Forum Discussion
Aggregate in numerical order
- 2 years ago
Hi Anonymous, why not to use Power Query solution? If you don't know how to use this query - read note at the botom of my post.
I've added 3 more rows to your sample data just to demonstrate that we consider every Order # separately.
EDIT: 2024-03-28 15:07 GMT+1: I've edited my code, and now it is realy fast also with big data
Result
let fn_AggLT = (lst as list)=> List.Reverse(List.Generate( ()=> [ x = List.Count(lst)-1, y = lst{x} ], each [x] >= 0, each [ x = [x]-1, y = [y] + lst{x} ], each [y] ) ), Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMjMwMDIwUNJRMjQwMDYB0UDsnJOamAekjYDYWN/IQt/IwMhEKVYHi3KQEuf8xBIgZU5YtTEQByQmZ0PtwaLaEIdTjAkrR3KKBWHVSE4xQVUdCwA=", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [#"Order #" = _t, Product = _t, Operation = _t, #"Operation name" = _t, #"Operation Leadtime" = _t, #"Order Plan date" = _t]), ChangedType = Table.TransformColumnTypes(Source,{{"Order #", Int64.Type}, {"Product", Int64.Type}, {"Operation", Int64.Type}, {"Operation name", type text}, {"Operation Leadtime", type number}, {"Order Plan date", type date}}, "en-US"), // Added Aggregated Leadtime into inner [All] table GroupedRows = Table.Group(ChangedType, {"Order #"}, {{"All", each Table.FromColumns(Table.ToColumns(_) & {fn_AggLT(List.Buffer([Operation Leadtime]))}, Value.Type(_ & #table(type table[Aggregated Leadtime=number], {{}}))), type table}}), CombinedAll = Table.Combine(GroupedRows[All]), Ad_StartDate = Table.AddColumn(CombinedAll, "Start Date", each Date.AddDays([Order Plan date], -[Aggregated Leadtime]), type date) in Ad_StartDate
Thank you so much for your reply.
I realize that I perhaps should posted this in Desktop category and not in PowerQuerry because I'm not that advanzed in coding. Is it possible to solve my issue in DAX?
I also forgot to mention that data in my above table is coming from two different fact tables. I've merged two fact tables.
Hi Anonymous, why not to use Power Query solution? If you don't know how to use this query - read note at the botom of my post.
I've added 3 more rows to your sample data just to demonstrate that we consider every Order # separately.
EDIT: 2024-03-28 15:07 GMT+1: I've edited my code, and now it is realy fast also with big data
Result
let
fn_AggLT =
(lst as list)=>
List.Reverse(List.Generate(
()=> [ x = List.Count(lst)-1, y = lst{x} ],
each [x] >= 0,
each [ x = [x]-1, y = [y] + lst{x} ],
each [y] )
),
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMjMwMDIwUNJRMjQwMDYB0UDsnJOamAekjYDYWN/IQt/IwMhEKVYHi3KQEuf8xBIgZU5YtTEQByQmZ0PtwaLaEIdTjAkrR3KKBWHVSE4xQVUdCwA=", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [#"Order #" = _t, Product = _t, Operation = _t, #"Operation name" = _t, #"Operation Leadtime" = _t, #"Order Plan date" = _t]),
ChangedType = Table.TransformColumnTypes(Source,{{"Order #", Int64.Type}, {"Product", Int64.Type}, {"Operation", Int64.Type}, {"Operation name", type text}, {"Operation Leadtime", type number}, {"Order Plan date", type date}}, "en-US"),
// Added Aggregated Leadtime into inner [All] table
GroupedRows = Table.Group(ChangedType, {"Order #"}, {{"All", each Table.FromColumns(Table.ToColumns(_) & {fn_AggLT(List.Buffer([Operation Leadtime]))}, Value.Type(_ & #table(type table[Aggregated Leadtime=number], {{}}))), type table}}),
CombinedAll = Table.Combine(GroupedRows[All]),
Ad_StartDate = Table.AddColumn(CombinedAll, "Start Date", each Date.AddDays([Order Plan date], -[Aggregated Leadtime]), type date)
in
Ad_StartDate
- Anonymous2 years agoNot applicable
Hi,
Thanks allot. Is it possible for you to attach the sample as pbix file? It will be easier for me to solve it 🙂
- dufoq32 years agoCommunity Champion
It is, but it is not necessary 🙂 - Read note below my posts - there is a picture with explanation how to use my query. Don't forget to rename your columns - you provided different column names in sample data vs screenshot in next post.
- Anonymous2 years agoNot applicable
well you made my day - thanks allot and happy eastern 🙂