Dynamic variable in PQ to compare and calculate total

[pdhlk]

Hi guys,

is there a way to store/change a variable in a loop?

I want to calculate the total amount of days products were available and only take the longst timeframe.

(All those products are the same, like they have the ID 1)

I.e

15.10.21 - 30.11.21 
07.12.21 - 28.02.22 

21.07.22 - 20.08.22 

totaldiff = 162 days

Is this possible in PowerQuery or Dax? I would like to avoid other softwaresolutions and try to get this working in PQ or Dax. Functions seem to be a way but will take a lot of performance by doing the loop n^n times. 

In Pseudocode my solution would look like this:

[measure/pq]=

var post = [Posting Date]
var unPost = [Unposting Date]

var totalDatesDifference = Date.diff(post , unPost)

loop this: 

   if(unPost > minPost)
       break
   else
       post = [Posting Date]
       unPost = [Unposting Date]
       total = total + Date.diff( unPost , post )

Thanks for your help

[_AlexandreRM_]

Don't forget to accept my post as solution to help people to find it 🙂

[_AlexandreRM_ · Accepted solution]

Hello @pdhlk , here is an example of what you could do with PQ:

let
Source = Excel.CurrentWorkbook(){[Name="DatesDiff"]}[Content],
typed = Table.TransformColumnTypes(Source,{{"Post date", type date}, {"Unpost date", type date}}),
datesListsAdded = Table.AddColumn(typed, "Dates list", each List.Dates([Post date], Duration.Days([Unpost date] - [Post date]) + 1, #duration(1, 0, 0, 0))),
datesListsExpanded = Table.ExpandListColumn(datesListsAdded, "Dates list"),
count = List.Count(List.Distinct(datesListsExpanded[Dates list]))
in
count

The global behavior is to generate a list of all dates between each post and unpost date, expand these lists, and count the number of distinct dates.

With your sample data, I get 162 days, which is what I also find with manual calculation (you are missing some days in your own calculation, you should recheck it).

Hope this helps.

[pdhlk]

Thank you very much, it works like a charm! 

And yes I calculated my own sample data wrong... 😣