You can try with UDF, like the following code snippet:
from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, StringType
df = spark.read.text("./README.md")
split_func = udf(lambda text: text.split(" "), ArrayType(StringType()))
df.withColumn("split_value", split_func("value")).show()
Thanks
Yanbo
On Tue, Apr 25, 2017 at 12:27 AM, Selvam Raman <sel...@gmail.com> wrote:
> documentDF = spark.createDataFrame([
>
> ("Hi I heard about Spark".split(" "), ),
>
> ("I wish Java could use case classes".split(" "), ),
>
> ("Logistic regression models are neat".split(" "), )
>
> ], ["text"])
>
>
> How can i achieve the same df while i am reading from source?
>
> doc = spark.read.text("/Users/rs/Desktop/nohup.out")
>
> how can i create array<string> type with "sentences" column from
> doc(dataframe)
>
>
> The below one creates more than one column.
>
> rdd.map(lambda rdd: rdd[0]).map(lambda row:row.split(" "))
>
> --
> Selvam Raman
> "லஞ்சம் தவிர்த்து நெஞ்சம் நிமிர்த்து"
>
