Writing Power Query (M language) Table.X functions that will dynamically tailor the table scope to each row of data

[Alex MEDC]

I have loaded a simple flat file I have pulled into a query. The data looks like this:

	A	B	C	D	E	F
Broad Identifier	Specific Identifier	Candidate	Candidate Score	Qualifier	Candidate Score Evaluation, Desired Outcome
Ignore
Lowest
Lowest
Ignore
Ignore
Lowest
Lowest
Ignore
Ignore

<tbody>
[TD="align: center"]1
[/TD]

[TD="align: center"]2
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"]9.111
[/TD]
[TD="align: right"]0.222
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]3
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"]9.111
[/TD]
[TD="align: right"]0.222
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]4
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"]9.777
[/TD]
[TD="align: right"]1.444
[/TD]

[TD="align: right"][/TD]

[TD="align: center"]5
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]6
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"]9.555
[/TD]
[TD="align: right"]1.222
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]7
[/TD]
[TD="align: right"]14
[/TD]
[TD="align: right"]14.111
[/TD]
[TD="align: right"]14.222
[/TD]
[TD="align: right"]1.111
[/TD]
[TD="align: right"][/TD]

[TD="align: center"]8
[/TD]
[TD="align: right"]14
[/TD]
[TD="align: right"]14.111
[/TD]
[TD="align: right"]14.111
[/TD]
[TD="align: right"][/TD]

[TD="align: right"][/TD]

[TD="align: center"]9
[/TD]
[TD="align: right"]14
[/TD]
[TD="align: right"]14.111
[/TD]
[TD="align: right"]14.333
[/TD]
[TD="align: right"]1.222
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]10
[/TD]
[TD="align: right"]14
[/TD]
[TD="align: right"]14.111
[/TD]
[TD="align: right"]14.444
[/TD]
[TD="align: right"]1.333
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]11
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.444
[/TD]
[TD="align: right"]9.111
[/TD]
[TD="align: right"]0.333
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]12
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.444
[/TD]
[TD="align: right"]9.399
[/TD]
[TD="align: right"]0.045
[/TD]

[TD="align: right"][/TD]

[TD="align: center"]13
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.444
[/TD]
[TD="align: right"]9.111
[/TD]
[TD="align: right"]0.333
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]14
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.444
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"]0.111
[/TD]
[TD="align: right"][/TD]

[TD="align: center"]15
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.444
[/TD]
[TD="align: right"]9.555
[/TD]
[TD="align: right"]1.111
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]16
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.777
[/TD]
[TD="align: right"]9.111
[/TD]
[TD="align: right"]0.666
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]17
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.777
[/TD]
[TD="align: right"]9.111
[/TD]
[TD="align: right"]0.666
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]18
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.777
[/TD]
[TD="align: right"]9.333
[/TD]
[TD="align: right"]0.444
[/TD]
[TD="align: right"][/TD]
[TD="align: right"][/TD]

[TD="align: center"]19
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.777
[/TD]
[TD="align: right"]9.555
[/TD]
[TD="align: right"]0.222
[/TD]
[TD="align: right"][/TD]

[TD="align: center"]20
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.777
[/TD]
[TD="align: right"]9.599
[/TD]
[TD="align: right"]0.178
[/TD]

[TD="align: right"][/TD]

[TD="align: center"]21
[/TD]
[TD="align: right"]9
[/TD]
[TD="align: right"]9.777
[/TD]
[TD="align: right"]9.888
[/TD]
[TD="align: right"]1.111
[/TD]

[TD="align: right"][/TD]

</tbody>

Data2
​

Note that the real input data is in columns A:E; column F is just a hand-typed set of values that I am HOPING I can achieve with M code, with your help.

Here is the M code for the full query taken from the Power Query editor:

let
    Source = Csv.Document(File.Contents("H:\Misc\Power Query experiment\Data2.csv"),[Delimiter=",", Columns=6, Encoding=1252, QuoteStyle=QuoteStyle.None]),
    #"Promoted Headers" = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),
    #"Changed Type" = Table.TransformColumnTypes(#"Promoted Headers",{{"Broad Identifier", Int64.Type}, {"Specific Identifier", type number}, {"Candidate", type number}, {"Candidate Score", type number}, {"Qualifier", type text}, {"Candidate Score Evaluation, Desired Outcome", type text}}),
    #"Added Conditional Column" = Table.AddColumn(#"Changed Type", "Candidate Score Evaluation", each if [Candidate Score] = List.Min(Table.SelectRows(#"Changed Type",
                                                                                                                                                       each ([Specific Identifier] = [Specific Identifier] and [Qualifier] <> "Ignore"))[Candidate Score])
                                                                                                    then "Lowest"
                                                                                                    else null)
in
    #"Added Conditional Column"

You will notice from the code that the column added in the final step is intended to, for each row, (a) focus only on rows in the table that share the same value in the "Specific Identifier" column as the current row and (b) ignore rows that have Ignore in the "Qualifier" column. If you try this code out on the data, you'll see that it does not achieve the desired results.

Please help.

 

[peter789]

It's not pretty but this is one way of doing it.
First filter out ignore. Then replace null with zero if that is what you mean to do, otherwise filter out null.
Then reference this query.
Then group this query by Specific Identifier and return the minimum Candidate score and give the query a meaningful name.
Merge the two queries linking on the Specific identifier and Candidate score (use Ctrl to select second link).
Expand minimum.
Filter by not null.
Add custom column with formula ="Lowest".
Go back to original query, merge as new with the the query which has the column with Lowest in it.
Link again on Specific Identifier and Candidate Score.
Expand table to only return Custom column which will add Lowest.
Then tidy up by removing unwanted columns.
Peter

 

[Alex MEDC]

Thanks, Peter. Another user got an answer to me on another forum already, and he provided code. I will have to take some time to think about it after hours, since I'm not a coder. But anyway, I dropped it in and it worked. Check it out at https://community.powerbi.com/t5/De...able-scope/m-p/693101/highlight/false#M334266

 

[peter789]

OK
Glad you found a solution elsewhere.