Series arithmetic and alignment in Data Analysis Python - Mini Project: Build & Apply

Series arithmetic and alignment

📖 Scenario: You work in a small store that tracks daily sales of two products. Sometimes, sales data for a product is missing on some days. You want to add the sales of both products day by day, but only where data for both products exists.

🎯 Goal: Build a Python program using pandas Series to add sales data of two products with automatic alignment by date. You will create the sales data, set a threshold for minimum sales, add the sales with alignment, and print the result.

📋 What You'll Learn

Create two pandas Series named sales_product_a and sales_product_b with sales data indexed by dates.

Create a variable min_sales to hold the minimum sales threshold.

Add the two Series using the add() method with fill_value=0 to handle missing dates.

Print the resulting Series showing total sales per date.

💡 Why This Matters

🌍 Real World

Stores and businesses often have sales data for different products recorded on different days. Adding sales data with alignment helps combine information correctly even if some days are missing for some products.

💼 Career

Data analysts and scientists use pandas Series arithmetic and alignment to clean, combine, and analyze time series data in retail, finance, and many other fields.

1

Create sales data for two products

Import pandas as pd. Create a pandas Series called sales_product_a with these values: 10 on '2024-06-01', 15 on '2024-06-02', and 7 on '2024-06-04'. Create another Series called sales_product_b with these values: 5 on '2024-06-01', 8 on '2024-06-03', and 12 on '2024-06-04'. Use the dates as the index for both Series.

Data Analysis Python

# Import pandas and create sales_product_a and sales_product_b Series with given dates and values
# Your code here

Need a hint?

Use pd.Series with index=pd.to_datetime([...]) to create Series with date indexes.

2

Set minimum sales threshold

Create a variable called min_sales and set it to 10. This will be used later to filter total sales.

Data Analysis Python

import pandas as pd

sales_product_a = pd.Series([10, 15, 7], index=pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-04']))
sales_product_b = pd.Series([5, 8, 12], index=pd.to_datetime(['2024-06-01', '2024-06-03', '2024-06-04']))
# Create min_sales variable and set it to 10
# Your code here

Need a hint?

Just create a variable named min_sales and assign the value 10.

3

Add sales data with alignment

Create a new Series called total_sales by adding sales_product_a and sales_product_b using the add() method. Use fill_value=0 to treat missing sales as zero.

Data Analysis Python

import pandas as pd

sales_product_a = pd.Series([10, 15, 7], index=pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-04']))
sales_product_b = pd.Series([5, 8, 12], index=pd.to_datetime(['2024-06-01', '2024-06-03', '2024-06-04']))

min_sales = 10
# Add sales_product_a and sales_product_b using add() with fill_value=0 and store in total_sales
# Your code here

Need a hint?

Use sales_product_a.add(sales_product_b, fill_value=0) to add with alignment.

4

Print total sales

Print the total_sales Series to see the combined sales per date.

Data Analysis Python

import pandas as pd

sales_product_a = pd.Series([10, 15, 7], index=pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-04']))
sales_product_b = pd.Series([5, 8, 12], index=pd.to_datetime(['2024-06-01', '2024-06-03', '2024-06-04']))

min_sales = 10

total_sales = sales_product_a.add(sales_product_b, fill_value=0)
# Print total_sales
# Your code here

Need a hint?

Use print(total_sales) to show the combined sales.