Github user windpiger commented on a diff in the pull request: https://github.com/apache/spark/pull/15994#discussion_r89727312 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala --- @@ -437,4 +444,38 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { case v => throw new IllegalArgumentException( s"Unsupported value type ${v.getClass.getName} ($v).") } + + /** + * (Scala-specific) Returns a new [[DataFrame]] that replaces null or NaN values in specified + * numeric, bool, string columns. If a specified column is not a numeric, boolean, string column, + * it is ignored. + * + * @since 2.1.0 + */ + private def fill1[T](value: T, cols: Seq[String]): DataFrame = { + value match { + case _: jl.Double | _: jl.Integer | _: jl.Float | _: jl.Boolean | _: jl.Long | _: String => + case _ => + throw new IllegalArgumentException( + s"Unsupported value type ${value.getClass.getName} ($value).") + } + + val targetColumnType = value match { + case _: jl.Double | _: jl.Integer | _: jl.Float | _: jl.Long => NumericType + case _: jl.Boolean => BooleanType + case _: String => StringType + } + + val columnEquals = df.sparkSession.sessionState.analyzer.resolver + val projections = df.schema.fields.map { f => + // Only fill if the column is part of the cols list. + if (((f.dataType.isInstanceOf[NumericType] && targetColumnType == NumericType) --- End diff -- Thanks! I have modified except one: If T is a double type , this should be apply to all Numeric columns(include LongType/IntegerType), or just apply to FractionType? The fill(value Double) apply to all Numeric columns, and I think fill(value Long) also keep the logic.
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