1D and 2D broadcasting in NumPy - Mini Project: Build & Apply

1D and 2D broadcasting

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You want to analyze the sales data to understand how many breads were sold each day and how the prices affect total revenue.

🎯 Goal: Build a small program using numpy to apply 1D and 2D broadcasting. You will create arrays for daily sales and prices, then calculate total revenue using broadcasting.

📋 What You'll Learn

Create a 2D numpy array called sales representing the number of breads sold for 3 types over 4 days.

Create a 1D numpy array called prices representing the price of each bread type.

Use broadcasting to calculate the total revenue per day for each bread type.

Print the resulting revenue array.

💡 Why This Matters

🌍 Real World

Broadcasting helps quickly perform operations on sales and price data without writing loops, saving time and reducing errors.

💼 Career

Data analysts and scientists use broadcasting in numpy to efficiently handle and analyze multi-dimensional data like sales, prices, and revenues.

1

Create the sales data array

Create a 2D numpy array called sales with these exact values: [[10, 15, 7], [12, 18, 5], [9, 14, 8], [11, 16, 6]]. This array represents the number of breads sold for 3 types over 4 days.

NumPy

import numpy as np
# Create the 2D numpy array called sales with the given values
# Your code here

Need a hint?

Use np.array() to create the 2D array with the exact values.

2

Create the prices array

Create a 1D numpy array called prices with these exact values: [2.5, 3.0, 4.0]. This array represents the price of each bread type.

NumPy

import numpy as np
sales = np.array([[10, 15, 7], [12, 18, 5], [9, 14, 8], [11, 16, 6]])
# Create the 1D numpy array called prices with the given values
# Your code here

Need a hint?

Use np.array() to create the 1D array with the exact prices.

3

Calculate total revenue using broadcasting

Use broadcasting to multiply the sales array by the prices array and store the result in a new variable called revenue. This will calculate the total revenue per bread type for each day.

NumPy

import numpy as np
sales = np.array([[10, 15, 7], [12, 18, 5], [9, 14, 8], [11, 16, 6]])
prices = np.array([2.5, 3.0, 4.0])
# Multiply sales by prices using broadcasting and store in revenue
# Your code here

Need a hint?

Simply multiply the 2D sales array by the 1D prices array to use broadcasting.

4

Print the revenue array

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

NumPy

import numpy as np
sales = np.array([[10, 15, 7], [12, 18, 5], [9, 14, 8], [11, 16, 6]])
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.