Vectorization over loops in NumPy - Mini Project: Build & Apply

Vectorization over loops

📖 Scenario: You work in a small bakery that tracks daily sales of three types of bread. You have the sales data for a week and want to calculate the total sales for each bread type.

🎯 Goal: Build a program that calculates total sales for each bread type using vectorized operations with numpy instead of loops.

📋 What You'll Learn

Create a numpy array with daily sales data for three bread types over 7 days.

Create a variable to hold the number of days.

Use vectorized numpy operations to calculate total sales for each bread type.

Print the total sales array.

💡 Why This Matters

🌍 Real World

Vectorized operations are used in data science to quickly process large datasets like sales, sensor data, or images without slow loops.

💼 Career

Data scientists and analysts use vectorization to write faster and cleaner code for data aggregation and analysis.

1

Create the sales data array

Create a numpy array called sales with the exact values: [[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]].

NumPy

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

Need a hint?

Use np.array([...]) with the exact nested list of sales values.

2

Create the days variable

Create a variable called days and set it to the number of rows in the sales array using sales.shape[0].

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]])
# Create the variable days with the number of rows in sales
# Your code here

Need a hint?

Use sales.shape[0] to get the number of rows (days).

3

Calculate total sales using vectorization

Create a variable called total_sales and set it to the sum of sales over the rows (axis 0) using np.sum(sales, axis=0).

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]])
days = sales.shape[0]
# Calculate total_sales by summing sales over axis 0
# Your code here

Need a hint?

Use np.sum with axis=0 to sum each bread type's sales over all days.

4

Print the total sales

Write a print statement to display the total_sales array.

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]])
days = sales.shape[0]
total_sales = np.sum(sales, axis=0)
# Print the total_sales array
# Your code here

Need a hint?

Use print(total_sales) to display the result.