如何转换来自RDD的熊猫数据帧。将分区()映射到Spark DataFrame?

我有一个Python函数,它返回熊猫数据帧。我在Spark 2.2.0中使用pyspark调用此函数 RDD.mapPartitions() . 但我无法转换返回的RDD mapPartitions() 进入Spark数据框。Pandas生成此错误:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

说明问题的简单代码:

import pandas as pd

def func(data):
    pdf = pd.DataFrame(list(data), columns=("A", "B", "C"))
    pdf += 10 # Add 10 to every value. The real function is a lot more complex!
    return [pdf]

pdf = pd.DataFrame([(1.87, 0.6, 7.1), (-0.3, 0.1, 8.2), (2.8, 0.3, 6.1), (-0.2, 0.5, 5.9)], columns=("A", "B", "C"))

sdf = spark.createDataFrame(pdf)
sdf.show()
rddIn = sdf.rdd

for i in rddIn.collect():
    print(i)

result = rddIn.mapPartitions(func)

for i in result.collect():
    print(i)

resDf = spark.createDataFrame(result) # --> ValueError!
resDf.show()

输出为:

+----+---+---+
|   A|  B|  C|
+----+---+---+
|1.87|0.6|7.1|
|-0.3|0.1|8.2|
| 2.8|0.3|6.1|
|-0.2|0.5|5.9|
+----+---+---+
Row(A=1.87, B=0.6, C=7.1)
Row(A=-0.3, B=0.1, C=8.2)
Row(A=2.8, B=0.3, C=6.1)
Row(A=-0.2, B=0.5, C=5.9)
       A     B     C
0  11.87  10.6  17.1
     A     B     C
0  9.7  10.1  18.2
      A     B     C
0  12.8  10.3  16.1
     A     B     C
0  9.8  10.5  15.9

但倒数第二行生成 ValueError 如上所述。我真的很想 resDf.show() 看起来完全一样 sdf.show() 表中每个值加10除外。理想情况下 result RDD的结构应与 rddIn ,RDD将进入 mapPartitions() .