简单的想法：
首先通过F.array函数将所有列收集到列表中
和
在groupBy上做F.collect_list。

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

sc = SparkContext('local')
sqlContext = SQLContext(sc)

data1 = [

[1, 35,"Male","2023-10-01",200],
[1, 35,"Male","2023-10-02",210],
[2, 28,"Female","2023-10-01",150],
[2, 28,"Female","2023-10-02",160],

]

columns =["member_id", "age", "gender", "date", "cost"]

df1 = sqlContext.createDataFrame(data=data1, schema=columns)

df1.show(n=10, truncate=False)
print("Collect columns into list")
df2 = df1.withColumn("colList", F.array(*df1.columns))
df2.show(n=10, truncate=False)
print("collect_list on the groupBy")
df3 = df2.groupBy("member_id").agg(F.collect_list(F.col("colList")))
df3.show(n=10, truncate=False)

字符串
输出量：

+---------+---+------+----------+----+
|member_id|age|gender|date      |cost|
+---------+---+------+----------+----+
|1        |35 |Male  |2023-10-01|200 |
|1        |35 |Male  |2023-10-02|210 |
|2        |28 |Female|2023-10-01|150 |
|2        |28 |Female|2023-10-02|160 |
+---------+---+------+----------+----+

Collect columns into list
+---------+---+------+----------+----+--------------------------------+
|member_id|age|gender|date      |cost|colList                         |
+---------+---+------+----------+----+--------------------------------+
|1        |35 |Male  |2023-10-01|200 |[1, 35, Male, 2023-10-01, 200]  |
|1        |35 |Male  |2023-10-02|210 |[1, 35, Male, 2023-10-02, 210]  |
|2        |28 |Female|2023-10-01|150 |[2, 28, Female, 2023-10-01, 150]|
|2        |28 |Female|2023-10-02|160 |[2, 28, Female, 2023-10-02, 160]|
+---------+---+------+----------+----+--------------------------------+

collect_list on the groupBy
+---------+--------------------------------------------------------------------+
|member_id|collect_list(colList)                                               |
+---------+--------------------------------------------------------------------+
|1        |[[1, 35, Male, 2023-10-01, 200], [1, 35, Male, 2023-10-02, 210]]    |
|2        |[[2, 28, Female, 2023-10-01, 150], [2, 28, Female, 2023-10-02, 160]]|
+---------+--------------------------------------------------------------------+

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