Skip to main content
cancel
Showing results for 
Search instead for 
Did you mean: 

Find everything you need to get certified on Fabric—skills challenges, live sessions, exam prep, role guidance, and more. Get started

Reply
roxy12
New Member

Add a list ranked by category using M Query.

In the data, there are representatives for each block, and the same person appears multiple times depending on the date and time. I'd like to create a column that groups by block and representative, counting the number of representatives for each block and ranking them.

img1.png

 

I will provide the source that was entered in "Data Input," so please feel free to use it if you like.

 

Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("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", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [rerepresentatives = _t, block = _t, datetime = _t])

 

 

 

This is a display of the final data that has been processed in Excel.

If anyone knows of any good ideas or methods to achieve a final result like this, could you please share them with me?

img2.png

 

 

Best regards,

roxy

1 ACCEPTED SOLUTION
tackytechtom
Super User
Super User

Hi @roxy12 ,

 

How about this:

tackytechtom_0-1681539668755.png

 

 

Here the code in M:

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("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", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [rerepresentatives = _t, block = _t, datetime = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"rerepresentatives", type text}, {"block", type text}, {"datetime", type datetime}}),
    #"Grouped Rows 1" = Table.Group(#"Changed Type", {"rerepresentatives", "block"}, {{"Count", each Table.RowCount(_), Int64.Type}}),
    #"Grouped Rows 2" = Table.Group(#"Grouped Rows 1", {"block"}, {{"AllRows", each SubGroupAddIndex(_), type table [block=nullable text, rerepresentatives=text, Count=number, Index=number]}}),
    SubGroupAddIndex = (Table as table) as table =>
    let
        #"Sorted Rows" = Table.Sort(Table,{{"Count", Order.Descending}}),
        #"Added Index" = Table.AddIndexColumn(#"Sorted Rows", "Index", 1, 1, Int64.Type)
    in         
        #"Added Index",
    #"Expanded AllRows" = Table.ExpandTableColumn(#"Grouped Rows 2", "AllRows", {"rerepresentatives", "Count", "Index"}, {"rerepresentatives", "Count", "IndexDense"}),
    #"Grouped Rows 3" = Table.Group(#"Expanded AllRows", {"block", "Count"}, {{"Index", each List.Min([IndexDense]), type nullable number}}),
    #"Merged Queries" = Table.NestedJoin(#"Expanded AllRows", {"block", "Count"}, #"Grouped Rows 3", {"block", "Count"}, "Grouped Rows", JoinKind.LeftOuter),
    #"Expanded Grouped Rows" = Table.ExpandTableColumn(#"Merged Queries", "Grouped Rows", {"Index"}, {"Grouped Rows.Index"}),
    #"Removed Columns" = Table.RemoveColumns(#"Expanded Grouped Rows",{"IndexDense"})
in
    #"Removed Columns"

Let me know if this helps 🙂

 

/Tom
https://www.tackytech.blog/
https://www.instagram.com/tackytechtom/



Did I answer your question➡️ Please, mark my post as a solution ✔️

Also happily accepting Kudos 🙂

Feel free to connect with me on LinkedIn! linkedIn

#proudtobeasuperuser 

View solution in original post

3 REPLIES 3
CNENFRNL
Community Champion
Community Champion

Genearlly speaking, PQ was designed for data cleansing instead of calculation. DAX solution is way more elegant.

CNENFRNL_1-1681542844719.png


Thanks to the great efforts by MS engineers to simplify syntax of DAX! Most beginners are SUCCESSFULLY MISLED to think that they could easily master DAX; but it turns out that the intricacy of the most frequently used RANKX() is still way beyond their comprehension!

DAX is simple, but NOT EASY!

tackytechtom
Super User
Super User

Hi @roxy12 ,

 

How about this:

tackytechtom_0-1681539668755.png

 

 

Here the code in M:

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("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", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [rerepresentatives = _t, block = _t, datetime = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"rerepresentatives", type text}, {"block", type text}, {"datetime", type datetime}}),
    #"Grouped Rows 1" = Table.Group(#"Changed Type", {"rerepresentatives", "block"}, {{"Count", each Table.RowCount(_), Int64.Type}}),
    #"Grouped Rows 2" = Table.Group(#"Grouped Rows 1", {"block"}, {{"AllRows", each SubGroupAddIndex(_), type table [block=nullable text, rerepresentatives=text, Count=number, Index=number]}}),
    SubGroupAddIndex = (Table as table) as table =>
    let
        #"Sorted Rows" = Table.Sort(Table,{{"Count", Order.Descending}}),
        #"Added Index" = Table.AddIndexColumn(#"Sorted Rows", "Index", 1, 1, Int64.Type)
    in         
        #"Added Index",
    #"Expanded AllRows" = Table.ExpandTableColumn(#"Grouped Rows 2", "AllRows", {"rerepresentatives", "Count", "Index"}, {"rerepresentatives", "Count", "IndexDense"}),
    #"Grouped Rows 3" = Table.Group(#"Expanded AllRows", {"block", "Count"}, {{"Index", each List.Min([IndexDense]), type nullable number}}),
    #"Merged Queries" = Table.NestedJoin(#"Expanded AllRows", {"block", "Count"}, #"Grouped Rows 3", {"block", "Count"}, "Grouped Rows", JoinKind.LeftOuter),
    #"Expanded Grouped Rows" = Table.ExpandTableColumn(#"Merged Queries", "Grouped Rows", {"Index"}, {"Grouped Rows.Index"}),
    #"Removed Columns" = Table.RemoveColumns(#"Expanded Grouped Rows",{"IndexDense"})
in
    #"Removed Columns"

Let me know if this helps 🙂

 

/Tom
https://www.tackytech.blog/
https://www.instagram.com/tackytechtom/



Did I answer your question➡️ Please, mark my post as a solution ✔️

Also happily accepting Kudos 🙂

Feel free to connect with me on LinkedIn! linkedIn

#proudtobeasuperuser 

@tackytechtom 

Thank you very much for your advice!!

Helpful resources

Announcements
Europe Fabric Conference

Europe’s largest Microsoft Fabric Community Conference

Join the community in Stockholm for expert Microsoft Fabric learning including a very exciting keynote from Arun Ulag, Corporate Vice President, Azure Data.

July 2024 Power BI Update

Power BI Monthly Update - July 2024

Check out the July 2024 Power BI update to learn about new features.

July Newsletter

Fabric Community Update - July 2024

Find out what's new and trending in the Fabric Community.