如何用字典正确使用reduce

我正在使用自定义函数作为reduce操作的一部分。对于以下示例,我收到以下消息 TypeError: reduce() takes no keyword arguments -我相信这是由于我使用字典的方式造成的 mapping 在函数中 exposed_colum -你能帮我修一下这个功能吗?

from pyspark.sql import DataFrame, Row
from pyspark.sql.functions import col
from pyspark.sql import SparkSession
from functools import reduce


def process_data(df: DataFrame):
    col_mapping = dict(zip(["name", "age"], ["a", "b"]))

    # Do other things...

    def exposed_column(df: DataFrame, mapping: dict):
        return df.select([col(c).alias(mapping.get(c, c)) for c in df.columns])

    return reduce(exposed_column, sequence=col_mapping, initial=df)


spark = SparkSession.builder.appName("app").getOrCreate()
l = [
    ("Bob", 25, "Spain"),
    ("Marc", 22, "France"),
    ("Steve", 20, "Belgium"),
    ("Donald", 26, "USA"),
]
rdd = spark.sparkContext.parallelize(l)
people = rdd.map(lambda x: Row(name=x[0], age=int(x[1]), country=x[2])).toDF()

people.show()
process_data(people).show()

people.show() 看起来像这样

+---+-------+------+
|age|country|  name|
+---+-------+------+
| 25|  Spain|   Bob|
| 22| France|  Marc|
| 20|Belgium| Steve|
| 26|    USA|Donald|
+---+-------+------+

这是预期的输出

+------+---+
|     a|  b|
+------+---+
|   Bob| 25|
|  Marc| 22|
| Steve| 20|
|Donald| 26|
+------+---+