根据你的问题，这是我能想到的最接近的了-
这样做的目的是使 temp_df1 dataframe将行转置到各个列，然后 join 与 temp_df2 获取所需的 value 领域

import pyspark

from pyspark.sql import SQLContext
import pyspark.sql.functions as F

temp_df1 = spark.createDataFrame(
    [
        ("A","Test1"),
        ("A","Test2"),
        ("A","Test3"),
        ("B","Test1"),
        ("B","Test2"),
    ],
    ["key","value"]
)

temp_df1 = temp_df1.groupby("key").pivot("value").agg(F.first("value"))#.show()

temp_df2 = spark.createDataFrame(
    [
        ("A","Value1"),
        ("A","Value2"),
        ("B","Value1"),
        ("B","Value2"),
    ],
    ["key","value"]
)

temp_df3 = temp_df1.join(temp_df2,temp_df1['key'] == temp_df2['key'],'inner').select(
                                                  temp_df1['*']
                                                  ,temp_df2['value']
  )

temp_df3.show()

+---+-----+-----+-----+------+
|key|Test1|Test2|Test3| value|
+---+-----+-----+-----+------+
|  A|Test1|Test2|Test3|Value1|
|  A|Test1|Test2|Test3|Value2|
|  B|Test1|Test2| null|Value1|
|  B|Test1|Test2| null|Value2|
+---+-----+-----+-----+------+