Transpose DataFrame single row to column in Spark with scala - scala

Question

I saw this question here:
Transpose DataFrame Without Aggregation in Spark with scala and I wanted to do exactly the opposite.
I have this Dataframe with a single row, with values that are string, int, bool, array:
+-----+-------+-----+------+-----+
|col1 | col2 |col3 | col4 |col5 |
+-----+-------+-----+------+-----+
|val1 | val2 |val3 | val4 |val5 |
+-----+-------+-----+------+-----+
And I want to transpose it like this:
+-----------+-------+
|Columns | values|
+-----------+-------+
|col1 | val1 |
|col2 | val2 |
|col3 | val3 |
|col4 | val4 |
|col5 | val5 |
+-----------+-------+
I am using Apache Spark 2.4.3 with Scala 2.11
Edit: Values can be of any type (int, double, bool, array), not only strings.

Answer 1

Thought differently with out using arrays_zip (which is available in => Spark 2.4)] and got the below...
It will work for Spark =>2.0 onwards in a simpler way (flatmap , map and explode functions)...
Here map function (used in with column) creates a new map column. The input columns must be grouped as key-value pairs.
Case : String data type in Data :
import org.apache.spark.sql.functions._
val df: DataFrame =Seq((("val1"),("val2"),("val3"),("val4"),("val5"))).toDF("col1","col2","col3","col4","col5")
var columnsAndValues = df.columns.flatMap { c => Array(lit(c), col(c)) }
df.printSchema()
df.withColumn("myMap", map(columnsAndValues:_*)).select(explode($"myMap"))
.toDF("Columns","Values").show(false)
Result :
root
|-- col1: string (nullable = true)
|-- col2: string (nullable = true)
|-- col3: string (nullable = true)
|-- col4: string (nullable = true)
|-- col5: string (nullable = true)
+-------+------+
|Columns|Values|
+-------+------+
|col1 |val1 |
|col2 |val2 |
|col3 |val3 |
|col4 |val4 |
|col5 |val5 |
+-------+------+
Case : Mix of data types in Data :
If you have different types convert them to String... remaining steps wont change..
val df1 = df.select(df.columns.map(c => col(c).cast(StringType)): _*)
Full Example :
import org.apache.spark.sql.functions._
import spark.implicits._
import org.apache.spark.sql.Column
val df = Seq(((2), (3), (true), (2.4), ("val"))).toDF("col1", "col2", "col3", "col4", "col5")
df.printSchema()
/**
* convert all columns to to string type since its needed further
*/
val df1 = df.select(df.columns.map(c => col(c).cast(StringType)): _*)
df1.printSchema()
var ColumnsAndValues: Array[Column] = df.columns.flatMap { c => {
Array(lit(c), col(c))
}
}
df1.withColumn("myMap", map(ColumnsAndValues: _*))
.select(explode($"myMap"))
.toDF("Columns", "Values")
.show(false)
Result :
root
|-- col1: integer (nullable = false)
|-- col2: integer (nullable = false)
|-- col3: boolean (nullable = false)
|-- col4: double (nullable = false)
|-- col5: string (nullable = true)
root
|-- col1: string (nullable = false)
|-- col2: string (nullable = false)
|-- col3: string (nullable = false)
|-- col4: string (nullable = false)
|-- col5: string (nullable = true)
+-------+------+
|Columns|Values|
+-------+------+
|col1 |2 |
|col2 |3 |
|col3 |true |
|col4 |2.4 |
|col5 |val |
+-------+------+

Answer 2

From Spark-2.4 Use arrays_zip with array(column_values), array(column_names) then explode to get the result.
Example:
val df=Seq((("val1"),("val2"),("val3"),("val4"),("val5"))).toDF("col1","col2","col3","col4","col5")
val cols=df.columns.map(x => col(s"${x}"))
val str_cols=df.columns.mkString(",")
df.withColumn("new",explode(arrays_zip(array(cols:_*),split(lit(str_cols),",")))).
select("new.*").
toDF("values","Columns").
show()
//+------+-------+
//|values|Columns|
//+------+-------+
//| val1| col1|
//| val2| col2|
//| val3| col3|
//| val4| col4|
//| val5| col5|
//+------+-------+
UPDATE:
val df=Seq(((2),(3),(true),(2.4),("val"))).toDF("col1","col2","col3","col4","col5")
df.printSchema
//root
// |-- col1: integer (nullable = false)
// |-- col2: integer (nullable = false)
// |-- col3: boolean (nullable = false)
// |-- col4: double (nullable = false)
// |-- col5: string (nullable = true)
//cast to string
val cols=df.columns.map(x => col(s"${x}").cast("string").alias(s"${x}"))
val str_cols=df.columns.mkString(",")
df.withColumn("new",explode(arrays_zip(array(cols:_*),split(lit(str_cols),",")))).
select("new.*").
toDF("values","Columns").
show()
//+------+-------+
//|values|Columns|
//+------+-------+
//| 2| col1|
//| 3| col2|
//| true| col3|
//| 2.4| col4|
//| val| col5|
//+------+-------+