Common broadcasting patterns in NumPy - Mini Project: Build & Apply

Common broadcasting patterns

📖 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 sales compare to a daily target.

🎯 Goal: Build a small program using numpy arrays to apply common broadcasting patterns. You will create sales data, set a daily sales target, calculate total sales per day, and compare sales to the target using broadcasting.

📋 What You'll Learn

Use numpy arrays to store sales data

Create a daily sales target as a single number

Use broadcasting to sum sales per day

Use broadcasting to compare daily sales to the target

Print the final comparison result

💡 Why This Matters

🌍 Real World

Broadcasting is a powerful tool in data science to efficiently compare and combine data without writing loops. It helps analyze sales, sensor data, or any tabular data quickly.

💼 Career

Understanding broadcasting is essential for data analysts and scientists to write clean, fast, and readable code when working with arrays and matrices.

1

Create the sales data array

Import numpy as np and create a 2D numpy array called sales with these exact values: [[10, 15, 20], [12, 18, 25], [14, 20, 30]]. Each row is a day, and each column is a bread type.

NumPy

# Import numpy and create the sales array
# Your code here

Need a hint?

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

2

Set the daily sales target

Create a variable called daily_target and set it to the integer 50. This is the sales goal for each day.

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 25], [14, 20, 30]])
# Create the daily_target variable
# Your code here

Need a hint?

Just assign the number 50 to the variable daily_target.

3

Calculate total sales per day using broadcasting

Create a variable called total_sales that sums the sales of all bread types for each day using np.sum on sales with axis=1.

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 25], [14, 20, 30]])
daily_target = 50
# Calculate total sales per day
# Your code here

Need a hint?

Use np.sum with axis=1 to sum rows (days).

4

Compare total sales to daily target and print result

Create a variable called target_met that compares total_sales to daily_target using the >= operator. Then print target_met.

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 25], [14, 20, 30]])
daily_target = 50
total_sales = np.sum(sales, axis=1)
# Compare total_sales to daily_target and print
# Your code here

Need a hint?

Use total_sales >= daily_target to get a boolean array, then print it.