Concatenate columns in apache spark dataframe

Question

I need to concatenate two columns in a dataframe. Is there any function in spark sql to do the same?

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Answer 1

You can use the following set of codes for scala

import org.apache.spark.sql.functions.{concat, lit}

df.select(concat($"k", lit(" "), $"v"))

For Python

from pyspark.sql.functions import concat, col, lit

df.select(concat(col("k"), lit(" "), col("v")))

Answer 2

You can use CONCAT with SQL:

You can use following code for scala

import sqlContext.implicits._

val df = sc.parallelize(Seq(("scala", 1), ("implementation", 2))).toDF("k", "v")
df.registerTempTable("df")
sqlContext.sql("SELECT CONCAT(k, ' ',  v) FROM df")

In case of Python

df = sqlContext.createDataFrame([("python", 1), ("implementation", 2)], ("k", "v"))
df.registerTempTable("df")
sqlContext.sql("SELECT CONCAT(k, ' ',  v) FROM df")

Answer 3

val sqlContext = new SQLContext(sc)
case class MyDf(col1: String, col2: String)

//here is our dataframe
val df = sqlContext.createDataFrame(sc.parallelize(
    Array(MyDf("A", "B"), MyDf("C", "D"), MyDf("E", "F"))
))

//Define a udf to concatenate two passed in string values
val getConcatenated = udf( (first: String, second: String) => { first + " " + second } )

//use withColumn method to add a new column called newColName
df.withColumn("newColName", getConcatenated($"col1", $"col2")).select("newColName", "col1", "col2").show()

Answer 4

This code is helpful if you don't know the number or name of columns:

val dfResults = dfSource.select(concat_ws(",",dfSource.columns.map(c => col(c)): _*))

Comments

If my colum names are stored in list say col_list and I want to concatenate them with space between each column value  .... In pyspark Dataframe .. any idea ?how to do this

---

I think you can use one loop and fetch one by one from your list and add space.

Answer 5

Use the following code:

import pyspark
from pyspark.sql import functions as sf
sc = pyspark.SparkContext()
sqlc = pyspark.SQLContext(sc)
df = sqlc.createDataFrame([('row11','row12'), ('row21','row22')], ['colname1', 'colname2'])
df.show()

gives,

+--------+--------+
|colname1|colname2|
+--------+--------+
|   row11|   row12|
|   row21|   row22|
+--------+--------+

create new column by concatenating:

df = df.withColumn('joined_column', 
                    sf.concat(sf.col('colname1'),sf.lit('_'), sf.col('colname2')))
df.show()

+--------+--------+-------------+
|colname1|colname2|joined_column|
+--------+--------+-------------+
|   row11|   row12|  row11_row12|
|   row21|   row22|  row21_row22|
+--------+--------+-------------+

Answer 6

In Spark 2.3.0, you may do:

spark.sql( """ select '1' || column_a from table_a """)

Answer 7

Try this:

​spark.sql( """ select '1' || column_a from table_a """)

Answer 8

You can do it in pyspark using sqlContext..

Comments

Can you explain how?

---

Something like this:

#Suppose we have a dataframe:
df = sqlContext.createDataFrame([('row1_1','row1_2')], ['colname1', 'colname2'])

# Now we can concatenate columns and assign the new column a name
df = df.select(concat(df.colname1, df.colname2).alias('joined_colname'))

---

Yess I agree with @Ali, have a look at it @Kalgi.

Answer 9

You can use the function concat with select. something like this:

val newDf = df.selectExpr("concat(nvl(COL1, ''), nvl(COL2, '')) as NEW_COLUMN")

Answer 10

Using concat and withColumn:

val Df =
  df.withColumn(
    "NEW_COLUMN",
    concat(
      when(col("COL1").isNotNull, col("COL1")).otherwise(lit("null")),
      when(col("COL2").isNotNull, col("COL2")).otherwise(lit("null"))))

Answer 11

its late but this how you can achieve:

if you want to add some delim  then df.withColumn("crimes",concat($"E_CATEGORY",lit("|"),$"E_C_TYPE",lit("|"),$"E_SUB_TYPE"))

otherwise

or df.withColumn("crimes",concat($"E_CATEGORY",$"E_C_TYPE",$"E_SUB_TYPE"))