How to split a column by using length split and MaxSplit in Pyspark dataframe? - apache-spark-sql

Question

For Example
If I have a Column as given below by calling and showing the CSV in Pyspark
+--------+
| Names|
+--------+
|Rahul |
|Ravi |
|Raghu |
|Romeo |
+--------+
if I specify in my functions as Such
Length = 2
Maxsplit = 3
Then I have to get the results as
+----------+-----------+----------+
|Col_1 |Col_2 |Col_3 |
+----------+-----------+----------+
| Ra | hu | l |
| Ra | vi | Null |
| Ra | gh | u |
| Ro | me | o |
+----------+-----------+----------+
Simirarly in Pyspark
Length = 3
Max split = 2 it should provide me the output such as
+----------+-----------+
|Col_1 |Col_2 |
+----------+-----------+
| Rah | ul |
| Rav | i |
| Rag | hu |
| Rom | eo |
+----------+-----------+
This is how it should look like, Thank you

Answer 1

Another way to go about this. Should be faster than any looping or udf solution.
from pyspark.sql import functions as F
def split(df,length,maxsplit):
return df.withColumn('Names',F.split("Names","(?<=\\G{})".format('.'*length)))\
.select(*((F.col("Names")[x]).alias("Col_"+str(x+1)) for x in range(0,maxsplit)))
split(df,3,2).show()
#+-----+-----+
#|Col_1|Col_2|
#+-----+-----+
#| Rah| ul|
#| Rav| i|
#| Rag| hu|
#| Rom| eo|
#+-----+-----+
split(df,2,3).show()
#+-----+-----+-----+
#|col_1|col_2|col_3|
#+-----+-----+-----+
#| Ra| hu| l|
#| Ra| vi| |
#| Ra| gh| u|
#| Ro| me| o|
#+-----+-----+-----+

Answer 2

Try this,
import pyspark.sql.functions as F
tst = sqlContext.createDataFrame([("Raghu",1),("Ravi",2),("Rahul",3)],schema=["Name","val"])
def fn (split,max_n,tst):
for i in range(max_n):
tst_loop=tst.withColumn("coln"+str(i),F.substring(F.col("Name"),(i*split)+1,split))
tst=tst_loop
return(tst)
tst_res = fn(3,2,tst)
The for loop can also replaced by a list comprehension or reduce, but i felt in you case, a for loop looked neater. they have the same physical plan anyway.
The results
+-----+---+-----+-----+
| Name|val|coln0|coln1|
+-----+---+-----+-----+
|Raghu| 1| Rag| hu|
| Ravi| 2| Rav| i|
|Rahul| 3| Rah| ul|
+-----+---+-----+-----+

Answer 3

Try this
def split(data,length,maxSplit):
start=1
for i in range(0,maxSplit):
data = data.withColumn(f'col_{start}-{start+length-1}',f.substring('channel',start,length))
start=length+1
return data
df = split(data,3,2)
df.show()
+--------+----+-------+-------+
| channel|type|col_1-3|col_4-6|
+--------+----+-------+-------+
| web| 0| web| |
| web| 1| web| |
| web| 2| web| |
| twitter| 0| twi| tte|
| twitter| 1| twi| tte|
|facebook| 0| fac| ebo|
|facebook| 1| fac| ebo|
|facebook| 2| fac| ebo|
+--------+----+-------+-------+

Answer 4

Perhaps this is useful-
Load the test data
Note: written in scala
val Length = 2
val Maxsplit = 3
val df = Seq("Rahul", "Ravi", "Raghu", "Romeo").toDF("Names")
df.show(false)
/**
* +-----+
* |Names|
* +-----+
* |Rahul|
* |Ravi |
* |Raghu|
* |Romeo|
* +-----+
*/
split the string col as per the length and offset
val schema = StructType(Range(1, Maxsplit + 1).map(f => StructField(s"Col_$f", StringType)))
val split = udf((str:String, length: Int, maxSplit: Int) =>{
val splits = str.toCharArray.grouped(length).map(_.mkString).toArray
RowFactory.create(splits ++ Array.fill(maxSplit-splits.length)(null): _*)
}, schema)
val p = df
.withColumn("x", split($"Names", lit(Length), lit(Maxsplit)))
.selectExpr("x.*")
p.show(false)
p.printSchema()
/**
* +-----+-----+-----+
* |Col_1|Col_2|Col_3|
* +-----+-----+-----+
* |Ra |hu |l |
* |Ra |vi |null |
* |Ra |gh |u |
* |Ro |me |o |
* +-----+-----+-----+
*
* root
* |-- Col_1: string (nullable = true)
* |-- Col_2: string (nullable = true)
* |-- Col_3: string (nullable = true)
*/
Dataset[Row] -> Dataset[Array[String]]
val x = df.map(r => {
val splits = r.getString(0).toCharArray.grouped(Length).map(_.mkString).toArray
splits ++ Array.fill(Maxsplit-splits.length)(null)
})
x.show(false)
x.printSchema()
/**
* +-----------+
* |value |
* +-----------+
* |[Ra, hu, l]|
* |[Ra, vi,] |
* |[Ra, gh, u]|
* |[Ro, me, o]|
* +-----------+
*
* root
* |-- value: array (nullable = true)
* | |-- element: string (containsNull = true)
*/