SPARK DataFrame: How to efficiently split dataframe for each group based on same column values

Question

I have a DataFrame generated as follows:

df.groupBy($\"Hour\", $\"Category\")
  .agg(sum($\"value\").alias(\"TotalValue\"))
  .sort($\"Hour\".asc,$\"TotalVal         


        

Answer 1

This has been answered here for Spark (Scala):

How can I split a dataframe into dataframes with same column values in SCALA and SPARK

and here for pyspark:

PySpark - Split/Filter DataFrame by column's values