Broadcasting rules in NumPy - Mini Project: Build & Apply

Understanding Broadcasting Rules with NumPy

📖 Scenario: Imagine you are working with sales data for a small store. You have daily sales numbers for different products, and you want to apply a discount rate to all products easily using NumPy arrays.

🎯 Goal: You will learn how to use NumPy broadcasting rules to apply a discount rate to a 2D array of sales data without writing loops.

📋 What You'll Learn

Create a NumPy 2D array with exact sales data

Create a NumPy 1D array with exact discount rates

Use broadcasting to apply discounts to sales data

Print the discounted sales array

💡 Why This Matters

🌍 Real World

Applying discounts or adjustments to sales data is common in retail and business analytics. Broadcasting helps apply these changes efficiently without writing loops.

💼 Career

Data scientists and analysts often use broadcasting in NumPy to perform fast and readable calculations on large datasets.

1

Create the sales data array

Import NumPy as np and create a 2D NumPy array called sales with these exact values: [[100, 200, 300], [400, 500, 600]].

NumPy

# Import numpy and create the sales array
# Your code here

Need a hint?

Use np.array() to create the array with the exact nested list.

2

Create the discount rates array

Create a 1D NumPy array called discounts with these exact values: [0.9, 0.8, 0.7] representing discount rates for each product.

NumPy

import numpy as np
sales = np.array([[100, 200, 300], [400, 500, 600]])
# Create the discounts array
# Your code here

Need a hint?

Create a 1D array with the discount rates matching the number of columns in sales.

3

Apply broadcasting to calculate discounted sales

Create a new NumPy array called discounted_sales by multiplying sales with discounts using broadcasting.

NumPy

import numpy as np
sales = np.array([[100, 200, 300], [400, 500, 600]])
discounts = np.array([0.9, 0.8, 0.7])
# Multiply sales by discounts using broadcasting
# Your code here

Need a hint?

Simply multiply the 2D sales array by the 1D discounts array to apply broadcasting.

4

Print the discounted sales array

Print the discounted_sales array to see the result of applying the discounts.

NumPy

import numpy as np
sales = np.array([[100, 200, 300], [400, 500, 600]])
discounts = np.array([0.9, 0.8, 0.7])
discounted_sales = sales * discounts
# Print the discounted_sales array
# Your code here

Need a hint?

Use print(discounted_sales) to display the array.