我认为将值组合到列表中而不是在列表中查找最大值是最简单的方法.

I think combing values to a list and than finding max on it would be the simplest approach.

from pyspark.sql.types import *

schema = StructType([
    StructField("ClientId", IntegerType(), True),
    StructField("m_ant21", IntegerType(), True),
    StructField("m_ant22", IntegerType(), True),
    StructField("m_ant23", IntegerType(), True),
    StructField("m_ant24", IntegerType(), True)
])

df = spark\
    .createDataFrame(
        data=[(0, None, None, None, None),
             (1, 23, 13, 17, 99),
             (2, 0, 0, 0, 1),
             (3, 0, None, 1, 0)],
        schema=schema)

import pyspark.sql.functions as F

def agg_to_list(m21,m22,m23,m24):
    return [m21,m22,m23,m24]

u_agg_to_list = F.udf(agg_to_list, ArrayType(IntegerType()))

df2 = df.withColumn('all_values', u_agg_to_list('m_ant21', 'm_ant22', 'm_ant23', 'm_ant24'))\
        .withColumn('max', F.sort_array("all_values", False)[0])\
        .select('ClientId', 'max')

df2.show()

输出:

+--------+----+
|ClientId|max |
+--------+----+
|0       |null|
|1       |99  |
|2       |1   |
|3       |1   |
+--------+----+