NORM PPF function pyspark

Question

Code Used:

from pyspark.sql.types import FloatType
from scipy import stats 
from scipy.stats import norm
mylist = [0.083, 0.219, 0.126] 
df = spark.createDataFrame(mylist, FloatType())
df.show() 
norm_ppf = F.udf(lambda x: float(norm.ppf(x)))
df.withColumn("var2", norm_ppf(df['value'])).show()

Module scipy is already installed on the system. How do we resolve this issue? Is there any way I can apply norm.ppf function on a column in pyspark

Error Message:

/var/opt/teradata/cloudera/parcels/CDH/lib/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical) 376 """ 377 if isinstance(truncate, bool) and truncate: --> 378
print(self._jdf.showString(n, 20, vertical)) 379 else: 380 print(self._jdf.showString(n, int(truncate), vertical))
/usr/local/lib/python3.6/site-packages/py4j/java_gateway.py in call(self, *args) 1255 answer = self.gateway_client.send_command(command) 1256
return_value = get_return_value( -> 1257 answer, self.gateway_client, self.target_id, self.name) 1258 1259 for temp_arg in temp_args:
/var/opt/teradata/cloudera/parcels/CDH/lib/spark/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()
/usr/local/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name) 326
raise Py4JJavaError( 327 "An error occurred while calling {0}{1}{2}. ". --> 328
format(target_id, ".", name), value) 329 else:
330 raise Py4JError( Py4JJavaError: An error occurred while calling o2145.showString. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 19.0 failed 4 times, most recent failure: Lost task 0.3 in stage 19.0 (TID 82, d8-td-cdh.boigroup.net, executor 38): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "/var/opt/teradata/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 361, in main func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type) File "/var/opt/teradata/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 236, in read_udfs arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type, runner_conf) File "/var/opt/teradata/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 163, in read_single_udf f, return_type = read_command(pickleSer, infile) File "/var/opt/teradata/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 64, in read_command command = serializer._read_with_length(file) File "/var/opt/teradata/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 172, in _read_with_length return self.loads(obj) File "/var/opt/teradata/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 577, in loads return pickle.loads(obj, encoding=encoding) ModuleNotFoundError: No module named 'scipy' at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452) at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81) at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:624) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$11.apply(Executor.scala:407) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1363) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:413) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1890) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1877) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:929) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2111) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2060) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2049) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:740) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2081)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2102)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2121)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3383) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544) at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364) at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363) at org.apache.spark.sql.Dataset.head(Dataset.scala:2544) at org.apache.spark.sql.Dataset.take(Dataset.scala:2758) at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254) at org.apache.spark.sql.Dataset.showString(Dataset.scala:291) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) norm_cdf = F.udf(lambda x: float(norm.cdf(x))) df.withColumn("var2", norm_cdf(df['value'])).show()

NORM PPF function pyspark

Answers (1)

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