How to convert a type Any List to a type Double (Scala) - scala

Question

I am new to Scala and I would like to understand some basic stuff.
First of all, I need to calculate the average of a certain column of a DataFrame and use the result as a double type variable.
After some Internet research I was able to calculate the average and at the same time pass it into a List type Any by using the following command:
val avgX_List = mainDataFrame.groupBy().agg(mean("_c1")).collect().map(_(0)).toList
where "_c1" is the second column of my dataframe. This line of code returns a List with type List[Any].
To pass the result into a variable I used the following command:
var avgX = avgX_List(0)
hoping that the var avgX would be type double automatically but that didn't happen obviously.
So now let the questions begin:
What does map(_(0)) do? I know the basic definition of the map() transformation but I can't find an explanation with this exact argument
I know that by using .toList method in the end of the command my result will be a List with type Any. Is there a way that I could change this into List which contains type Double elements? Or even convert this one
Do you think that it would be much more appropriate to pass the column of my Dataframe into a List[Double] and then calculate the average of its elements?
Is the solution I showed above at any point of view correct based on my problem? I know that "it is working" is different from "correct solution"?
Summing up, I need to calculate the average of a certain column of a Dataframe and have the result as a double type variable.
Note that: I am Greek and I find it hard sometimes to understand some English coding "slang".

Answer 1

map(_(0)) is a shortcut for map( (r: Row) => r(0) ), which is in turn a shortcut for map( (r: Row) => r.apply(0) ). The apply method returns Any, and so you are losing the right type. Try using map(_.getAs[Double](0)) or map(_.getDouble(0)) instead.
Collecting all entries of the column and then computing the average would be highly counterproductive, because you'd have to send huge amounts of data to the master node, and then do all the calculations on this single central node. That would be the exact opposite of what Spark is good for.
You also don't need collect(...).toList, because you can access the 0-th entry directly (it doesn't matter whether you get it from an Array or from a List). Since you are collapsing everything into a single Row anyway, you could get rid of the map step entirely by reordering the methods a little bit:
val avgX = mainDataFrame.groupBy().agg(mean("_c1")).collect()(0).getDouble(0)
It can be written even shorter using the first method:
val avgX = mainDataFrame.groupBy().agg(mean("_c1")).first().getDouble(0)

Answer 2

#Any dataType in Scala can't be directly converted to Double.
#Use toString & then toDouble on final captured result.
#Eg-
#scala> x
#res22: Any = 1.0
#scala> x.toString.toDouble
#res23: Double = 1.0
#Note- Instead of using map().toList() directly use (0)(0) to get the final value from your resultset.
#TestSample(Scala)-
val wa = Array("one","two","two")
val wrdd = sc.parallelize(wa,3).map(x=>(x,1))
val wdf = wrdd.toDF("col1","col2")
val x = wdf.groupBy().agg(mean("col2")).collect()(0)(0).toString.toDouble
#O/p-
#scala> val x = wdf.groupBy().agg(mean("col2")).collect()(0)(0).toString.toDouble
#x: Double = 1.0