For example, I have a Spark DataFrame with three columns 'Domain', 'ReturnCode', and 'RequestType' Example Starting Dataframe www.google.com,200,GET
www.google.com,300,GET
www.espn.com,200,POST
I would like to pivot on Domain and get aggregate counts for the various ReturnCodes and RequestTypes. Do I have to pivot each table separately and then join them back together or is it possible to do it in one step? Desired Dataframe Domain,200,300,GET,POST
www.google.com,1,1,2,0
www.espn.com,1,0,0,1
Example of Pivot Code with Join val dfa = df.groupby('Domain').pivot('ReturnCode').count()
val dfb = df.groupby('Domain').pivot('RequestType').count()
dfa.join(dfb, Seq("ReferrerDomain", "ReferrerDomain")).show()
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