Avoiding broadcasting mistakes in NumPy - Mini Project: Build & Apply

Avoiding broadcasting mistakes

📖 Scenario: Imagine you are analyzing sales data for a small store. You have daily sales numbers for 5 products over a week. You want to apply a discount to each product's sales but must be careful to avoid mistakes caused by incorrect array shapes in calculations.

🎯 Goal: You will create a NumPy array for sales data, define a discount array, apply the discount correctly using broadcasting, and print the discounted sales.

📋 What You'll Learn

Create a NumPy array called sales with shape (7, 5) representing 7 days and 5 products.

Create a NumPy array called discount with shape (5,) representing discount factors for each product.

Apply the discount to the sales data correctly using broadcasting.

Print the resulting discounted sales array.

💡 Why This Matters

🌍 Real World

Retail stores often apply discounts to products and need to calculate updated sales or revenue correctly using arrays of data.

💼 Career

Data analysts and scientists must understand broadcasting to manipulate multi-dimensional data without errors in Python using NumPy.

1

Create the sales data array

Import NumPy as np and create a NumPy array called sales with these exact values and shape (7, 5):
[[100, 200, 300, 400, 500],
[110, 210, 310, 410, 510],
[120, 220, 320, 420, 520],
[130, 230, 330, 430, 530],
[140, 240, 340, 440, 540],
[150, 250, 350, 450, 550],
[160, 260, 360, 460, 560]]

NumPy

import numpy as np
# Create the sales array with shape (7, 5) and the exact values
# Your code here

Need a hint?

Use np.array([...]) with the exact nested list of values.

2

Create the discount array

Create a NumPy array called discount with these exact values:
[0.9, 0.8, 0.85, 0.95, 0.9] representing discount factors for each product.

NumPy

import numpy as np
sales = np.array([[100, 200, 300, 400, 500],
                  [110, 210, 310, 410, 510],
                  [120, 220, 320, 420, 520],
                  [130, 230, 330, 430, 530],
                  [140, 240, 340, 440, 540],
                  [150, 250, 350, 450, 550],
                  [160, 260, 360, 460, 560]])
# Create the discount array with the exact values
# Your code here

Need a hint?

Use np.array([...]) with the exact discount values.

3

Apply the discount using broadcasting

Create a new NumPy array called discounted_sales by multiplying sales with discount using broadcasting. Use the * operator directly.

NumPy

import numpy as np
sales = np.array([[100, 200, 300, 400, 500],
                  [110, 210, 310, 410, 510],
                  [120, 220, 320, 420, 520],
                  [130, 230, 330, 430, 530],
                  [140, 240, 340, 440, 540],
                  [150, 250, 350, 450, 550],
                  [160, 260, 360, 460, 560]])
discount = np.array([0.9, 0.8, 0.85, 0.95, 0.9])
# Multiply sales by discount using broadcasting and assign to discounted_sales
# Your code here

Need a hint?

Use the * operator to multiply sales and discount arrays directly.

4

Print the discounted sales

Print the discounted_sales array to see the final discounted sales values.

NumPy

import numpy as np
sales = np.array([[100, 200, 300, 400, 500],
                  [110, 210, 310, 410, 510],
                  [120, 220, 320, 420, 520],
                  [130, 230, 330, 430, 530],
                  [140, 240, 340, 440, 540],
                  [150, 250, 350, 450, 550],
                  [160, 260, 360, 460, 560]])
discount = np.array([0.9, 0.8, 0.85, 0.95, 0.9])
discounted_sales = sales * discount
# Print the discounted_sales array
# Your code here

Need a hint?

Use print(discounted_sales) to display the array.