just use coalesce function
df.selectExpr("name", "coalesce(age, 0) as age")
--
Jan Sterba
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On Fri, Feb 26, 2016 at 5:27 AM, Divya Gehlot <divya.htco...@gmail.com>
wrote:
> Hi,
> I have dataset which looks like below
> name age
> alice 35
> bob null
> peter 24
> I need to replace null values of columns with 0
> so I referred Spark API DataframeNAfunctions.scala
> <https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala>
>
> I tried the below code its throwing exception
> scala> import org.apache.spark.sql.types.{StructType, StructField,
> StringType,IntegerType,LongType,DoubleType, FloatType};
> import org.apache.spark.sql.types.{StructType, StructField, StringType,
> IntegerType, LongType, DoubleType, FloatType}
>
> scala> val nulltestSchema = StructType(Seq(StructField("name", StringType,
> false),StructField("age", DoubleType, true)))
> nulltestSchema: org.apache.spark.sql.types.StructType =
> StructType(StructField(name,StringType,false),
> StructField(age,DoubleType,true))
>
> scala> val dfnulltest =
> sqlContext.read.format("com.databricks.spark.csv").option("header",
> "true").schema(nulltestSchema).load("hdfs://
> 172.31.29.201:8020/TestDivya/Spark/nulltest.csv")
> dfnulltest: org.apache.spark.sql.DataFrame = [name: string, age: double]
>
> scala> val dfchangenull =
> dfnulltest.na.fill(0,Seq("age")).select("name","age")
> dfchangenull: org.apache.spark.sql.DataFrame = [name: string, age: double]
>
> scala> dfchangenull.show
> 16/02/25 23:15:59 WARN TaskSetManager: Lost task 0.0 in stage 2.0 (TID 2,
> ip-172-31-22-135.ap-southeast-1.compute.internal):
> java.text.ParseException: Unparseable number: "null"
> at java.text.NumberFormat.parse(NumberFormat.java:350)
>
>
