rolling() for moving windows in Pandas - Mini Project: Build & Apply

Using pandas rolling() for Moving Window Calculations

📖 Scenario: You work as a data analyst for a small bakery. You have daily sales data for a week. You want to understand the trend by calculating the moving average of sales over a 3-day window.

🎯 Goal: Build a small program that uses pandas rolling() to calculate the 3-day moving average of daily sales and display the result.

📋 What You'll Learn

Create a pandas DataFrame with exact daily sales data for 7 days

Create a variable for the rolling window size set to 3

Use the rolling() method on the sales column with the window size variable

Calculate the mean for each rolling window

Print the resulting moving average series

💡 Why This Matters

🌍 Real World

Moving averages help smooth out short-term fluctuations in data, making it easier to see trends. Businesses use this to analyze sales, stock prices, or website traffic.

💼 Career

Data analysts and scientists often use rolling window calculations to prepare data for reports and forecasting.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with a column 'sales' containing these exact values: [10, 20, 15, 25, 30, 20, 40].

Pandas

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

Need a hint?

Use pd.DataFrame and pass a dictionary with key 'sales' and the list of values.

2

Set the rolling window size

Create a variable called window_size and set it to 3.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [10, 20, 15, 25, 30, 20, 40]})
# Set the window_size variable to 3
# Your code here

Need a hint?

Just assign the number 3 to the variable window_size.

3

Calculate the 3-day moving average

Create a new variable called moving_avg that uses sales_data['sales'].rolling(window_size).mean() to calculate the moving average.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [10, 20, 15, 25, 30, 20, 40]})
window_size = 3
# Calculate the moving average using rolling() and mean()
# Your code here

Need a hint?

Use rolling(window_size) on the sales column, then call mean().

4

Print the moving average result

Print the moving_avg variable to display the 3-day moving average values.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [10, 20, 15, 25, 30, 20, 40]})
window_size = 3
moving_avg = sales_data['sales'].rolling(window_size).mean()
# Print the moving_avg variable
# Your code here

Need a hint?

Use print(moving_avg) to show the result.