Why window functions matter in Pandas - See It in Action

Why window functions matter

📖 Scenario: Imagine you work in a sales team. You have daily sales data for a store. You want to see how sales change day by day and understand trends better.

🎯 Goal: You will create a small sales dataset, set a window size, calculate the rolling average sales using a window function, and print the result. This helps smooth out daily ups and downs to see trends.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Create a variable for the window size

Use pandas rolling window function to calculate the rolling average

Print the resulting DataFrame with the rolling average column

💡 Why This Matters

🌍 Real World

Rolling averages are used in sales, finance, and weather data to smooth out short-term fluctuations and highlight longer-term trends.

💼 Career

Data analysts and scientists use window functions like rolling averages to prepare data for reports and decision-making.

1

Create the sales data

Create a pandas DataFrame called sales_data with two columns: 'date' and 'sales'. Use these exact values for 'date': '2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'. Use these exact values for 'sales': 100, 120, 130, 90, 110. Convert the 'date' column to datetime type.

Pandas

# Import pandas and create the sales_data DataFrame with the exact dates and sales
# Your code here

Need a hint?

Use pd.DataFrame to create the data and pd.to_datetime to convert the date column.

2

Set the window size

Create a variable called window_size and set it to 3. This will be the number of days to look back when calculating the rolling average.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'date': ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'],
    'sales': [100, 120, 130, 90, 110]
})
sales_data['date'] = pd.to_datetime(sales_data['date'])

# Create window_size variable and set it to 3
# Your code here

Need a hint?

Just create a variable named window_size and assign the number 3.

3

Calculate the rolling average sales

Create a new column in sales_data called 'rolling_avg'. Use the pandas rolling function on the 'sales' column with the window size window_size and calculate the mean for each window.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'date': ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'],
    'sales': [100, 120, 130, 90, 110]
})
sales_data['date'] = pd.to_datetime(sales_data['date'])

window_size = 3

# Calculate rolling average and add it as 'rolling_avg' column
# Your code here

Need a hint?

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

4

Print the sales data with rolling average

Print the sales_data DataFrame to see the original sales and the new rolling average column.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'date': ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'],
    'sales': [100, 120, 130, 90, 110]
})
sales_data['date'] = pd.to_datetime(sales_data['date'])

window_size = 3

sales_data['rolling_avg'] = sales_data['sales'].rolling(window=window_size).mean()

# Print the sales_data DataFrame
# Your code here

Need a hint?

Use print(sales_data) to show the DataFrame with the rolling average.