Github user thunterdb commented on a diff in the pull request: https://github.com/apache/spark/pull/16971#discussion_r102589719 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala --- @@ -89,18 +89,17 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * Note that values greater than 1 are accepted but give the same result as 1. * @return the approximate quantiles at the given probabilities of each column * - * @note Rows containing any null or NaN values will be removed before calculation. If - * the dataframe is empty or all rows contain null or NaN, null is returned. + * @note null and NaN values will be removed from the numerical column before calculation. If + * the dataframe is empty, or all rows in some column contain null or NaN, null is returned. * * @since 2.2.0 */ def approxQuantile( cols: Array[String], probabilities: Array[Double], relativeError: Double): Array[Array[Double]] = { - // TODO: Update NaN/null handling to keep consistent with the single-column version try { - StatFunctions.multipleApproxQuantiles(df.select(cols.map(col): _*).na.drop(), cols, + StatFunctions.multipleApproxQuantiles(df.select(cols.map(col): _*), cols, probabilities, relativeError).map(_.toArray).toArray } catch { case e: NoSuchElementException => null --- End diff -- +1. I tend to think that the result should be `NaN` (following the IEEE convention) or `null` (following scala Option convention). But pending a resolution, I am fine with throwing an exception because it is the most conservative behavior (stopping computations).
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