Window functions (expanding, ewm) in Data Analysis Python - Mini Project: Build & Apply

Analyzing Daily Sales Trends with Window Functions

📖 Scenario: You work in a small store that tracks daily sales. You want to understand how sales change over time and spot trends. Using window functions like expanding and exponentially weighted moving averages (ewm) helps you see the bigger picture beyond daily ups and downs.

🎯 Goal: Build a simple Python program that uses expanding and ewm window functions on daily sales data to calculate cumulative and smoothed sales values.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data

Add a configuration variable for the smoothing factor

Calculate expanding sum of sales

Calculate exponentially weighted moving average (ewm) of sales

Print the DataFrame with original and calculated columns

💡 Why This Matters

🌍 Real World

Stores and businesses use expanding sums to track total sales over time and ewm to smooth out daily fluctuations for better trend analysis.

💼 Career

Data analysts and scientists use window functions like expanding and ewm to prepare and analyze time series data for reports and decision-making.

1

Create daily sales data

Create a pandas DataFrame called sales_data with two columns: 'day' and 'sales'. Use these exact values: days 1 to 7, and sales values 100, 120, 130, 90, 150, 170, 160.

Data Analysis Python

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

Hint

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

2

Set smoothing factor for ewm

Create a variable called alpha and set it to 0.3. This will be the smoothing factor for the exponentially weighted moving average.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 150, 170, 160]
})

# Create alpha variable for smoothing factor
# Your code here

Hint

Just create a variable named alpha and assign it the value 0.3.

3

Calculate expanding sum and ewm average

Add two new columns to sales_data: 'expanding_sum' which is the expanding sum of sales, and 'ewm_avg' which is the exponentially weighted moving average of sales using the alpha variable.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 150, 170, 160]
})

alpha = 0.3

# Calculate expanding sum and assign to 'expanding_sum'
# Calculate ewm average with alpha and assign to 'ewm_avg'
# Your code here

Hint

Use expanding().sum() on the sales column for cumulative sum.
Use ewm(alpha=alpha).mean() on the sales column for smoothed average.

4

Print the sales data with calculations

Print the sales_data DataFrame to display the original sales, expanding sum, and ewm average columns.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 150, 170, 160]
})

alpha = 0.3

sales_data['expanding_sum'] = sales_data['sales'].expanding().sum()
sales_data['ewm_avg'] = sales_data['sales'].ewm(alpha=alpha).mean()

# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to show the DataFrame with all columns.