Python 两个数据帧的相等性

我有以下情况：

我有两个数据帧，只包含一列

DF1=(1,2,3,4,5)
DF2=(3,6,7,8,9,10)

基本上，这些值是键，如果DF1中的键不在DF2中，我将创建DF1的拼花文件（在当前示例中，它应该返回false）。我目前实现要求的方式是：

val df1count= DF1.count
val df2count=DF2.count
val diffDF=DF2.except(DF1)
val diffCount=diffDF.count
if(diffCount==(df2count-df1count)) true
else false

---

这种方法的问题是我调用了4次动作元素，这肯定不是最好的方法。有人能告诉我实现这一点的最佳有效方法吗？

您可以使用以下功能：

import org.apache.spark.sql.functions._

def diff(key: String, df1: DataFrame, df2: DataFrame): DataFrame = {
  val fields = df1.schema.fields.map(_.name)
  val diffColumnName = "Diff"

  df1
    .join(df2, df1(key) === df2(key), "full_outer")
    .withColumn(
      diffColumnName,
      when(df1(key).isNull, "New row in DataFrame 2")
        .otherwise(
          when(df2(key).isNull, "New row in DataFrame 1")
            .otherwise(
              concat_ws("",
                fields.map(f => when(df1(f) =!= df2(f), s"$f ").otherwise("")):_*
              )
            )
        )
    )
    .filter(col(diffColumnName) =!= "")
    .select(
      fields.map(f =>
        when(df1(key).isNotNull, df1(f)).otherwise(df2(f)).alias(f)
      ) :+ col(diffColumnName):_*
    )
}

在您的情况下，运行以下命令：

diff("emp_id", df1, df2)

示例

import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._

object DiffDataFrames extends App {
  val session = SparkSession.builder().master("local").getOrCreate()

  import session.implicits._

  val df1 = session.createDataset(Seq((1,"a",11),(2,"b",2),(3,"c",33),(5,"e",5))).toDF("n", "s", "i")
  val df2 = session.createDataset(Seq((1,"a",11),(2,"bb",2),(3,"cc",34),(4,"d",4))).toDF("n", "s", "i")

  def diff(key: String, df1: DataFrame, df2: DataFrame): DataFrame =
  /* above definition */

  diff("n", df1, df2).show(false)
}

---

以下是获取两个数据帧之间不常见行的方法：

val d1 = Seq((3, "Chennai", "rahman", "9848022330", 45000, "SanRamon"), (1, "Hyderabad", "ram", "9848022338", 50000, "SF"), (2, "Hyderabad", "robin", "9848022339", 40000, "LA"), (4, "sanjose", "romin", "9848022331", 45123, "SanRamon"))
val d2 = Seq((3, "Chennai", "rahman", "9848022330", 45000, "SanRamon"), (1, "Hyderabad", "ram", "9848022338", 50000, "SF"), (2, "Hyderabad", "robin", "9848022339", 40000, "LA"), (4, "sanjose", "romin", "9848022331", 45123, "SanRamon"), (4, "sanjose", "romino", "9848022331", 45123, "SanRamon"), (5, "LA", "Test", "1234567890", 12345, "Testuser"))

val df1 = d1.toDF("emp_id" ,"emp_city" ,"emp_name" ,"emp_phone" ,"emp_sal" ,"emp_site")
val df2 = d2.toDF("emp_id" ,"emp_city" ,"emp_name" ,"emp_phone" ,"emp_sal" ,"emp_site")

spark.sql("((select * from df1) union (select * from df2)) minus ((select * from df1) intersect (select * from df2))").show //spark is SparkSession

---

你能告诉我如何申报df1和df2吗。我在下面声明了类似的sqlContext=sqlContext（sc）df=sqlContext.sql（“从表1中选择*”）df2=sqlContext.sql（“从表2中选择*”），然后按原样处理上述代码。。。。正在获取语法错误。。。。我对spark scala Codec非常陌生，你能纠正我的错误吗？当我尝试运行以下代码时，我得到一个错误：未找到：值df1，未找到df2。。导入org.apache.spark.sql.{DataFrame，SQLContext}导入org.apache.spark.sql.functions.\uval sc:SparkContext val SQLContext val=new org.apache.spark.sql.SQLContext（sc）SQLContext=SQLContext（sc）df1=SQLContext.sql（“从表1中选择*”）df2=SQLContext.sql（“从表2中选择*”）diff（“租户”，df1，df2）def diff（key:String，df1:DataFrame，df2:DataFrame）：DataFrame={……}///diff-fun提供的代码hi，我添加了一个简短的示例。当数据帧没有key\u列时，如何连接df1和df2的多个列。如何更新上面的diff函数以处理多个键。