Deedle Row based calculation - f#

Question

I am trying to use Deedle to do some row based calculation. however most of the examples are column based. For example I have this simple structure:
let tt = Series.ofObservations[ 1=>10.0; 3=>20.0;5=> 30.0 ]
let tt2 = Series.ofObservations[1=> 10.0; 3=> Double.NaN; 6=>30.0 ]
let f1 = frame ["cola" => tt; "colb"=>tt2]
val f1 : Frame<int,string> =
cola colb
1 -> 10 10
3 -> 20 <missing>
5 -> 30 <missing>
6 -> <missing> 30
I want to calculate the mean of cola and colb. if I do
f1.Rows |> Series.mapValues(fun r -> (r.GetAs<float>("cola") + r.GetAs<float>("colb") )/2.0)
val it : Series<int,float> =
1 -> 10
3 -> <missing>
5 -> <missing>
6 -> <missing>
i know i can match with each column to handle the mean, however this will not be practical if there are lots of columns.
each row returned by f1.Rows is a ObjectSeries can this be converted into a float Series and apply the stats.mean to a row?
thanks
casbby
Update:
I think i might have found one of the ways to do this (reference: https://github.com/BlueMountainCapital/Deedle/issues/100) :
folding operation:
f1.Rows |> Series.mapValues(fun v -> v.As<float>() |> Series.foldValues (fun acc elem -> elem + acc) 0.0 )
mean (it properly skip the missing value):
f1.Rows |> Series.mapValues(fun v -> v.As<float>() |> Stats.mean )
count:
f1.Rows |> Series.mapValues(fun v -> v.As<float>() |> Stats.count )
if there is a different way please let me know. hopefully this can be useful to new comers like myself.

Answer 1

Your approach using f1.Rows, casting each row to a numerical series and then applying Stats functions is exactly what I was going to suggest as an answer, so I think that approach makes a perfect sense.
Another option that I can think of is to turn the frame into a de-normalized representation and then group the rows by the cola and colb values (so, you'll have all data as rows, but grouped by the other attribute):
let byCol =
f1
|> Frame.stack
|> Frame.groupRowsByString "Column";;
This gives you:
Row Column Value
cola 0 -> 1 cola 10
2 -> 3 cola 20
3 -> 5 cola 30
colb 1 -> 1 colb 10
4 -> 6 colb 30
Now you can use functions working with hierarchical indices to do the calculations. For example, to compute mean of Value for the two groups, you can write:
byCol?Value |> Stats.levelMean fst
I'm not sure which approach I'd recommend at the moment - it probably depends on other operations that you need to do with the data. But it's good to keep the alternative one in mind..