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Posted to user@spark.apache.org by nayan sharma <na...@gmail.com> on 2017/07/16 18:25:17 UTC
splitting columns into new columns
I’ve a Dataframe where in some columns there are multiple values, always separated by ^
phone|contact|
ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5|
phone1|phone2|contact1|contact2|
ERN~5XXXXXXX7|EPN~58XXXX91551~|C~MXXXH~MSO~|CAxxE~~~~~~3XXX5|
How can this be achieved using loop as the separator between column values
are not constant.
data.withColumn("phone",split($"phone","\\^")).select($"phone".getItem(0).as("phone1"),$"phone".getItem(1).as("phone2”))
I though of doing this way but the problem is column are having 100+ separator between the column values
Thank you,
Nayan
Re: splitting columns into new columns
Posted by ayan guha <gu...@gmail.com>.
Hi
Please use explode, which is written to solve exactly your problem.
Consider below:
>>> s = ["ERN~58XXXXXX7~^EPN~5XXXXX551~|1000"]
>>> df = sc.parallelize(s).map(lambda t: t.split('|')).toDF(['phone','id'])
>>> df.registerTempTable("t")
>>> resDF = sqlContext.sql("select id,explode(phone) phones from (select
id, split(phone,'~') as phone from t) x")
>>> resDF.show(truncate=False)
+----+---------+
|id |phones |
+----+---------+
|1000|ERN |
|1000|58XXXXXX7|
|1000|^EPN |
|1000|5XXXXX551|
|1000| |
+----+---------+
HTH.....
On Tue, Jul 18, 2017 at 3:15 AM, nayan sharma <na...@gmail.com>
wrote:
> Hi Pralabh,
>
> Thanks for your help.
>
> val xx = columnList.map(x => x->0).toMap
> val opMap = dataFrame.rdd.flatMap { row =>
> columnList.foldLeft(xx) { case (y, col) =>
> val s = row.getAs[String](col).split("\\^").length
> if (y(col) < s)
> y.updated(col, s)
> else
> y
> }.toList
> }
>
>
> val colMaxSizeMap = opMap.groupBy(x => x._1).map(x => x._2.toList.maxBy(x
> => x._2)).collect().toMap
> val x = dataFrame.rdd.map{x =>
> val op = columnList.flatMap{ y =>
> val op = x.getAs[String](y).split("\\^")
> op++List.fill(colMaxSizeMap(y)-op.size)("")
> }
> Row.fromSeq(op)
> }
>
> val structFieldList = columnList.flatMap{colName =>
> List.range(0,colMaxSizeMap(colName),1).map{ i =>
> StructField(s"$colName"+s"$i",StringType)
> }
> }
> val schema = StructType(structFieldList)
> val data1=spark.createDataFrame(x,schema)
>
> opMap
> res13: org.apache.spark.rdd.RDD[(String, Int)]
>
> But It is failing when opMap has null value.It is throwing java.lang.
> NullPointerException
> trying to figure out.
>
> val opMap1=opMap.filter(_._2 !="")
>
> tried doing this but it is also failing with same exception.
>
> Thanks,
> Nayan
>
>
>
>
> On 17-Jul-2017, at 4:54 PM, Pralabh Kumar <pr...@gmail.com> wrote:
>
> Hi Nayan
>
> Please find the solution of your problem which work on spark 2.
>
> val spark = SparkSession.builder().appName("practice").
> enableHiveSupport().getOrCreate()
> val sc = spark.sparkContext
> val sqlContext = spark.sqlContext
> import spark.implicits._
> val dataFrame = sc.parallelize(List("ERN~58XXXXXX7~^EPN~5XXXXX551~|C~
> MXXX~MSO~^CAxxE~~~~~~3XXX5"))
> .map(s=>s.split("\\|")).map(s=>(s(0),s(1)))
> .toDF("phone","contact")
> dataFrame.show()
> val newDataSet= dataFrame.rdd.map(data=>{
> val t1 = ArrayBuffer[String] ()
> for (i <- 0.to(1)) {
> val col = data.get(i).asInstanceOf[String]
> val dd= col.split("\\^").toSeq
> for(col<-dd){
> t1 +=(col)
> }
> }
> Row.fromSeq(t1.seq)
> })
>
> val firtRow = dataFrame.select("*").take(1)(0)
> dataFrame.schema.fieldNames
> var schema =""
>
> for ((colNames,idx) <- dataFrame.schema.fieldNames.zipWithIndex.view) {
> val data = firtRow(idx).asInstanceOf[String].split("\\^")
> var j = 0
> for(d<-data){
> schema = schema + colNames + j + ","
> j = j+1
> }
> }
> schema=schema.substring(0,schema.length-1)
> val sqlSchema = StructType(schema.split(",").map(s=>StructField(s,
> StringType,false)))
> sqlContext.createDataFrame(newDataSet,sqlSchema).show()
>
> Regards
> Pralabh Kumar
>
>
> On Mon, Jul 17, 2017 at 1:55 PM, nayan sharma <na...@gmail.com>
> wrote:
>
>> If I have 2-3 values in a column then I can easily separate it and create
>> new columns with withColumn option.
>> but I am trying to achieve it in loop and dynamically generate the new
>> columns as many times the ^ has occurred in column values
>>
>> Can it be achieve in this way.
>>
>> On 17-Jul-2017, at 3:29 AM, ayan guha <gu...@gmail.com> wrote:
>>
>> You are looking for explode function.
>>
>> On Mon, 17 Jul 2017 at 4:25 am, nayan sharma <na...@gmail.com>
>> wrote:
>>
>>> I’ve a Dataframe where in some columns there are multiple values, always
>>> separated by ^
>>>
>>> phone|contact|
>>> ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5|
>>>
>>> phone1|phone2|contact1|contact2|
>>> ERN~5XXXXXXX7|EPN~58XXXX91551~|C~MXXXH~MSO~|CAxxE~~~~~~3XXX5|
>>>
>>> How can this be achieved using loop as the separator between column
>>> values
>>> are not constant.
>>> data.withColumn("phone",split($"phone","\\^")).select($"phon
>>> e".getItem(0).as("phone1"),$"phone".getItem(1).as("phone2”))
>>> I though of doing this way but the problem is column are having 100+
>>> separator between the column values
>>>
>>>
>>>
>>> Thank you,
>>> Nayan
>>>
>> --
>> Best Regards,
>> Ayan Guha
>>
>>
>>
>
>
--
Best Regards,
Ayan Guha
Re: splitting columns into new columns
Posted by nayan sharma <na...@gmail.com>.
Hi Pralabh,
Thanks for your help.
val xx = columnList.map(x => x->0).toMap
val opMap = dataFrame.rdd.flatMap { row =>
columnList.foldLeft(xx) { case (y, col) =>
val s = row.getAs[String](col).split("\\^").length
if (y(col) < s)
y.updated(col, s)
else
y
}.toList
}
val colMaxSizeMap = opMap.groupBy(x => x._1).map(x => x._2.toList.maxBy(x => x._2)).collect().toMap
val x = dataFrame.rdd.map{x =>
val op = columnList.flatMap{ y =>
val op = x.getAs[String](y).split("\\^")
op++List.fill(colMaxSizeMap(y)-op.size)("")
}
Row.fromSeq(op)
}
val structFieldList = columnList.flatMap{colName =>
List.range(0,colMaxSizeMap(colName),1).map{ i =>
StructField(s"$colName"+s"$i",StringType)
}
}
val schema = StructType(structFieldList)
val data1=spark.createDataFrame(x,schema)
opMap
res13: org.apache.spark.rdd.RDD[(String, Int)]
But It is failing when opMap has null value.It is throwing java.lang.NullPointerException
trying to figure out.
val opMap1=opMap.filter(_._2 !="")
tried doing this but it is also failing with same exception.
Thanks,
Nayan
> On 17-Jul-2017, at 4:54 PM, Pralabh Kumar <pr...@gmail.com> wrote:
>
> Hi Nayan
>
> Please find the solution of your problem which work on spark 2.
>
> val spark = SparkSession.builder().appName("practice").enableHiveSupport().getOrCreate()
> val sc = spark.sparkContext
> val sqlContext = spark.sqlContext
> import spark.implicits._
> val dataFrame = sc.parallelize(List("ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5"))
> .map(s=>s.split("\\|")).map(s=>(s(0),s(1)))
> .toDF("phone","contact")
> dataFrame.show()
> val newDataSet= dataFrame.rdd.map(data=>{
> val t1 = ArrayBuffer[String] ()
> for (i <- 0.to <http://0.to/>(1)) {
> val col = data.get(i).asInstanceOf[String]
> val dd= col.split("\\^").toSeq
> for(col<-dd){
> t1 +=(col)
> }
> }
> Row.fromSeq(t1.seq)
> })
>
> val firtRow = dataFrame.select("*").take(1)(0)
> dataFrame.schema.fieldNames
> var schema =""
>
> for ((colNames,idx) <- dataFrame.schema.fieldNames.zipWithIndex.view) {
> val data = firtRow(idx).asInstanceOf[String].split("\\^")
> var j = 0
> for(d<-data){
> schema = schema + colNames + j + ","
> j = j+1
> }
> }
> schema=schema.substring(0,schema.length-1)
> val sqlSchema = StructType(schema.split(",").map(s=>StructField(s,StringType,false)))
> sqlContext.createDataFrame(newDataSet,sqlSchema).show()
>
> Regards
> Pralabh Kumar
>
>
> On Mon, Jul 17, 2017 at 1:55 PM, nayan sharma <nayansharma13@gmail.com <ma...@gmail.com>> wrote:
> If I have 2-3 values in a column then I can easily separate it and create new columns with withColumn option.
> but I am trying to achieve it in loop and dynamically generate the new columns as many times the ^ has occurred in column values
>
> Can it be achieve in this way.
>
>> On 17-Jul-2017, at 3:29 AM, ayan guha <guha.ayan@gmail.com <ma...@gmail.com>> wrote:
>>
>> You are looking for explode function.
>>
>> On Mon, 17 Jul 2017 at 4:25 am, nayan sharma <nayansharma13@gmail.com <ma...@gmail.com>> wrote:
>> I’ve a Dataframe where in some columns there are multiple values, always separated by ^
>>
>> phone|contact|
>> ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5|
>>
>> phone1|phone2|contact1|contact2|
>> ERN~5XXXXXXX7|EPN~58XXXX91551~|C~MXXXH~MSO~|CAxxE~~~~~~3XXX5|
>> How can this be achieved using loop as the separator between column values
>> are not constant.
>>
>> data.withColumn("phone",split($"phone","\\^")).select($"phone".getItem(0).as("phone1"),$"phone".getItem(1).as("phone2”))
>> I though of doing this way but the problem is column are having 100+ separator between the column values
>>
>>
>>
>> Thank you,
>> Nayan
>> --
>> Best Regards,
>> Ayan Guha
>
>
Re: splitting columns into new columns
Posted by Pralabh Kumar <pr...@gmail.com>.
Hi Nayan
Please find the solution of your problem which work on spark 2.
val spark =
SparkSession.builder().appName("practice").enableHiveSupport().getOrCreate()
val sc = spark.sparkContext
val sqlContext = spark.sqlContext
import spark.implicits._
val dataFrame =
sc.parallelize(List("ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5"))
.map(s=>s.split("\\|")).map(s=>(s(0),s(1)))
.toDF("phone","contact")
dataFrame.show()
val newDataSet= dataFrame.rdd.map(data=>{
val t1 = ArrayBuffer[String] ()
for (i <- 0.to(1)) {
val col = data.get(i).asInstanceOf[String]
val dd= col.split("\\^").toSeq
for(col<-dd){
t1 +=(col)
}
}
Row.fromSeq(t1.seq)
})
val firtRow = dataFrame.select("*").take(1)(0)
dataFrame.schema.fieldNames
var schema =""
for ((colNames,idx) <- dataFrame.schema.fieldNames.zipWithIndex.view) {
val data = firtRow(idx).asInstanceOf[String].split("\\^")
var j = 0
for(d<-data){
schema = schema + colNames + j + ","
j = j+1
}
}
schema=schema.substring(0,schema.length-1)
val sqlSchema =
StructType(schema.split(",").map(s=>StructField(s,StringType,false)))
sqlContext.createDataFrame(newDataSet,sqlSchema).show()
Regards
Pralabh Kumar
On Mon, Jul 17, 2017 at 1:55 PM, nayan sharma <na...@gmail.com>
wrote:
> If I have 2-3 values in a column then I can easily separate it and create
> new columns with withColumn option.
> but I am trying to achieve it in loop and dynamically generate the new
> columns as many times the ^ has occurred in column values
>
> Can it be achieve in this way.
>
> On 17-Jul-2017, at 3:29 AM, ayan guha <gu...@gmail.com> wrote:
>
> You are looking for explode function.
>
> On Mon, 17 Jul 2017 at 4:25 am, nayan sharma <na...@gmail.com>
> wrote:
>
>> I’ve a Dataframe where in some columns there are multiple values, always
>> separated by ^
>>
>> phone|contact|
>> ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5|
>>
>> phone1|phone2|contact1|contact2|
>> ERN~5XXXXXXX7|EPN~58XXXX91551~|C~MXXXH~MSO~|CAxxE~~~~~~3XXX5|
>>
>> How can this be achieved using loop as the separator between column values
>> are not constant.
>> data.withColumn("phone",split($"phone","\\^")).select($"
>> phone".getItem(0).as("phone1"),$"phone".getItem(1).as("phone2”))
>> I though of doing this way but the problem is column are having 100+
>> separator between the column values
>>
>>
>>
>> Thank you,
>> Nayan
>>
> --
> Best Regards,
> Ayan Guha
>
>
>
Re: splitting columns into new columns
Posted by nayan sharma <na...@gmail.com>.
If I have 2-3 values in a column then I can easily separate it and create new columns with withColumn option.
but I am trying to achieve it in loop and dynamically generate the new columns as many times the ^ has occurred in column values
Can it be achieve in this way.
> On 17-Jul-2017, at 3:29 AM, ayan guha <gu...@gmail.com> wrote:
>
> You are looking for explode function.
>
> On Mon, 17 Jul 2017 at 4:25 am, nayan sharma <nayansharma13@gmail.com <ma...@gmail.com>> wrote:
> I’ve a Dataframe where in some columns there are multiple values, always separated by ^
>
> phone|contact|
> ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5|
>
> phone1|phone2|contact1|contact2|
> ERN~5XXXXXXX7|EPN~58XXXX91551~|C~MXXXH~MSO~|CAxxE~~~~~~3XXX5|
> How can this be achieved using loop as the separator between column values
> are not constant.
>
> data.withColumn("phone",split($"phone","\\^")).select($"phone".getItem(0).as("phone1"),$"phone".getItem(1).as("phone2”))
> I though of doing this way but the problem is column are having 100+ separator between the column values
>
>
>
> Thank you,
> Nayan
> --
> Best Regards,
> Ayan Guha
Re: splitting columns into new columns
Posted by ayan guha <gu...@gmail.com>.
You are looking for explode function.
On Mon, 17 Jul 2017 at 4:25 am, nayan sharma <na...@gmail.com>
wrote:
> I’ve a Dataframe where in some columns there are multiple values, always
> separated by ^
>
> phone|contact|
> ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5|
>
> phone1|phone2|contact1|contact2|
> ERN~5XXXXXXX7|EPN~58XXXX91551~|C~MXXXH~MSO~|CAxxE~~~~~~3XXX5|
>
> How can this be achieved using loop as the separator between column values
> are not constant.
>
> data.withColumn("phone",split($"phone","\\^")).select($"phone".getItem(0).as("phone1"),$"phone".getItem(1).as("phone2”))
> I though of doing this way but the problem is column are having 100+
> separator between the column values
>
>
>
> Thank you,
> Nayan
>
--
Best Regards,
Ayan Guha