Arrays 如何将结构或类的数组从UDF返回到dataframe列值中？

我只想将列值设置为UDF返回的stuct数组。它给我的错误是：

TypeError:new（）正好接受3个参数（给定1个）

TypeError回溯（最近的调用 最后）在（） 22返回日期 23 --->24 MergeAdjacentUsages=udf（MergeAdjacentUsage，ArrayType（Dates（））） 25 26 df1=df.groupBy（['ID'，'pID']）.agg（MergeAdjacentUsages（F.collect_list（struct（'startTime'，'endTime'）））。别名（“Times”））

---

任何帮助、想法或提示都将不胜感激。

pyspark不允许用户定义类对象作为数据框列类型。相反，我们需要创建

StructType

，它可以类似于python中的类/命名元组

例如：

d = [{'ID': '1', 'pID': 1000, 'startTime':'2018.07.02T03:34:20', 'endTime':'2018.07.03T02:40:20'}, {'ID': '1', 'pID': 1000, 'startTime':'2018.07.02T03:45:20', 'endTime':'2018.07.03T02:50:20'}, {'ID': '2', 'pID': 2000, 'startTime':'2018.07.02T03:34:20', 'endTime':'2018.07.03T02:40:20'}, {'ID': '2', 'pID': 2000, 'startTime':'2018.07.02T03:45:20', 'endTime':'2018.07.03T02:50:20'}]

df = spark.createDataFrame(d)

Dates = namedtuple("Dates", "startTime endTime")


def MergeAdjacentUsage(timeSets):
  DatesArray = []
  for times in timeSets:
    DatesArray.append(Dates(startTime=times.startTime, endTime=times.endTime))
  return DatesArray


MergeAdjacentUsages = udf(MergeAdjacentUsage,ArrayType(Dates()))

df1=df.groupBy(['ID','pID']).agg(MergeAdjacentUsages(F.collect_list(struct('startTime','endTime'))).alias("Times"))

display(df1)

希望这有帮助

from pyspark.sql.types import *
from pyspark.sql.functions import udf
from pyspark.sql import functions as F
# from pyspark.sql.functions import *

d = [{'ID': '1', 'pID': 1000, 'startTime': '2018.07.02T03:34:20', 'endTime': '2018.07.03T02:40:20'},
     {'ID': '1', 'pID': 1000, 'startTime': '2018.07.02T03:45:20', 'endTime': '2018.07.03T02:50:20'},
     {'ID': '2', 'pID': 2000, 'startTime': '2018.07.02T03:34:20', 'endTime': '2018.07.03T02:40:20'},
     {'ID': '2', 'pID': 2000, 'startTime': '2018.07.02T03:45:20', 'endTime': '2018.07.03T02:50:20'}]

df = spark.createDataFrame(d)

# Dates = namedtuple("Dates", "startTime endTime")

schema = ArrayType(StructType([
    StructField("startTime", StringType(), False),
    StructField("endTime", StringType(), False)
]))


MergeAdjacentUsages = udf(lambda xs: xs, schema)

df1 = df.groupBy(['ID', 'pID']).agg(MergeAdjacentUsages(
    F.collect_list(F.struct('startTime', 'endTime'))).alias("Times"))
df1.show(truncate=False)

+---+----+----------------------------------------------------------------------------------------+
|ID |pID |Times                                                                                   |
+---+----+----------------------------------------------------------------------------------------+
|2  |2000|[[2018.07.02T03:34:20, 2018.07.03T02:40:20], [2018.07.02T03:45:20, 2018.07.03T02:50:20]]|
|1  |1000|[[2018.07.02T03:34:20, 2018.07.03T02:40:20], [2018.07.02T03:45:20, 2018.07.03T02:50:20]]|
+---+----+----------------------------------------------------------------------------------------+