map() for element-wise mapping in Data Analysis Python - Mini Project: Build & Apply

Using map() for Element-wise Mapping in Data Science

📖 Scenario: You work in a small grocery store. You have a list of prices for some fruits. You want to apply a discount to each price to prepare for a sale.

🎯 Goal: Learn how to use the map() function to apply a discount to each price in a list.

📋 What You'll Learn

Create a list of fruit prices

Create a discount rate variable

Use map() with a function to apply the discount to each price

Print the discounted prices as a list

💡 Why This Matters

🌍 Real World

Applying discounts or taxes to prices is common in retail and e-commerce. Using <code>map()</code> helps automate this process efficiently.

💼 Career

Data analysts and scientists often need to transform data element-wise. Knowing how to use <code>map()</code> is a basic but important skill.

1

Create a list of fruit prices

Create a list called prices with these exact values: 2.5, 3.0, 4.5, 1.2, 5.0.

Data Analysis Python

# Create the list of prices
# Your code here

Hint

Use square brackets [] to create a list and separate values with commas.

2

Create a discount rate variable

Create a variable called discount_rate and set it to 0.1 to represent a 10% discount.

Data Analysis Python

prices = [2.5, 3.0, 4.5, 1.2, 5.0]
# Create the discount_rate variable
# Your code here

Hint

Assign the value 0.1 to the variable discount_rate.

3

Use map() to apply the discount to each price

Use map() with a lambda function to create a new list called discounted_prices. The lambda function should subtract the discount from each price using price * discount_rate.

Data Analysis Python

prices = [2.5, 3.0, 4.5, 1.2, 5.0]
discount_rate = 0.1
# Use map() with lambda to apply discount
# Your code here

Hint

Use map() with a lambda that takes price and returns price - price * discount_rate. Wrap the result with list().

4

Print the discounted prices

Print the list discounted_prices to see the prices after the discount.

Data Analysis Python

prices = [2.5, 3.0, 4.5, 1.2, 5.0]
discount_rate = 0.1
discounted_prices = list(map(lambda price: price - price * discount_rate, prices))
# Print discounted_prices
# Your code here

Hint

Use print(discounted_prices) to display the final list.