Expanding window operations in Pandas - Mini Project: Build & Apply

Expanding Window Operations with pandas

📖 Scenario: You work as a data analyst for a small online store. You have daily sales data and want to understand how the total sales grow over time. Instead of just looking at daily sales, you want to see the running total of sales up to each day.

🎯 Goal: Build a pandas DataFrame with daily sales data, then use expanding window operations to calculate the running total of sales for each day.

📋 What You'll Learn

Create a pandas DataFrame with exact daily sales data

Create a variable to hold the sales column name

Use pandas expanding() method to calculate running total sales

Print the resulting DataFrame with the running total column

💡 Why This Matters

🌍 Real World

Expanding window operations help analyze cumulative trends in sales, stock prices, or any time series data.

💼 Career

Data analysts and scientists use expanding windows to summarize data over time and detect patterns or growth.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with two columns: 'day' and 'sales'. Use these exact values: days from 1 to 5, and sales as 100, 150, 200, 130, 170.

Pandas

# Create the sales_data DataFrame with 'day' and 'sales' columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing the exact lists for 'day' and 'sales'.

2

Set the sales column name

Create a variable called sales_column and set it to the string 'sales'.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5],
    'sales': [100, 150, 200, 130, 170]
})

# Create the sales_column variable
# Your code here

Need a hint?

Just assign the string 'sales' to the variable sales_column.

3

Calculate running total sales using expanding window

Create a new column in sales_data called 'running_total' by applying the pandas expanding() method on the sales_column and then calling sum().

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5],
    'sales': [100, 150, 200, 130, 170]
})

sales_column = 'sales'

# Calculate running total sales using expanding sum
# Your code here

Need a hint?

Use sales_data[sales_column].expanding().sum() to get the running total and assign it to a new column.

4

Print the DataFrame with running totals

Write a print() statement to display the sales_data DataFrame with the new 'running_total' column.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5],
    'sales': [100, 150, 200, 130, 170]
})

sales_column = 'sales'

sales_data['running_total'] = sales_data[sales_column].expanding().sum()

# Print the sales_data DataFrame
# Your code here

Need a hint?

Use print(sales_data) to show the DataFrame with the running totals.