply the function to each row.

4

Print the DataFrame with total sales

Write a print() statement to display the books DataFrame with the new 'TotalSales' column.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10.0, 15.5, 8.75],
    'Quantity': [5, 3, 10]
})

calculate_sales = lambda row: row['Price'] * row['Quantity']

books['TotalSales'] = books.apply(calculate_sales, axis=1)
# Print the books DataFrame
# Your code here

Need a hint?

Use print(books) to show the DataFrame.