Spark dataframe filter

Question

val df = sc.parallelize(Seq((1,\"Emailab\"), (2,\"Phoneab\"), (3, \"Faxab\"),(4,\"Mail\"),(5,\"Other\"),(6,\"MSL12\"),(7,\"MSL\"),(8,\"HCP\"),(9,\"HCP12\"))).toDF(\"c1\"         


        

Answer 1

I used below to filter rows from dataframe and this worked form me.Spark 2.2

val spark = new org.apache.spark.sql.SQLContext(sc)    
val data = spark.read.format("csv").
          option("header", "true").
          option("delimiter", "|").
          option("inferSchema", "true").
          load("D:\\test.csv")   


import  spark.implicits._
val filter=data.filter($"dept" === "IT" )

OR

val filter=data.filter($"dept" =!= "IT" )

Answer 2

This works too. Concise and very similar to SQL.

df.filter("c2 not like 'MSL%' and c2 not like 'HCP%'").show
+---+-------+
| c1|     c2|
+---+-------+
|  1|Emailab|
|  2|Phoneab|
|  3|  Faxab|
|  4|   Mail|
|  5|  Other|
+---+-------+

Answer 3

val df1 = df.filter(not(df("c2").rlike("MSL"))&&not(df("c2").rlike("HCP")))

This worked.

Answer 4

df.filter(not(
    substring(col("c2"), 0, 3).isin("MSL", "HCP"))
    )