如何在PySpark DataFrame中的GroupBy和聚合之后传递第三方列？

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()

data = [("Alice", 25, "NYC"), ("Bob", 30, "LA"), ("Alice", 35, "LA"), ("Bob", 20, "NYC")]
df = spark.createDataFrame(data, ["Name", "Age", "City"])
df.show()

+-----+---+----+
| Name|Age|City|
+-----+---+----+
|Alice| 25| NYC|
|  Bob| 30|  LA|
|Alice| 35|  LA|
|  Bob| 20| NYC|
+-----+---+----+

result = df.groupBy("Name").agg({"Age": "avg"})
result.show()

+-----+--------+
| Name|avg(Age)|
+-----+--------+
|  Bob|    25.0|
|Alice|    30.0|
+-----+--------+

from pyspark.sql.functions import col, max

result_with_city = result.withColumn("City", col("Name"))
df_max_age = df.groupBy("Name").agg(max("Age").alias("max_age"))
result_with_city = result_with_city.join(df_max_age, "Name")
result_with_city.show()

+-----+--------+----+
| Name|avg(Age)|City|
+-----+--------+----+
|  Bob|    25.0| NYC|
|Alice|    30.0| NYC|
+-----+--------+----+