Rolling mean and sum in Pandas - Mini Project: Build & Apply

Rolling mean and sum

📖 Scenario: You work as a data analyst for a small store. You have daily sales data for a week. Your manager wants to see the average and total sales over the last 3 days for each day to understand trends better.

🎯 Goal: Build a program that calculates the rolling mean and rolling sum of sales over a 3-day window using pandas.

📋 What You'll Learn

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

Set a window size variable for rolling calculations.

Calculate rolling mean and rolling sum using pandas.

Print the resulting DataFrame showing original sales, rolling mean, and rolling sum.

💡 Why This Matters

🌍 Real World

Rolling averages and sums help smooth out daily fluctuations in sales data, making it easier to spot trends and patterns over time.

💼 Career

Data analysts and scientists often use rolling calculations to analyze time series data in finance, sales, weather, and many other fields.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with a column 'sales' containing these exact values: [10, 20, 15, 30, 25, 40, 35].

Pandas

import pandas as pd
# Create the sales_data DataFrame with the given sales values
# 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 to represent the 3-day rolling window.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [10, 20, 15, 30, 25, 40, 35]})
# 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 rolling mean and sum

Use the rolling method on sales_data['sales'] with the window size window_size to create two new columns: 'rolling_mean' and 'rolling_sum' that store the rolling mean and rolling sum respectively.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [10, 20, 15, 30, 25, 40, 35]})
window_size = 3
# Calculate rolling mean and rolling sum and add as new columns
# Your code here

Need a hint?

Use .rolling(window=window_size).mean() and .rolling(window=window_size).sum() on the sales column.

4

Print the final DataFrame

Print the sales_data DataFrame to display the original sales, rolling mean, and rolling sum columns.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': [10, 20, 15, 30, 25, 40, 35]})
window_size = 3
sales_data['rolling_mean'] = sales_data['sales'].rolling(window=window_size).mean()
sales_data['rolling_sum'] = sales_data['sales'].rolling(window=window_size).sum()
# Print the sales_data DataFrame
# Your code here

Need a hint?

Use print(sales_data) to show the DataFrame.