Create Moving-avg.py

Moving-avg.py

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+# In a DataFrame df_sales with columns Date, ProductID, and QuantitySold, how would you calculate a 7-day rolling average of QuantitySold for each product?
+
+from pyspark.sql import SparkSession
+from pyspark.sql import Row
+from pyspark.sql.window import Window
+import pyspark.sql.functions as F
+
+# Initialize Spark session
+spark = SparkSession.builder.appName("RollingAverageCalculation").getOrCreate()
+
+# 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", F.to_date(F.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', F.avg('QuantitySold').over(windowSpec))
+
+# Show results
+rollingAvg.show()