How about using `withColumn` and UDF?
example:
+ https://gist.github.com/zoltanctoth/2deccd69e3d1cde1dd78
<https://gist.github.com/zoltanctoth/2deccd69e3d1cde1dd78>
+ https://ragrawal.wordpress.com/2015/10/02/spark-custom-udf-example/
On Mon, Apr 17, 2017 at 8:25 PM, Zeming Yu <zemin...@gmail.com> wrote:
> I've got a dataframe with a column looking like this:
>
> display(flight.select("duration").show())
>
> +--------+
> |duration|
> +--------+
> | 15h10m|
> | 17h0m|
> | 21h25m|
> | 14h30m|
> | 24h50m|
> | 26h10m|
> | 14h30m|
> | 23h5m|
> | 21h30m|
> | 11h50m|
> | 16h10m|
> | 15h15m|
> | 21h25m|
> | 14h25m|
> | 14h40m|
> | 16h0m|
> | 24h20m|
> | 14h30m|
> | 14h25m|
> | 14h30m|
> +--------+
> only showing top 20 rows
>
>
>
> I need to extract the hour as a number and store it as an additional
> column within the same dataframe. What's the best way to do that?
>
>
> I tried the following, but it failed:
>
> import re
> def getHours(x):
> return re.match('([0-9]+(?=h))', x)
> temp = flight.select("duration").rdd.map(lambda x:getHours(x[0])).toDF()
> temp.select("duration").show()
>
>
> error message:
>
>
> ---------------------------------------------------------------------------Py4JJavaError
> Traceback (most recent call
> last)<ipython-input-89-1d5bec255302> in <module>() 2 def getHours(x):
> 3 return re.match('([0-9]+(?=h))', x)----> 4 temp =
> flight.select("duration").rdd.map(lambda x:getHours(x[0])).toDF() 5
> temp.select("duration").show()
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py
> in toDF(self, schema, sampleRatio) 55 [Row(name=u'Alice',
> age=1)] 56 """---> 57 return
> sparkSession.createDataFrame(self, schema, sampleRatio) 58 59
> RDD.toDF = toDF
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py
> in createDataFrame(self, data, schema, samplingRatio, verifySchema) 518
> 519 if isinstance(data, RDD):--> 520 rdd, schema =
> self._createFromRDD(data.map(prepare), schema, samplingRatio) 521
> else: 522 rdd, schema = self._createFromLocal(map(prepare,
> data), schema)
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py
> in _createFromRDD(self, rdd, schema, samplingRatio) 358 """
> 359 if schema is None or isinstance(schema, (list, tuple)):--> 360
> struct = self._inferSchema(rdd, samplingRatio) 361
> converter = _create_converter(struct) 362 rdd =
> rdd.map(converter)
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py
> in _inferSchema(self, rdd, samplingRatio) 329 :return:
> :class:`pyspark.sql.types.StructType` 330 """--> 331 first
> = rdd.first() 332 if not first: 333 raise
> ValueError("The first row in RDD is empty, "
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\rdd.py
> in first(self) 1359 ValueError: RDD is empty 1360 """->
> 1361 rs = self.take(1) 1362 if rs: 1363
> return rs[0]
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\rdd.py
> in take(self, num) 1341 1342 p = range(partsScanned,
> min(partsScanned + numPartsToTry, totalParts))-> 1343 res =
> self.context.runJob(self, takeUpToNumLeft, p) 1344 1345
> items += res
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\context.py
> in runJob(self, rdd, partitionFunc, partitions, allowLocal) 963 #
> SparkContext#runJob. 964 mappedRDD =
> rdd.mapPartitions(partitionFunc)--> 965 port =
> self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
> 966 return list(_load_from_socket(port,
> mappedRDD._jrdd_deserializer)) 967
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\java_gateway.py
> in __call__(self, *args) 1131 answer =
> self.gateway_client.send_command(command) 1132 return_value =
> get_return_value(-> 1133 answer, self.gateway_client,
> self.target_id, self.name) 1134 1135 for temp_arg in temp_args:
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\utils.py
> in deco(*a, **kw) 61 def deco(*a, **kw): 62 try:---> 63
> return f(*a, **kw) 64 except
> py4j.protocol.Py4JJavaError as e: 65 s =
> e.java_exception.toString()
> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\protocol.py
> in get_return_value(answer, gateway_client, target_id, name) 317
> raise Py4JJavaError( 318 "An error occurred
> while calling {0}{1}{2}.\n".--> 319 format(target_id,
> ".", name), value) 320 else: 321 raise
> Py4JError(
> Py4JJavaError: An error occurred while calling
> z:org.apache.spark.api.python.PythonRDD.runJob.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 75.0 failed 1 times, most recent failure: Lost task 0.0 in stage
> 75.0 (TID 1035, localhost, executor driver):
> org.apache.spark.api.python.PythonException: Traceback (most recent call
> last):
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
> line 174, in main
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
> line 169, in process
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py",
> line 272, in dump_stream
> bytes = self.serializer.dumps(vs)
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py",
> line 427, in dumps
> return pickle.dumps(obj, protocol)
> _pickle.PicklingError: Can't pickle <class '_sre.SRE_Match'>: attribute
> lookup SRE_Match on _sre failed
>
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
> at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
> at java.lang.Thread.run(Unknown Source)
>
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
> at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:441)
> at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
> at java.lang.reflect.Method.invoke(Unknown Source)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:280)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Unknown Source)
> Caused by: org.apache.spark.api.python.PythonException: Traceback (most
> recent call last):
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
> line 174, in main
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
> line 169, in process
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py",
> line 272, in dump_stream
> bytes = self.serializer.dumps(vs)
> File
> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py",
> line 427, in dumps
> return pickle.dumps(obj, protocol)
> _pickle.PicklingError: Can't pickle <class '_sre.SRE_Match'>: attribute
> lookup SRE_Match on _sre failed
>
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
> at
> org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
> at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
> ... 1 more
>
>
>
>
>
>
