下面是我的两个输入PySpark Dataframe
Dataframe 1

li = [('abc', 'xyz')]
liColumns = ["aid", "bid"]
tempDF = spark.createDataFrame(data=li, schema = liColumns)
tempDF.printSchema()
tempDF.show(truncate=False)
+---+---+
|aid|bid|
+---+---+
|abc|xyz|
+---+---+

Dataframe 2

other_li = [('abc', '111', 'desc111'), ('abc', '112', 'desc112'), ('xyz', 'A123', 'city'), ('xyz', 'A456', 'state'), ('xyz', 'A789', 'zip')]
otherColumns = ['real_aid', 'code', 'some_value']
otherDF = spark.createDataFrame(data=other_li, schema = otherColumns)
otherDF.printSchema()
otherDF.show(truncate=False)
+--------+----+----------+
|real_aid|code|some_value|
+--------+----+----------+
|abc     |111 |desc111   |
|abc     |112 |desc112   |
|xyz     |A123|city      |
|xyz     |A456|state     |
|xyz     |A789|zip       |
+--------+----+----------+

问题：我如何合并这两个 Dataframe 来获得第三个 Dataframe 。2我知道如何使用append/union来完成这一操作，但是有没有一种方法可以使用joins来完成这一操作？3或者有没有一种方法可以更有效地完成这一操作？4我需要在两个大表上完成这一操作。

预期 Dataframe

output_li = [('abc', '111', 'desc111'), ('abc', '112', 'desc112'), ('abc', 'A123', 'city'), ('abc', 'A456', 'state'), ('abc', 'A789', 'zip'), ('xyz', 'A123', 'city'), ('xyz', 'A456', 'state'), ('xyz', 'A789', 'zip')]
otherColumns = ['real_aid', 'code', 'some_value']
otherDF = spark.createDataFrame(data=output_li, schema = otherColumns)
otherDF.printSchema()
otherDF.show(truncate=False)
+--------+----+----------+
|real_aid|code|some_value|
+--------+----+----------+
|abc     |111 |desc111   |
|abc     |112 |desc112   |
|abc     |A123|city      |
|abc     |A456|state     |
|abc     |A789|zip       |
|xyz     |A123|city      |
|xyz     |A456|state     |
|xyz     |A789|zip       |
+--------+----+----------+