Rolling window calculations in Data Analysis Python - Mini Project: Build & Apply

Rolling Window Calculations

📖 Scenario: You work as a data analyst for a small store. You have daily sales data for a week. You want to understand how sales change over time by calculating the average sales over a rolling window of 3 days.

🎯 Goal: Build a small program that calculates the 3-day rolling average of daily sales using Python and pandas.

📋 What You'll Learn

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

Set a window size variable for the rolling calculation.

Calculate the rolling average of sales using the window size.

Print the resulting rolling average values.

💡 Why This Matters

🌍 Real World

Rolling window calculations help smooth out short-term fluctuations in data, making it easier to see trends. This is useful in sales, finance, weather data, and many other fields.

💼 Career

Data analysts and scientists often use rolling calculations to prepare data for reports and decision-making, helping businesses understand patterns and make better choices.

1

Create the sales data

Import pandas as pd and create a DataFrame called sales_data with two columns: 'day' containing numbers 1 to 7, and 'sales' containing these exact values: 100, 120, 130, 90, 110, 115, 105.

Data Analysis Python

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame with a dictionary to create the data.

2

Set the rolling window size

Create a variable called window_size and set it to the integer 3 to represent the 3-day window.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 110, 115, 105]
})
# Set the window_size variable to 3
# Your code here

Hint

Just assign the number 3 to window_size.

3

Calculate the rolling average

Create a new column in sales_data called 'rolling_avg' that contains the rolling average of the 'sales' column using the window_size. Use the pandas rolling() method followed by mean().

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 110, 115, 105]
})
window_size = 3
# Calculate the rolling average and store it in 'rolling_avg' column
# Your code here

Hint

Use sales_data['sales'].rolling(window=window_size).mean() to get the rolling average.

4

Print the rolling average

Print the 'rolling_avg' column from the sales_data DataFrame to see the rolling averages.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 110, 115, 105]
})
window_size = 3
sales_data['rolling_avg'] = sales_data['sales'].rolling(window=window_size).mean()
# Print the 'rolling_avg' column
# Your code here

Hint

Use print(sales_data['rolling_avg']) to display the rolling averages.