title: Spark groupby multiple columns separately
body: <p>I have a Spark dataframe like below, and I want to perform some aggregate functions on it by different columns independently of each other and get some statistics over a single column.</p>
  
  <pre><code>val df = (Seq((1, "a", "1"),
                (1,"b", "3"),
                (1,"c", "6"),
                (2, "a", "9"),
                (2,"c", "10"),
                (1,"b","8" ),
                (2, "c", "3"),
                (3,"r", "19")).toDF("col1", "col2", "col3"))
  
  df.show()
  +----+----+----+
  |col1|col2|col3|
  +----+----+----+
  |   1|   a|   1|
  |   1|   b|   3|
  |   1|   c|   6|
  |   2|   a|   9|
  |   2|   c|  10|
  |   1|   b|   8|
  |   2|   c|   3|
  |   3|   r|  19|
  +----+----+----+
  </code></pre>
  
  <p>I want to group by col1 and col2 and get the average of column col3 to get the following output dataframe:</p>
  
  <pre><code>+----+----+----+---------+---------+
  |col1|col2|col3|mean_col1|mean_col2|
  +----+----+----+---------+---------+
  |   1|   a|   1|      4.5|      5.0|
  |   1|   b|   3|      4.5|      5.5|
  |   1|   c|   6|      4.5|     6.33|
  |   2|   a|   9|     7.33|      5.0|
  |   2|   c|  10|     7.33|     6.33|
  |   1|   b|   8|      4.5|      5.5|
  |   2|   c|   3|     7.33|     6.33|
  |   3|   r|  19|     19.0|     19.0|
  +----+----+----+---------+---------+
  </code></pre>
  
  <p>This can be done using the following operations:</p>
  
  <pre><code>val col1df = df.groupBy("col1").agg(round(mean("col3"),2).alias("mean_col1"))
  
  val col2df = df.groupBy("col2").agg(round(mean("col3"),2).alias("mean_col2"))
  
  df.join(col1df, "col1").join(col2df, "col2").select($"col1",$"col2",$"col3",$"mean_col1",$"mean_col2").show()
  </code></pre>
  
  <p>However, if I have many more columns to group by, I'd need to perform several expensive join operations. Moreover, grouping by each column before doing the join seems rather cumbersome. What's the best way to get the output dataframe by minimizing (and preferably eliminating) the join operations, and without having to generate the dataframes col1df and col2df ?</p>