Calculating Sales Differences Using PySpark: Lag Function and Window Specifications

In this video, we demonstrate how to use PySpark to analyze sales data by applying the lag function and window specifications. We start with a sample dataset of sales transactions with new salesperson names and dates.
Data Creation: We create a DataFrame with columns for salesperson names, dates, and sales amounts.
Displaying the DataFrame: We show the initial DataFrame for a clear view of the data.
Window Specification: We define a window specification that partitions the data by salesperson and orders it by date.
Lag Function: We apply the lag function to calculate the previous day's sales for each salesperson.
Sales Difference Calculation: We compute the day-to-day sales difference by subtracting the previous day's sales from the current day's sales.
Final Display: We present the DataFrame with the new columns for previous sales and sales difference.
By following along, you'll learn how to efficiently track and compare sales performance over time using PySpark.