Github user brkyvz commented on a diff in the pull request:
https://github.com/apache/spark/pull/5842#discussion_r29545291
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
---
@@ -77,4 +78,42 @@ private[sql] object StatFunctions {
})
counts.cov
}
+
+ /** Generate a table of frequencies for the elements of two columns. */
+ private[sql] def crossTabulate(df: DataFrame, col1: String, col2:
String): DataFrame = {
+ val tableName = s"${col1}_$col2"
+ val distinctCol2 =
df.select(col2).distinct.collect().sortBy(_.get(0).toString)
--- End diff --
That's what I did first. Xiangrui thought this would be more efficient.
On Fri, May 1, 2015 at 10:16 PM, Reynold Xin <notificati...@github.com>
wrote:
> In
>
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
> <https://github.com/apache/spark/pull/5842#discussion_r29545262>:
>
> > @@ -77,4 +78,42 @@ private[sql] object StatFunctions {
> > })
> > counts.cov
> > }
> > +
> > + /** Generate a table of frequencies for the elements of two columns.
*/
> > + private[sql] def crossTabulate(df: DataFrame, col1: String, col2:
String): DataFrame = {
> > + val tableName = s"${col1}_$col2"
> > + val distinctCol2 =
df.select(col2).distinct.collect().sortBy(_.get(0).toString)
>
> btw - isn't a more efficient way to run this is to do groupBy(col1,
> col2).count(), and then pivot the table?
>
> â
> Reply to this email directly or view it on GitHub
> <https://github.com/apache/spark/pull/5842/files#r29545262>.
>
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