Vectorized operations vs loops in Data Analysis Python - Hands-On Comparison

Vectorized operations vs loops

📖 Scenario: Imagine you have a list of daily sales numbers for a small store. You want to calculate the total sales and also increase each day's sales by 10% to see the effect of a price increase.

🎯 Goal: You will create a list of sales numbers, set a percentage increase, use a loop and then vectorized operations with NumPy to increase sales, and finally print the results to compare.

📋 What You'll Learn

Create a list of daily sales numbers called sales with these exact values: 100, 150, 200, 130, 170

Create a variable called increase_percent and set it to 10

Use a for loop with variable i to increase each sale in sales by increase_percent percent and store results in a new list called increased_sales_loop

Use NumPy vectorized operations to increase sales by increase_percent percent and store results in a NumPy array called increased_sales_vectorized

Print both increased_sales_loop and increased_sales_vectorized to compare the results

💡 Why This Matters

🌍 Real World

Retail stores and businesses often need to quickly update prices or sales data and analyze the effects. Vectorized operations help do this faster and with simpler code.

💼 Career

Data analysts and scientists use vectorized operations in Python with libraries like NumPy to handle large datasets efficiently, making their work faster and more reliable.

1

Create the sales data list

Create a list called sales with these exact values: 100, 150, 200, 130, 170

Data Analysis Python

# Create the sales list with exact values
# Your code here

Hint

Use square brackets [] to create a list and separate numbers with commas.

2

Set the increase percentage

Create a variable called increase_percent and set it to 10

Data Analysis Python

sales = [100, 150, 200, 130, 170]
# Create increase_percent variable and set to 10
# Your code here

Hint

Just assign the number 10 to the variable increase_percent.

3

Increase sales using a for loop

Use a for loop with variable i to increase each sale in sales by increase_percent percent. Store the results in a new list called increased_sales_loop

Data Analysis Python

sales = [100, 150, 200, 130, 170]
increase_percent = 10
# Use a for loop with i to create increased_sales_loop
# Your code here

Hint

Use range(len(sales)) to loop over indexes, calculate increased value, and add to the new list.

4

Increase sales using NumPy vectorized operations and print results

Import NumPy as np. Use NumPy vectorized operations to increase sales by increase_percent percent and store the result in a NumPy array called increased_sales_vectorized. Then print both increased_sales_loop and increased_sales_vectorized

Data Analysis Python

sales = [100, 150, 200, 130, 170]
increase_percent = 10
increased_sales_loop = []
for i in range(len(sales)):
    increased_value = sales[i] * (1 + increase_percent / 100)
    increased_sales_loop.append(increased_value)

# Import numpy as np
# Use vectorized operation to create increased_sales_vectorized
# Print increased_sales_loop and increased_sales_vectorized
# Your code here

Hint

Use np.array(sales) to convert list to array, then multiply by 1 + increase_percent / 100. Use print() to show both results.