Python 为什么pyspark中数据帧上的withColumn会弄乱数据帧

我有一个具有以下模式的数据帧：

last_year_df.printSchema()
root
|-- invNum: string (nullable = true)
|-- custNum: string (nullable = true)
|-- entprsNum: string (nullable = true)
|-- billTp: string (nullable = true)
|-- invAmtUSD: string (nullable = true)
|-- invRevenueTp: string (nullable = true)
|-- entryDt: string (nullable = true)
|-- dueDt: string (nullable = true)
|-- settledDt: string (nullable = true)
|-- days_to_settle: integer (nullable = true)

我可以在字段上显示（）和在天上描述（）来解决问题。 我想通过以下函数调整天数以结算

def days_adjust(days):
    if days < 0:
        ret_days = 0
    elif days > 120:
        ret_days = 120
    else:
        ret_days = days
    return ret_days

adjust_udf = udf(days_adjust, IntegerType())
last_year_df = last_year_df.withColumn("days_to_settle_adjusted",adjust_udf(last_year_df['days_to_settle']))
#last_year_df = last_year_df.withColumn("days_to_settle_adjusted", last_year_df['days_to_settle'] + 100)

last_year_df.select("settledDt").show()

def days\u adjust（天数）：
如果天数<0：
回收天数=0
elif天数>120天：
返修天数=120
其他：
归还天数=天
返回返回返回天数
adjust\u udf=udf（天数\u adjust，IntegerType（））
上一年(df)=上一年(df),带列(结算天数),调整(上一年(df"结算天数))
#上一年(df)=上一年(df),带列(调整),上一年(df)[结算(df)天数]+100)
上一年选择（“已结算”）.show（）

在带有udf的withColumn之后，我在数据帧上尝试的任何操作都会给出一个错误。如果我在不使用udf的情况下使用commented withColumn，那么数据帧在之后就可以了。以下是错误：

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-23-900db0b86899> in <module>()
 15 #last_year_df = last_year_df.withColumn("days_to_settle_adjusted", last_year_df['days_to_settle'] + 100)
 16 
 --> 17 last_year_df.select("settledDt").show()
 18 last_year_df.select("dueDt").show()
 19 last_year_df.select("days_to_settle").show()

 /usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark   /sql/dataframe.pyc in show(self, n, truncate)
255         +---+-----+
256         """
--> 257         print(self._jdf.showString(n, truncate))
258 
259     def __repr__(self):

/usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
811         answer = self.gateway_client.send_command(command)
812         return_value = get_return_value(
--> 813             answer, self.gateway_client, self.target_id, self.name)
814 
815         for temp_arg in temp_args:

/usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/utils.pyc in deco(*a, **kw)
 43     def deco(*a, **kw):
 44         try:
 ---> 45             return f(*a, **kw)
 46         except py4j.protocol.Py4JJavaError as e:
 47             s = e.java_exception.toString()

 /usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
306                 raise Py4JJavaError(
307                     "An error occurred while calling {0}{1}{2}.\n".
--> 308                     format(target_id, ".", name), value)
309             else:
310                 raise Py4JError(

Py4JJavaError: An error occurred while calling o317.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 54.0 failed 1 times, most recent failure: Lost task 0.0 in stage 54.0 (TID 806, localhost): java.lang.ArrayIndexOutOfBoundsException
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:260)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
at org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$2.apply(ExistingRDD.scala:59)
at org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$2.apply(ExistingRDD.scala:56)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:966)
at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:972)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:452)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:280)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:239)

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at java.lang.Thread.getStackTrace(Thread.java:1117)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212)
at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)
at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086)
at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1498)
at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1505)
at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375)
at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1374)
at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099)
at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1374)
at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1456)
at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:170)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:95)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55)
at java.lang.reflect.Method.invoke(Method.java:507)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:785)
Caused by: java.lang.ArrayIndexOutOfBoundsException
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:260)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
at org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$2.apply(ExistingRDD.scala:59)
at org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$2.apply(ExistingRDD.scala:56)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:966)
at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:972)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:452)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:280)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:239)

Py4JJavaError回溯（最近一次调用）
在（）
15#上一年(df=上一年)df.带列（“结算天数)调整”，上一年(df[“结算天数”]+100）
16
-->17上一年度选择（“已结算”）显示（）
18上一年度的选择（“dueDt”）.show（）
19上一年日期选择（“结算天数”）。显示（）
/show中的usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/dataframe.pyc（self，n，truncate）
255         +---+-----+
256         """
-->257打印（self.\u jdf.showString（n，截断））
258
259定义报告（自我）：
/usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/java_-gateway.py in __调用（self，*args）
811 answer=self.gateway\u client.send\u命令（command）
812返回值=获取返回值(
-->813应答，self.gateway\u客户端，self.target\u id，self.name）
814
815对于临时参数中的临时参数：
/deco中的usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/utils.pyc（*a，**kw）
43 def装饰（*a，**千瓦）：
44尝试：
--->45返回f（*a，**kw）
46除py4j.protocol.Py4JJavaError外，错误为e:
47 s=e.java_exception.toString（）
/获取返回值中的usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/protocol.py（答案、网关客户端、目标id、名称）
306 raise PY4JJAVA错误(
307“调用{0}{1}{2}时出错。\n”。
-->308格式（目标id，“.”，名称），值）
309其他：
310升起Py4JError(
Py4JJavaError:调用o317.showString时出错。
：org.apache.spark.SparkException:作业因阶段失败而中止：阶段54.0中的任务0失败1次，最近的失败：阶段54.0中的任务0.0丢失（TID 806，本地主机）：java.lang.ArrayIndexOutOfBoundsException
在org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl（CatalystTypeConverters.scala:260）上
在org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl（CatalystTypeConverters.scala:250）上
位于org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst（CatalystTypeConverters.scala:102）
在org.apache.spark.sql.CatalystTypeConverters$$anonfun$createToCatalystConverters$2.apply上（CatalystTypeConverters.scala:401）
位于org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$2.apply（ExistingRDD.scala:59）
位于org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$2.apply（ExistingRDD.scala:56）
在scala.collection.Iterator$$anon$11.next（Iterator.scala:328）
在scala.collection.Iterator$$anon$14.hasNext（Iterator.scala:389）
在scala.collection.Iterator$$anon$11.hasNext（Iterator.scala:327）
在scala.collection.Iterator$$anon$11.hasNext（Iterator.scala:327）
在scala.collection.Iterator$GroupedIterator.fill处（Iterator.scala:966）
位于scala.collection.Iterator$GroupEditor.hasNext（Iterator.scala:972）
在scala.collection.Iterator$$anon$11.hasNext（Iterator.scala:327）
位于scala.collection.Iterator$class.foreach（Iterator.scala:727）
位于scala.collection.AbstractIterator.foreach（迭代器.scala:1157）
位于org.apache.spark.api.PythonRDD$.writeiteiteratortostream（PythonRDD.scala:452）
位于org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply（PythonRDD.scala:280）
位于org.apache.spark.util.Utils$.logUncaughtExceptions（Utils.scala:1765）
位于org.apache.spark.api.python.PythonRunner$WriterThread.run（PythonRDD.scala:239）
驱动程序堆栈跟踪：
位于org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages（DAGScheduler.scala:1431）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply（DAGScheduler.scala:1419）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply（DAGScheduler.scala:1418）
位于scala.collection.mutable.resizeblearray$class.foreach（resizeblearray.scala:59）
位于scala.collection.mutable.ArrayBuffer.foreach（ArrayBuffer.scala:47）
位于org.apache.spark.scheduler.DAGScheduler.abortStage（DAGScheduler.scala:1418）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply（DAGScheduler.scala:799）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply（DAGScheduler.scala:799）
位于scala.Option.foreach（Option.scala:236）
位于org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed（DAGScheduler.scala:799）
位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive（DAGScheduler.scala:1640）
位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive（DAGScheduler.scala:1599）
位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive（DAGScheduler.scala:1588）
位于org.apache.spark.util.EventLoop$$anon$1.run（EventLoop.scala:48）
位于java.lang.Thread.getStackTrace（Thread.java:1117）
位于org.apache.spark.scheduler.DAGScheduler.runJob（DAGScheduler.scala:620）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:1832）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:1845）
在org.apac