apply() on rows (axis=1) in Pandas - Mini Project: Build & Apply

Using apply() on rows with axis=1 in pandas

📖 Scenario: You work in a small bookstore. You have a table of books with their prices and quantities. You want to find the total value of each book's stock.

🎯 Goal: Create a pandas DataFrame with book data, then use apply() on rows (axis=1) to calculate the total value 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 function called calculate_value that takes a row and returns the product of Price and Quantity.

Use apply() on books with axis=1 and the calculate_value function to create a new column TotalValue.

Print the books DataFrame to show the new column.

💡 Why This Matters

🌍 Real World

Calculating total stock value helps bookstores and shops understand their inventory worth quickly.

💼 Career

Data analysts and data scientists often use apply() on rows to create new features or calculate values based on multiple columns.

1

Create the books DataFrame

Create a pandas DataFrame called books with these exact columns and data: Title with values 'Book A', 'Book B', 'Book C'; Price with values 10, 15, 7; and Quantity with values 5, 3, 10.

Pandas

import pandas as pd
# Create the books DataFrame with Title, Price, and Quantity columns
# 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 the calculate_value function

Create a function called calculate_value that takes one argument row and returns the product of row['Price'] and row['Quantity'].

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10, 15, 7],
    'Quantity': [5, 3, 10]
})

# Define the calculate_value function
# Your code here

Need a hint?

Define a function with def that multiplies row['Price'] and row['Quantity'].

3

Apply the function to each row

Use apply() on the books DataFrame with axis=1 and the calculate_value function to create a new column called TotalValue.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10, 15, 7],
    'Quantity': [5, 3, 10]
})

def calculate_value(row):
    return row['Price'] * row['Quantity']

# Use apply() with axis=1 to create TotalValue column
# Your code here

Need a hint?

Use books['TotalValue'] = books.apply(calculate_value, axis=1) to add the new column.

4

Print the books DataFrame

Print the books DataFrame to show the new TotalValue column.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10, 15, 7],
    'Quantity': [5, 3, 10]
})

def calculate_value(row):
    return row['Price'] * row['Quantity']

books['TotalValue'] = books.apply(calculate_value, axis=1)

# Print the books DataFrame
# Your code here

Need a hint?

Use print(books) to display the DataFrame with the new column.