Set Operation

Allows you to perform addition or subtraction of rows from DataFrames with identical schemas and different data.

Parameters

Parameter	Description	Required
DataFrame 1	First input DataFrame	True
DataFrame 2	Second input DataFrame	True
DataFrame N	Nth input DataFrame	False
Operation type	Operation to perform - Union: Returns a DataFrame containing rows in any one of the input DataFrames, while preserving duplicates. - Intersect All: Returns a DataFrame containing rows in all of the input DataFrames, while preserving duplicates. - Except All: Returns a DataFrames containing rows in the first DataFrame, but not in the other DataFrames, while preserving duplicates.	True

info

To add more input DataFrames, simply click + icon on the left sidebar

Examples

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Operation Type - Union

Python

def union(spark: SparkSession, in0: DataFrame, in1: DataFrame, ) -> DataFrame:
    return in0.unionAll(in1)

Scala

object union {
  def apply(spark: SparkSession, in0: DataFrame, in1: DataFrame): DataFrame =
    in0.unionAll(in1)
}

---

Operation Type - Intersect All

Python

def intersectAll(spark: SparkSession, in0: DataFrame, in1: DataFrame, ) -> DataFrame:
    return in0.intersectAll(in1)

Scala

object intersectAll {
  def apply(spark: SparkSession, in0: DataFrame, in1: DataFrame): DataFrame =
    in0.intersectAll(in1)
}

---

Operation Type - Except All

Python

def exceptAll(spark: SparkSession, in0: DataFrame, in1: DataFrame, ) -> DataFrame:
    return in0.exceptAll(in1)

Scala

object exceptAll {
  def apply(spark: SparkSession, in0: DataFrame, in1: DataFrame): DataFrame =
    in0.exceptAll(in1)
}