Why advanced broadcasting matters in NumPy - See It in Action

Why advanced broadcasting matters

📖 Scenario: Imagine you work in a small bakery. You have daily sales data for three types of bread over a week. You want to quickly calculate the total revenue each day by multiplying the sales by the price per bread type. Using advanced broadcasting in NumPy helps you do this easily without writing complex loops.

🎯 Goal: Learn how to use NumPy's advanced broadcasting to multiply a 2D array of daily sales by a 1D array of prices, producing total revenue per day for each bread type.

📋 What You'll Learn

Create a NumPy array called sales with exact daily sales data for 7 days and 3 bread types

Create a NumPy array called prices with exact prices for the 3 bread types

Use advanced broadcasting to multiply sales by prices to get revenue

Print the revenue array to see total revenue per day

💡 Why This Matters

🌍 Real World

In business, you often have sales data and prices separately. Broadcasting helps quickly calculate revenues or costs without manual loops.

💼 Career

Data scientists and analysts use broadcasting to write efficient code for financial calculations, sales analysis, and many other tasks involving arrays.

1

Create the sales data array

Create a NumPy array called sales with these exact values representing daily sales for 7 days and 3 bread types: [[10, 5, 2], [12, 7, 3], [9, 6, 4], [11, 8, 5], [13, 7, 6], [14, 9, 7], [15, 10, 8]]

NumPy

import numpy as np
# Create the sales array with the exact values
# Your code here

Need a hint?

Use np.array() and type the exact nested list of sales numbers.

2

Create the prices array

Create a NumPy array called prices with these exact prices for the 3 bread types: [2.5, 3.0, 4.0]

NumPy

import numpy as np
sales = np.array([[10, 5, 2], [12, 7, 3], [9, 6, 4], [11, 8, 5], [13, 7, 6], [14, 9, 7], [15, 10, 8]])
# Create the prices array with the exact values
# Your code here

Need a hint?

Use np.array() with the exact list of prices.

3

Calculate revenue using advanced broadcasting

Use advanced broadcasting to multiply the sales array by the prices array and save the result in a new variable called revenue

NumPy

import numpy as np
sales = np.array([[10, 5, 2], [12, 7, 3], [9, 6, 4], [11, 8, 5], [13, 7, 6], [14, 9, 7], [15, 10, 8]])
prices = np.array([2.5, 3.0, 4.0])
# Multiply sales by prices using broadcasting and save to revenue
# Your code here

Need a hint?

Just multiply sales by prices directly. NumPy will broadcast automatically.

4

Print the revenue array

Print the revenue array to display total revenue per day for each bread type

NumPy

import numpy as np
sales = np.array([[10, 5, 2], [12, 7, 3], [9, 6, 4], [11, 8, 5], [13, 7, 6], [14, 9, 7], [15, 10, 8]])
prices = np.array([2.5, 3.0, 4.0])
revenue = sales * prices
# Print the revenue array
# Your code here

Need a hint?

Use print(revenue) to show the final result.