Vectorized operations vs loops in Pandas - Hands-On Comparison

Vectorized operations vs loops

📖 Scenario: You work in a small store that tracks daily sales. You have a list of sales amounts for each day. You want to calculate the total sales and also add tax to each sale. You can do this by using loops or by using faster vectorized operations with pandas.

🎯 Goal: Learn how to use vectorized operations in pandas to perform calculations on data quickly, instead of using slower loops.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data

Create a tax rate variable

Calculate total sales using a loop

Calculate sales with tax using vectorized operations

Print the results

💡 Why This Matters

🌍 Real World

Stores and businesses often need to calculate totals and apply taxes or discounts to many sales quickly.

💼 Career

Data scientists and analysts use vectorized operations in pandas to handle large datasets efficiently without slow loops.

1

Create the sales data

Create a pandas DataFrame called sales_data with one column named 'sales' and these exact values: 100, 150, 200, 130, 170.

Pandas

import pandas as pd
# Create the sales_data DataFrame with the sales column and values
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where the key is 'sales' and the value is the list of numbers.

2

Set the tax rate

Create a variable called tax_rate and set it to 0.1 (which means 10%).

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [100, 150, 200, 130, 170]})
# Create the tax_rate variable and set it to 0.1
# Your code here

Need a hint?

Just assign 0.1 to the variable tax_rate.

3

Calculate total sales using a loop

Create a variable called total_sales and set it to 0. Then use a for loop with the variable sale to go through sales_data['sales'] and add each sale to total_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [100, 150, 200, 130, 170]})
tax_rate = 0.1
# Create total_sales = 0
# Use for sale in sales_data['sales'] to add each sale to total_sales
# Your code here

Need a hint?

Start with total_sales = 0. Then add each sale inside the loop using total_sales += sale.

4

Calculate sales with tax using vectorized operations and print results

Create a new column in sales_data called 'sales_with_tax' by multiplying sales_data['sales'] by (1 + tax_rate). Then print total_sales and print the sales_data DataFrame.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [100, 150, 200, 130, 170]})
tax_rate = 0.1
total_sales = 0
for sale in sales_data['sales']:
    total_sales += sale
# Create sales_with_tax column using vectorized operation
# Print total_sales
# Print sales_data
# Your code here

Need a hint?

Multiply the whole sales column by 1 + tax_rate to get the new column. Use print() to show results.