Can be as simple as -
from pyspark.sql.functions import split
flight.withColumn('hour',split(flight.duration,'h').getItem(0))
Thank you,
*Pushkar Gujar*
On Thu, Apr 20, 2017 at 4:35 AM, Zeming Yu <zemin...@gmail.com> wrote:
> Any examples?
>
> On 20 Apr. 2017 3:44 pm, "颜发才(Yan Facai)" <facai....@gmail.com> wrote:
>
>> 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
>>>
>>>
>>>
>>>
>>>
>>>
>>
