Dataframe : GroupBy by list of column names [duplicate] - scala

Question

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Scala-Spark Dynamically call groupby and agg with parameter values
(1 answer)
Closed 4 years ago.
I have a Dataframe with multiple columns and a List of column names.
I want to process my Dataframe by grouping it according to my list.
Here is an example of what I am trying to do :
val tagList = List("col1","col3","col5")
var tagsForGroupBy = tagList(0)
if(tagList.length>1){
for(i <- 1 to tagList.length-1){
tagsForGroupBy = tagsForGroupBy+","+tags(i)
}
}
// df is a Dataframe with schema (col0, col1, col2, col3, col4, col5)
df.groupBy("col0",tagsForGroupBy)
I understand why it does not work, but I don't know how to make it work.
What is the best solution to do that ?
EDIT :
Here is a more complete example of what I am doing (including SCouto solution) :
I have my tagList that contains some column names ("col3","col5"). I also want to include "col0" and "col1" in my groupBy, independently of my list.
After my groupBy and my aggregations, I want to select all columns used for group By and the new columns from aggregation.
val tagList = List("col3","col5")
val tmpListForGroup = new ListBuffer[String]()
val tmpListForSelect = new ListBuffer[String]()
tmpListForGroup +=tagList (0)
tmpListForSelect +=tagList (0)
for(i <- 1 to tagList .length-1){
tmpListForGroup +=(tagList (i))
tmpListForSelect +=(tagList (i))
}
tmpListForGroup +="col0"
tmpListForGroup +="col1"
tmpListForSelect +="aggValue1"
tmpListForSelect +="aggValue2"
// df is a Dataframe with schema (col0, col1, col2, col3, col4, col5)
df.groupBy(tmpListForGroup.head,tmpListForGroup.tail:_*)
.agg(
[aggFunction].as("aggValue1"),
[aggFunction].as("aggValue1"))
)
.select(tmpListForSelect .head,tmpListForSelect .tail:_*)
This code do exactly what I want, but it look very ugly and complicated for something that (I think) should be simple.
Is there another solution for that ?

Answer 1

When sending column names as Strings, groupBy receives a column as first parameter and a sequence of them as second:
def groupBy(col1: String,cols: String*)
So you need to send two arguments and convert the second one to a sequence:
This will work fine for you:
df.groupBy(tagsForGroupBy.head, tagsForGroupBy.tail:_*)
Or if you want to separete col0 from the list as in your example:
df.groupBy("col0", tagsForGroupBy:_*)