Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/5799#discussion_r29404815
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala ---
@@ -1414,4 +1415,37 @@ class DataFrame private[sql](
val jrdd = rdd.map(EvaluatePython.rowToArray(_,
fieldTypes)).toJavaRDD()
SerDeUtil.javaToPython(jrdd)
}
+
+
/////////////////////////////////////////////////////////////////////////////
+ // Statistic functions
+
/////////////////////////////////////////////////////////////////////////////
+
+ // scalastyle:off
+ object stat {
+ // scalastyle:on
+
+ /**
+ * Finding frequent items for columns, possibly with false positives.
Using the algorithm
+ * described in `http://www.cs.umd.edu/~samir/498/karp.pdf`.
+ *
+ * @param cols the names of the columns to search frequent items in
+ * @param support The minimum frequency for an item to be considered
`frequent`
+ * @return A Local DataFrame with the Array of frequent items for each
column.
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
--- End diff --
in df we usually support List[String] and Seq[String]. This is one reason
why we are using a separate name space.
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