Broadcasting errors and debugging in NumPy - Mini Project: Build & Apply

Broadcasting errors and debugging

📖 Scenario: Imagine you are working with sales data for a small store. You have daily sales numbers and want to apply a discount factor to each day's sales to see the adjusted sales. However, sometimes the shapes of your data arrays don't match, causing errors.

🎯 Goal: You will create two numpy arrays representing sales and discount factors. Then you will identify and fix a broadcasting error by adjusting the shape of the discount array. Finally, you will print the corrected adjusted sales.

📋 What You'll Learn

Create a numpy array called sales with values [100, 200, 300]

Create a numpy array called discounts with values [0.9, 0.8]

Create a new array adjusted_sales by multiplying sales and discounts with correct shapes to avoid broadcasting errors

Print the adjusted_sales array

💡 Why This Matters

🌍 Real World

In real data science, you often combine data arrays of different shapes. Understanding broadcasting helps you avoid errors and write efficient code.

💼 Career

Data scientists and analysts frequently manipulate arrays and matrices. Debugging broadcasting errors is a key skill to ensure correct calculations.

1

Create the sales and discounts arrays

Import numpy as np. Create a numpy array called sales with values [100, 200, 300]. Create another numpy array called discounts with values [0.9, 0.8].

NumPy

# Import numpy and create sales and discounts arrays
# Your code here

Need a hint?

Use np.array() to create arrays. Remember to import numpy first.

2

Add a shape adjustment for discounts

Create a new variable called discounts_reshaped by reshaping discounts to have shape (2, 1) using reshape.

NumPy

import numpy as np
sales = np.array([100, 200, 300])
discounts = np.array([0.9, 0.8])
# Reshape discounts to (2, 1)
# Your code here

Need a hint?

Use discounts.reshape((2, 1)) to change the shape.

3

Multiply sales and reshaped discounts to get adjusted sales

Create a new variable called adjusted_sales by multiplying discounts_reshaped and sales. This will use broadcasting correctly.

NumPy

import numpy as np
sales = np.array([100, 200, 300])
discounts = np.array([0.9, 0.8])
discounts_reshaped = discounts.reshape((2, 1))
# Multiply discounts_reshaped and sales to get adjusted_sales
# Your code here

Need a hint?

Use the * operator to multiply arrays. Broadcasting works when shapes align properly.

4

Print the adjusted sales array

Print the adjusted_sales array to see the result.

NumPy

import numpy as np
sales = np.array([100, 200, 300])
discounts = np.array([0.9, 0.8])
discounts_reshaped = discounts.reshape((2, 1))
adjusted_sales = discounts_reshaped * sales
# Print adjusted_sales
# Your code here

Need a hint?

Use print(adjusted_sales) to display the array.