我有以下两个数据框,我想基于col A加入该数据框
df1:
+------+--------+-------+
| A | B | C |
+------+--------+-------+
| a1 | 5 | asd |
| a2 | 12 | asd |
+------+--------+-------+
df2:
+------+--------+-------+
| A | B | D |
+------+--------+-------+
| a1 | 8 | qwe |
| a2 | 10 | qwe |
+------+--------+-------+
由于B列相同,因此假设在两者之间进行选择是一种逻辑,例如选择
+------+--------+------+-----+
| A | B | C | D |
+------+--------+------+-----+
| a1 | 8 | asd | qwe |
| a2 | 12 | asd | qwe |
+------+--------+-------+----+
实现此目标的简单方法是:
val _df1 = df1.withColumnRenamed("B","B_df1")
val _df2 = df2.withColumnRenamed("B", "B_df2)
_df1.join(_df2, Seq("A"))
.withColumn("B", when(col("B_df1") > col("B_df2"),
col("B_df1"))
.otherwise(col("B_df2"))
.drop(col("B_df1")
.drop("B_df2")
有没有更好的方法来实现此目的而无需重命名和删除列?
答案 0 :(得分:1)
这是使用selectExpr
的另一种方法。它节省了删除列的任何工作。
import spark.implicits._
val df1 = Seq(("a1",5,"asd"),
("a2",12,"asd")
).toDF("A","B","C")
val df2 = Seq(("a1",8,"qwe"),
("a2",10,"qwe")
).toDF("A","B","D")
import org.apache.spark.sql.functions.col
df1.as("a").join(df2.as("b"), col("a.A") === col("b.A")).selectExpr("a.A AS A",
"CASE WHEN a.B>b.B THEN a.B ELSE b.B END AS B",
"a.C AS C",
"b.D AS D").show()