Boolean indexing in Data Analysis Python - Mini Project: Build & Apply

Boolean Indexing

📖 Scenario: You work in a small store and have a list of products with their prices. You want to find which products cost more than $20 to decide what to promote.

🎯 Goal: Use boolean indexing on a pandas DataFrame to select products with prices greater than $20.

📋 What You'll Learn

Create a pandas DataFrame with product names and prices

Create a boolean condition to select products priced above $20

Use boolean indexing to filter the DataFrame

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Stores often need to filter products by price to create promotions or discounts.

💼 Career

Data analysts use boolean indexing to quickly select data subsets for reports and decision-making.

1

Create the products DataFrame

Import pandas as pd and create a DataFrame called products with these exact entries: 'Product' column with values 'Pen', 'Notebook', 'Backpack', 'Calculator', and 'Water Bottle', and 'Price' column with values 5, 15, 45, 30, and 10 respectively.

Data Analysis Python

# Import pandas and create the products DataFrame
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing two lists for columns.

2

Create a boolean condition for prices above 20

Create a variable called expensive that stores a boolean Series by checking which products['Price'] values are greater than 20.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Backpack', 'Calculator', 'Water Bottle'],
    'Price': [5, 15, 45, 30, 10]
})
# Create the boolean condition for prices > 20
# Your code here

Need a hint?

Use the comparison operator > on the Price column.

3

Filter products using boolean indexing

Create a new DataFrame called expensive_products by selecting rows from products where expensive is True using boolean indexing.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Backpack', 'Calculator', 'Water Bottle'],
    'Price': [5, 15, 45, 30, 10]
})
expensive = products['Price'] > 20
# Use boolean indexing to create expensive_products
# Your code here

Need a hint?

Use products[expensive] to filter rows.

4

Print the filtered DataFrame

Print the expensive_products DataFrame to see the products with prices greater than $20.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Backpack', 'Calculator', 'Water Bottle'],
    'Price': [5, 15, 45, 30, 10]
})
expensive = products['Price'] > 20
expensive_products = products[expensive]
# Print the expensive_products DataFrame
# Your code here

Need a hint?

Use print(expensive_products) to display the filtered DataFrame.