moving or rolling average

from pyspark.sql.types import Row

# Sample data
data = [Row(Date='2023-01-01', ProductID=100, QuantitySold=10),
        Row(Date='2023-01-02', ProductID=100, QuantitySold=15),
        Row(Date='2023-01-03', ProductID=100, QuantitySold=20),
        Row(Date='2023-01-04', ProductID=100, QuantitySold=25),
        Row(Date='2023-01-05', ProductID=100, QuantitySold=30),
        Row(Date='2023-01-06', ProductID=100, QuantitySold=35),
        Row(Date='2023-01-07', ProductID=100, QuantitySold=40),
        Row(Date='2023-01-08', ProductID=100, QuantitySold=45)]

# Create DataFrame
df_sales = spark.createDataFrame(data)

# Convert Date string to Date type
df_sales = df_sales.withColumn("Date", to_date(col("Date")))

# Window specification for 7-day rolling average
windowSpec = Window.partitionBy('ProductID').orderBy('Date').rowsBetween(-6, 0)

# Calculating the rolling average
rollingAvg = df_sales.withColumn('7DayAvg', avg('QuantitySold').over(windowSpec))

# Show results
rollingAvg.show()

+----------+---------+------------+-------+
|      Date|ProductID|QuantitySold|7DayAvg|
+----------+---------+------------+-------+
|2023-01-01|      100|          10|   10.0|
|2023-01-02|      100|          15|   12.5|
|2023-01-03|      100|          20|   15.0|
|2023-01-04|      100|          25|   17.5|
|2023-01-05|      100|          30|   20.0|
|2023-01-06|      100|          35|   22.5|
|2023-01-07|      100|          40|   25.0|
|2023-01-08|      100|          45|   30.0|
+----------+---------+------------+-------+

The code is same for n day average