2

I have a dataframe with 4 columns co1, col2, col3 and col4. I need to:

  • Group dataframe based on key col1 and col2
  • Then group other columns like col3 and col4 and display counts for col3 and col4.

Input

col1 col2 col3 col4
 1    1    2    4
 1    1    2    4
 1    1    3    5

Output

col1 col2 col_name col_value  cnt
1     1    col3      2         2
1     1    col3      3         1
1     1    col4      4         2
1     1    col4      5         1

Is this possible?

Alper t. Turker
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techie
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3 Answers3

1

This the case for melt like operation. You can use implementation provided by ahue as an answer to How to melt Spark DataFrame?.

val df = Seq(
  (1, 1, 2, 4), (1, 1, 2, 4), (1, 1, 3, 5)
).toDF("col1", "col2", "col3", "col4")


df.melt(
  Seq("col1", "col2"), Seq("col3", "col4"), "col_name", "col_value"
).groupBy("col1", "col2", "col_name", "col_value").count.show
// +----+----+--------+---------+-----+
// |col1|col2|col_name|col_value|count|
// +----+----+--------+---------+-----+
// |   1|   1|    col3|        3|    1|
// |   1|   1|    col4|        5|    1|
// |   1|   1|    col4|        4|    2|
// |   1|   1|    col3|        2|    2|
// +----+----+--------+---------+-----+
Alper t. Turker
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1

Here's one approach which should work for aribitrary numbers of key-columns and value-columns (Note that the sample dataset has been expanded for illustration purpose):

val df = Seq(
  (1, 1, 2, 4, 6),
  (1, 1, 2, 4, 7),
  (1, 1, 3, 5, 7)
).toDF("col1", "col2", "col3", "col4", "col5")

import org.apache.spark.sql.functions._

val keyCols = Seq("col1", "col2")
val valCols = Seq("col3", "col4", "col5")

val dfList = valCols.map( c => {
  val grpCols = keyCols :+ c

  df.groupBy(grpCols.head, grpCols.tail: _*).agg(count(col(c)).as("cnt")).
    select(keyCols.map(col) :+ lit(c).as("col_name") :+ col(c).as("col_value") :+ col("cnt"): _*)
} )

dfList.reduce(_ union _).show
// +----+----+--------+---------+---+
// |col1|col2|col_name|col_value|cnt|
// +----+----+--------+---------+---+
// |   1|   1|    col3|        3|  1|
// |   1|   1|    col3|        2|  2|
// |   1|   1|    col4|        4|  2|
// |   1|   1|    col4|        5|  1|
// |   1|   1|    col5|        6|  1|
// |   1|   1|    col5|        7|  2|
// +----+----+--------+---------+---+
Leo C
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0

We can use groupBy and union to achieve this. 

val x = Seq((1, 1,2,4),(1, 1,2,4),(1, 1,3,5)).toDF("col1", "col2", "col3", "col4")

val  y = x.groupBy("col1", "col2","col3").
          agg(count(col("col3")).alias("cnt")).
          withColumn("col_name", lit("col3")).
          select(col("col1"), col("col2"), col("col_name"), col("col3").alias("col_value"), col("cnt"))

val z = x.groupBy("col1", "col2","col4").
          agg(count(col("col4")).alias("cnt")).
          withColumn("col_name", lit("col4")).
          select(col("col1"), col("col2"), col("col_name"), col("col4").alias("col_value"), col("cnt"))

y.union(z).show()
pons
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