[GitHub] spark pull request #16971: [SPARK-19573][SQL] Make NaN/null handling consist...

[gatorsmile <gi...@git.apache.org>]

Github user gatorsmile commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16971#discussion_r103843868
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala ---
    @@ -89,22 +88,17 @@ 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 ignored in numerical columns before calculation. For
    +   *   columns only containing null or NaN values, an empty array 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,
    -        probabilities, relativeError).map(_.toArray).toArray
    -    } catch {
    -      case e: NoSuchElementException => null
    -    }
    +    StatFunctions.multipleApproxQuantiles(df.select(cols.map(col): _*), cols,
    +      probabilities, relativeError).map(_.toArray).toArray
    --- End diff --
    
    Nit: style issue
    ```
        StatFunctions.multipleApproxQuantiles(
          df.select(cols.map(col): _*),
          cols,
          probabilities,
          relativeError).map(_.toArray).toArray
    ```


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