Why broadcasting matters in NumPy - See It in Action

Why broadcasting matters

📖 Scenario: Imagine you run a small bakery. You have daily sales data for 5 days and want to calculate the total revenue each day. You know the price of each item sold. Broadcasting helps you multiply prices by quantities easily without writing loops.

🎯 Goal: Learn how to use NumPy broadcasting to multiply arrays of different shapes to calculate daily revenue.

📋 What You'll Learn

Create a NumPy array called quantities with daily sales for 3 items over 5 days

Create a NumPy array called prices with prices for the 3 items

Use broadcasting to multiply quantities by prices to get daily revenue per item

Sum the revenue per item to get total revenue per day

Print the total revenue per day

💡 Why This Matters

🌍 Real World

Businesses often have sales data and prices in separate lists. Broadcasting helps quickly calculate revenues or costs without writing loops.

💼 Career

Data analysts and scientists use broadcasting to efficiently process and analyze large datasets, making their code cleaner and faster.

1

Create daily sales data

Create a NumPy array called quantities with these exact values: [[2, 3, 1], [1, 0, 4], [3, 2, 2], [0, 1, 5], [4, 3, 0]]. This represents sales of 3 items over 5 days.

NumPy

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

Need a hint?

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

2

Create prices array

Create a NumPy array called prices with these exact values: [5, 3, 2]. These are the prices for the 3 items.

NumPy

import numpy as np
quantities = np.array([[2, 3, 1], [1, 0, 4], [3, 2, 2], [0, 1, 5], [4, 3, 0]])
# Create the prices array with the exact values
# Your code here

Need a hint?

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

3

Calculate daily revenue using broadcasting

Use broadcasting to multiply quantities by prices and store the result in daily_revenue_per_item. Then sum daily_revenue_per_item across items (axis 1) and store in total_daily_revenue.

NumPy

import numpy as np
quantities = np.array([[2, 3, 1], [1, 0, 4], [3, 2, 2], [0, 1, 5], [4, 3, 0]])
prices = np.array([5, 3, 2])
# Multiply quantities by prices using broadcasting
# Sum the revenue per item to get total daily revenue
# Your code here

Need a hint?

Use * to multiply arrays and .sum(axis=1) to sum rows.

4

Print total daily revenue

Print the total_daily_revenue array to show total revenue for each day.

NumPy

import numpy as np
quantities = np.array([[2, 3, 1], [1, 0, 4], [3, 2, 2], [0, 1, 5], [4, 3, 0]])
prices = np.array([5, 3, 2])
daily_revenue_per_item = quantities * prices
total_daily_revenue = daily_revenue_per_item.sum(axis=1)
# Print the total daily revenue
# Your code here

Need a hint?

Use print(total_daily_revenue) to display the result.