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
Anonymous
Not applicable

Faster Division/Multiplication using Merge same table Query - Power Query

I have a table of Columns containing Attribute counts. I have added a column that does row-wise summation of the Attribute column (denoted by Numbers) values. I want to Divide the Attribute columns by the "Count" column, in-place (replace values) i.e. do a `Percentage of Row-Total`. I don't want to add new columns, then do the calculation, then delete the original column.

 

"ID"  "CD"     "6"    "5"     "4"    "3"   "1"   "Count"

1RJ.14512453755302
2RJ.2436636579211
3RJ.32910441768258
4RJ.42646233411140
5RJ.53451412213161
6RJ.63363265813193
7RJ.7225640418167
8RJ.83493405411232
9RJ.93469345011198
10RJ.10274633428156
11RJ.113270264215185
12RJ.12225735455164
13RJ.13258339439199
14RJ.14316926508184
15RJ.152462393510170
16RJ.16117543576192
17RJ.17214521238118
18RJ.182051303212145
19RJ.19286034379168
20RJ.20263829414138
21RJ.212164234211161
22RJ.223361353811178
23RJ.23195136368150
24RJ.242265323611166
25RJ.252885354511204
26RJ.262656282711148
27RJ.27294341337153
28RJ.282257154311148
29RJ.29143022359110
30RJ.30193525278114
31RJ.311830282612114
32RJ.321354364410157
33RJ.33205919409147
34RJ.34243438235124
35RJ.35266737398177
36RJ.36274631347145
37RJ.372426273613126
38RJ.38142424211093
39RJ.393158342810161
40RJ.401845253710135

 

In one of my searches, someone had suggested that the values for multiple columns could be calculated in a faster way (avoid row level context evaluation) by merging the table with itself and with some M-code. But not shared the code example.

Is it possible?

1 ACCEPTED SOLUTION
Jimmy801
Community Champion
Community Champion

Hello @Anonymous 

 

here some dynamic solution. Uses Unpivoting, Grouping and twice transforming of columns. The only parameter you have to change is the list in the Unpivot other (in case you have some additional columns). As you can see you can also remove the column "count" from your table.

let
    Origine = 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 [ID = _t, CD = _t, #"6" = _t, #"5" = _t, #"4" = _t, #"3" = _t, #"1" = _t, Count = _t]),
    ChangeType = Table.TransformColumnTypes(Origine,{{"6", Int64.Type}, {"5", Int64.Type}, {"4", Int64.Type}, {"3", Int64.Type}, {"1", Int64.Type}}),
    #"Removed Columns" = Table.RemoveColumns(ChangeType,{"Count"}),
    #"Unpivoted Other Columns" = Table.UnpivotOtherColumns(#"Removed Columns", {"ID", "CD"}, "Attribute", "Value"),
    #"Grouped Rows" = Table.Group(#"Unpivoted Other Columns", {"CD"}, {{"AllRows", each _, type table [ID=text, CD=text, Attribute=text, Value=number]}}),
    TransformAllRows = Table.TransformColumns
    (
        #"Grouped Rows",
        {
            {
                "AllRows",
                (tbl)=> 
                let
                    SumValue = List.Sum(tbl[Value]),
                    TransformTbl = Table.TransformColumns
                    (
                        tbl,
                        {
                            {
                                "Value",
                                each _ / SumValue
                            }
                        }
                    )
                in 
                    TransformTbl

            }
        }
    ),
    #"Expanded AllRows" = Table.ExpandTableColumn(TransformAllRows, "AllRows", {"ID", "Attribute", "Value"}, {"ID", "Attribute", "Value"}),
    #"Pivoted Column" = Table.Pivot(#"Expanded AllRows", List.Distinct(#"Expanded AllRows"[Attribute]), "Attribute", "Value", List.Sum),
    #"Changed Type" = Table.TransformColumnTypes(#"Pivoted Column",{{"ID", Int64.Type}}),
    #"Sorted Rows" = Table.Sort(#"Changed Type",{{"ID", Order.Ascending}})
in
    #"Sorted Rows"

 

Copy paste this code to the advanced editor in a new blank query to see how the solution works.

If this post helps or solves your problem, please mark it as solution (to help other users find useful content and to acknowledge the work of users that helped you)
Kudoes are nice too

Have fun

Jimmy

 

View solution in original post

24 REPLIES 24
watkinnc
Super User
Super User

Have you tried just transforming the columns?

 

Table.TransformColumns(PriorStep, {{"6",each _/[Count]}, {"5", each _/[Count]}})

 

No reason that shouldn't work for ya. 


I’m usually answering from my phone, which means the results are visualized only in my mind. You’ll need to use my answer to know that it works—but it will work!!
Anonymous
Not applicable

Hi @watkinnc,

 

Thanks for your reply. Could you look at the sample files that i had shared earlier and see if you could recreate the Output sheet table from the given sample file?

Jimmy801
Community Champion
Community Champion

Hello @Anonymous 

 

here some dynamic solution. Uses Unpivoting, Grouping and twice transforming of columns. The only parameter you have to change is the list in the Unpivot other (in case you have some additional columns). As you can see you can also remove the column "count" from your table.

let
    Origine = 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 [ID = _t, CD = _t, #"6" = _t, #"5" = _t, #"4" = _t, #"3" = _t, #"1" = _t, Count = _t]),
    ChangeType = Table.TransformColumnTypes(Origine,{{"6", Int64.Type}, {"5", Int64.Type}, {"4", Int64.Type}, {"3", Int64.Type}, {"1", Int64.Type}}),
    #"Removed Columns" = Table.RemoveColumns(ChangeType,{"Count"}),
    #"Unpivoted Other Columns" = Table.UnpivotOtherColumns(#"Removed Columns", {"ID", "CD"}, "Attribute", "Value"),
    #"Grouped Rows" = Table.Group(#"Unpivoted Other Columns", {"CD"}, {{"AllRows", each _, type table [ID=text, CD=text, Attribute=text, Value=number]}}),
    TransformAllRows = Table.TransformColumns
    (
        #"Grouped Rows",
        {
            {
                "AllRows",
                (tbl)=> 
                let
                    SumValue = List.Sum(tbl[Value]),
                    TransformTbl = Table.TransformColumns
                    (
                        tbl,
                        {
                            {
                                "Value",
                                each _ / SumValue
                            }
                        }
                    )
                in 
                    TransformTbl

            }
        }
    ),
    #"Expanded AllRows" = Table.ExpandTableColumn(TransformAllRows, "AllRows", {"ID", "Attribute", "Value"}, {"ID", "Attribute", "Value"}),
    #"Pivoted Column" = Table.Pivot(#"Expanded AllRows", List.Distinct(#"Expanded AllRows"[Attribute]), "Attribute", "Value", List.Sum),
    #"Changed Type" = Table.TransformColumnTypes(#"Pivoted Column",{{"ID", Int64.Type}}),
    #"Sorted Rows" = Table.Sort(#"Changed Type",{{"ID", Order.Ascending}})
in
    #"Sorted Rows"

 

Copy paste this code to the advanced editor in a new blank query to see how the solution works.

If this post helps or solves your problem, please mark it as solution (to help other users find useful content and to acknowledge the work of users that helped you)
Kudoes are nice too

Have fun

Jimmy

 

Anonymous
Not applicable

Hi @Jimmy801 ,

 

I found this solution last night which is pretty fast and consice:

 

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("VVRLdiQxCLtLr/PmGTD+XGGWs+3X979GCgmYZNFWUiVTIMl+v1/y+nr9+/snYPqziM5ndXuWHf/Hz4a+Pl/vl5IbMIOwVryMxfez3OenIqAaqQEaz2VE2Rmf2bHhxAs/4E5yAzTezVg0tlo8k9gkc4Dr5Hq9dKmyGm1J7JLFHha5aBLtWn3BT3OvgbvJ3VXI0cio4uCvDeoh9VQL14rq3a4aFbvk3uKu/stHj3Ypg4y0IlB3CYHWp1YTT2Mgl2+BFm/3aP2gBMw8TnY6B+R8Ud+8XAd5TZLTO6DGmwMvbtkOP+8lOc0DmtSIVHlU0ycrp3tA5GxpVUYvGF02rZb0D4g5EUi0gPbx/FJoSQOB2mlWqSihD0ml00KgjoqRjdISOslM8dJEoMaWNcpFq9zLYmlNE7XdsFNnAElCRCzJaSK7hHiz+qWJ8iPOWsdPO9BSwtlp9s7a6aIy5T1jH1mmiVJrughE/eWlha3/jTB7mjYCIcjxX2niERg0XdNGIHOxah9Snuc7204fgdqRg3SYGTuch1bTR9bqWEvH5Hfp9JH30SzDKaeXj0JFLH0ESgeUI+/OE0e09BGIULHyqYmZp2Knj9YXFu4N6DxnnwLnbWN1k1pn9VZLuHQ4TZLTRyDMZEpPpapueJDTRg4WX1+7Io0jiRF3Vk4XgT8uJ6lv7B8nxtJEoPa9jm2ME8xRxsnSRWa4O+cipUfe05Ym8s6It94XMfSmeHlkZroIhDGzXURzZD8KfD7f", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [ID = _t, CD = _t, #"6" = _t, #"5" = _t, #"4" = _t, #"3" = _t, #"1" = _t, Counts = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"6", Int64.Type}, {"5", Int64.Type}, {"4", Int64.Type}, {"3", Int64.Type}, {"1", Int64.Type}, {"Counts", Int64.Type}}),
    
    PercentOfRow = Table.FromRecords(
    Table.TransformRows(
        #"Changed Type",
        (r) => 
        Record.TransformFields(
            r,
            {
                {"6", each r[#"6"] / r[Counts] },
                {"5", each r[#"5"] / r[Counts] },
                {"4", each r[#"4"] / r[Counts] },
                {"3", each r[#"3"] / r[Counts] },
                {"1", each r[#"1"] / r[Counts] }
            }
        )
    ),        
    type table [
               CD= Text.Type,
               ID = Int64.Type,
               #"6" = Percentage.Type, 
               #"5" = Percentage.Type,
               #"4" = Percentage.Type,
               #"3" = Percentage.Type,
               #"1" = Percentage.Type
    ]
)
in
    PercentOfRow

 

taken from here:

https://stackoverflow.com/questions/31885049/power-query-transform-a-column-by-multiplying-by-anothe... 

and here:

https://stackoverflow.com/questions/31548135/power-query-transform-a-column-based-on-another-column?... 

 

Anonymous
Not applicable

Hi @Anonymous 

the solution you found works at the row level, which is just what it seemed you asked not to do.

If you are also interested in this type of solution, it is also possible to make it usable in general and not only for 5 columns with a given  name.

 

Anonymous
Not applicable

I found @Anonymous solution quite innovative with the calculations done in lists. However, the solution loses other main columns like CD, ID etc and you have to again rename the Attrb columns.

 

@Jimmy801 solution was straight forward with the unpivot - pivot and calculations though it took the same amount of time as my solution.

 

The problem that i am facing is that i am adding additional calculated columns to this final table and at each step that references previous calculated step, the loading just increases exponentially into Gigabytes though only for these 40 rows.

for e.g.

1. Step1 = Table.AddColumn(Step0, each [6] / Average[6]) then

2. Step2 = Table.AddColumn(Step1, each [4] / Average[4]).

 

But that will be another post that i will put up today.

Anonymous
Not applicable

 

Hi @Anonymous 

" ...

I found @Rocco_sprmnt21 solution quite innovative with the calculations done in lists. However, the solution loses other main columns like CD, ID etc and you have to again rename the Attrb columns.

..."

 

 

I have tried, from what can be understood from the request, to stay strictly on the subject and give a hopefully useful suggestion.
I haven't tried to do a school homework with all the things in the right place: I don't have enough time to do this right now.
If from a performance point of view my solution is preferable to the others that work at the row level, you could fill in the missing aspects.

 

 

 

Anonymous
Not applicable

From the title of the subject it seemed an important aspect, but I'm not sure if that's the case.
However, I wanted to propose a comparison between the performances of the different solutions.
This calculates the percentages of the row total for a 10 X 1,000,000 table less than 5 '

 

 

 

 

 

 

let

dim=1000000,
    lst = List.Transform({1..10}, each List.Transform(List.Random(dim, _), each Number.Round(_*dim,0))),
    tfo=  Table.FromColumns(lst),
    #"Aggiunta colonna indice" = Table.AddIndexColumn(tfo, "Indice", 1, 1, Int64.Type),
    tab = Table.AddColumn(#"Aggiunta colonna indice", "CD", each "RJ."& Text.From([Indice])),
    Origine = tab,
    #"Rimosse colonne" = Table.RemoveColumns(Origine,{"Indice"}),
    #"Trasformate altre colonne tramite UnPivot" = Table.UnpivotOtherColumns(#"Rimosse colonne", {"CD"}, "Attributo", "Valore"),
    #"Rimosse colonne1" = Table.RemoveColumns(#"Trasformate altre colonne tramite UnPivot",{"Attributo"}),
    #"Colonna trasformata tramite Pivot" = Table.Pivot(#"Rimosse colonne1", List.Distinct(#"Rimosse colonne1"[CD]), "CD", "Valore", (v)=>List.Transform(v, each _/List.Sum(v))),
    #"Trasposta colonna" = Table.FromRows(Table.Transpose(#"Colonna trasformata tramite Pivot")[Column1])
in

#"Trasposta colonna"

 

 

 

 

this  ("yours") seems much faster

 

 

let 

    dim=1000000,

   Origine = Table.FromColumns(List.Transform({1..5}, each List.Transform(List.Random(dim, _), each Number.Round(_*dim,0))),{"6","5","4","3","1"}),
    #"Aggiunta colonna personalizzata" = Table.AddColumn(Origine, "Counts", each [6]+[5]+[4]+[3]+[1]),



    #"Changed Type" = Table.TransformColumnTypes(#"Aggiunta colonna personalizzata",{{"6", Int64.Type}, {"5", Int64.Type}, {"4", Int64.Type}, {"3", Int64.Type}, {"1", Int64.Type}, {"Counts", Int64.Type}}),
    
    PercentOfRow = Table.FromRecords(
    Table.TransformRows(
        #"Changed Type",
        (r) => 
        Record.TransformFields(
            r,
            {
                {"6", each r[#"6"] / r[Counts] },
                {"5", each r[#"5"] / r[Counts] },
                {"4", each r[#"4"] / r[Counts] },
                {"3", each r[#"3"] / r[Counts] },
                {"1", each r[#"1"] / r[Counts] }
            }
        )
    ),        
    type table [
            
               #"6" = Percentage.Type, 
               #"5" = Percentage.Type,
               #"4" = Percentage.Type,
               #"3" = Percentage.Type,
               #"1" = Percentage.Type
    ]
)
in
    PercentOfRow

 

 

 

 

 

Anonymous
Not applicable

Interesting @Anonymous !

 

The main problem with my solution or yours or @Jimmy801 is that when i start adding custom calculated columns (row-level evaluation) to our final table, the loading time just keeps on increasing exponentially. 😞

 

for e.g. if i add the following calculated columns to our final table:

Step1 = AddColumn(Final, "Total" , each [#"6"] + [#"4"], Percentage.Type)

RowAvgOfTotal = Number.Round(List.Average(Step1[Total]), 2)

Step2 = AddColumn(Step1, "Total%" , each [Total] / RowAvgOfTotal, Percentage.Type)

RowPercentileOfTotal = Number.Round(PercentileInclusive(Step2[Total%], 0.8), 4 )
Step3 = AddColumn(Step2, "Total%Percentile" , each [Total] / RowPercentileOfTotal, Percentage.Type)

PercentileInclusive Function:

//PercentileInclusive Function
let
    Source = (inputSeries as list, percentile as number) =>
let
    SeriesCount = List.Count(inputSeries),
    PercentileRank = percentile*(SeriesCount-1)+1, //percentile value between 0 and 1
    PercentileRankRoundedUp = Number.RoundUp(PercentileRank),
    PercentileRankRoundedDown = Number.RoundDown(PercentileRank),
    Percentile1 = List.Max(List.MinN(inputSeries,PercentileRankRoundedDown)),
    Percentile2 = List.Max(List.MinN(inputSeries,PercentileRankRoundedUp)),
    Percentile = Percentile1+(Percentile2-Percentile1)*(PercentileRank-PercentileRankRoundedDown)
in
    Percentile
in
    Source

Adding such calculated columns just increases loading time from 4 to 5mbs to gb's. It is apparent that adding the columns to previous steps each time increases the load time as i think the previous calculated columns are also getting evaluated again.  Any alternative approach that you can suggest?

Anonymous
Not applicable

I'm not sure I understand what you want to do, but maybe it can be useful to try doing separate calculations on the same source table and then putting it all together.

ac1 = func1 (tab),
ac2 = func2 (tab),
..

 

tfc=Table.FromColumns(

Anonymous
Not applicable

@Anonymous 


@Anonymous wrote:

I'm not sure I understand what you want to do, but maybe it can be useful to try doing separate calculations on the same source table and then putting it all together.

ac1 = func1 (tab),
ac2 = func2 (tab),
..

 

tfc=Table.FromColumns(


These seem useful, but don't know how to do it without affecting loading time. How would you do it with my given sample files to create the desired output as shown in the workbook in a speedier way? Right now, in my amateur way, the loading takes a lot of time (going into gb's).

Anonymous
Not applicable

Hi @Anonymous 

 

the csv and xlsx files you uploaded are not clear to me.

 

the files you uploaded are not clear to me.
And above all, it is not clear what gb's are.
I have already showed you with code that the operations you described (including those with percentiles) in a table of 1000000 rows and 10 columns are performed in a few minutes.
 
PS
In general, avoid making M do unnecessary operations such as rounding and other aesthetic things.
You can do these in a different environment or at the end.
 
Anonymous
Not applicable

@Anonymous oh, sorry for the confusion.

GB's  = Gigabytes...that's what shows up when the workbook is connecting to the Datasource i.e. Sample CSV when i am adding additional columns as shown in the `Desired output`.

 

What i meant is, given a sample csv file, how do i transform this csv into the `Desired Output` without increasing the load time when i refresh the queries? The output is pretty straight-forward and all the column formulae are shown.

 

If it's possible, can you revisit and have a look at the workbook? I am particularly interested in the suggestion you made earlier i.e. to calculate the columns separately and then club them together. Can you help here?

Anonymous
Not applicable

Since the csv and xlsx files you uploaded are not clear to me, I use a randomly created tables (5k rows)  to explain an idea of what can happen in your case (but without having a precise view of the input data and the code you use, I can just guess what happens).


Run the two different scripts and note the differences.

If you find any differences, use the scheme that best suits your needs.

 

let
    dim=50000,
    Origine = Table.FromColumns(List.Transform({1..10}, each List.Transform(List.Random(dim, _), each Number.Round(_*dim,0))),{"0".."9"}),
    #"Aggiunta colonna personalizzata" = Table.AddColumn(Origine, "total", each [4]+[6]),
    avg=List.Average(#"Aggiunta colonna personalizzata"[total]), 
    acp1 = Table.AddColumn(#"Aggiunta colonna personalizzata", "pctot", each [total]/avg),
    pcn=pcntl(acp1[pctot],0.8),
    acp = Table.AddColumn(acp1, "pcntl", each if [pctot]/pcn>1 then "H" else "")
in
    acp

 

 

 

 

let
    dim=50000,
    Origine = Table.FromColumns(List.Transform({1..10}, each List.Transform(List.Random(dim, _), each Number.Round(_*dim,0))),{"0".."9"}),
    #"Aggiunta colonna personalizzata" = Table.AddColumn(Origine, "total", each [4]+[6]),
    acp1 = Table.AddColumn(#"Aggiunta colonna personalizzata", "pctot", each [total]/List.Average(#"Aggiunta colonna personalizzata"[total])),
    acp = Table.AddColumn(acp1, "pcntl", each if [pctot]/pcntl(acp1[pctot],0.8)>1 then "H" else "")
in
    acp

 

 

pcntl(list, number) is the your percentile function.

 

 

 

Anonymous
Not applicable

let
    dim=500000,
    Origine = Table.FromColumns(List.Transform({1..10}, each List.Transform(List.Random(dim, _), each Number.Round(_*dim,0))),{"0".."9"}),


    PercentOfRow = Table.FromRecords( Table.TransformRows(Origine, (r)=>  let sum=List.Sum(Record.FieldValues(r)) in  Record.FromList(List.Transform(Record.FieldValues(r), each _/sum), {"0".."9"})& [Counts=sum])),
    pc = Table.TransformColumnTypes(PercentOfRow,List.Transform(Table.ColumnNames(Origine),each {_, Percentage.Type})),
    #"Aggiunta colonna personalizzata" = Table.AddColumn(pc, "total", each [4]+[6]),
    avg =  List.Average(#"Aggiunta colonna personalizzata"[total]),

    acp1 = Table.AddColumn(#"Aggiunta colonna personalizzata", "pctot", each [total]/avg),
    pcn=pcntl(acp1[pctot],0.8),
    acp = Table.AddColumn(acp1, "pcntl", each [pctot]/pcn)
in
    acp

 

 

this om my laptop ends in few minutes

Anonymous
Not applicable

I was tempted to generalize the following approach. For the first part it wasn't difficult.
But for the part where the type of the columns is specified, it wasn't easy.

 

 

 

 

 

PercentOfRow = Table.FromRecords(
    Table.TransformRows(
        #"Changed Type",
        (r) => 
        Record.TransformFields(
            r,
            {
                {"6", each r[#"6"] / r[Counts] },
                {"5", each r[#"5"] / r[Counts] },
                {"4", each r[#"4"] / r[Counts] },
                {"3", each r[#"3"] / r[Counts] },
                {"1", each r[#"1"] / r[Counts] }
            }
        )
    ),        
    type table [
               CD= Text.Type,
               ID = Int64.Type,
               #"6" = Percentage.Type, 
               #"5" = Percentage.Type,
               #"4" = Percentage.Type,
               #"3" = Percentage.Type,
               #"1" = Percentage.Type
    ]
)
in
    PercentOfRow

 

 

 

 

 

But here is the result anyway. 
 A big thank to   Ben Gribaudo  now the solution is  general and again one-linear

 

 

 

 

let 
    dim=1000000,
    Origine = Table.FromColumns(List.Transform({1..10}, each List.Transform(List.Random(dim, _), each Number.Round(_*dim,0))),{"0".."9"}),
    
    PercentOfRow = Table.FromRecords( Table.TransformRows(Origine, (r)=>  let sum=List.Sum(Record.FieldValues(r)) in  Record.FromList(List.Transform(Record.FieldValues(r), each _/sum) , {"0".."9"})& [Counts=sum]),   Expression.Evaluate("type table"& "["&Text.Combine(List.Transform({"0".."9"}, each _&"="&"Percentage.Type"),",")&", Counts=number]",#shared))

in
  PercentOfRow

 

 

 

 

 

Anonymous
Not applicable

Hi @Anonymous ,

 

Any query in M can be written as one line this way, it is just a matter of time and persistence :).

 

This is similar, but parametrised approach:

let
    Origine = 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 [ID = _t, CD = _t, #"6" = _t, #"5" = _t, #"4" = _t, #"3" = _t, #"1" = _t, Count = _t]),
    ChangeType = Table.TransformColumnTypes(Origine,{{"6", Int64.Type}, {"5", Int64.Type}, {"4", Int64.Type}, {"3", Int64.Type}, {"1", Int64.Type}}),
    #"Removed Columns" = Table.RemoveColumns(ChangeType,{"Count"}),


    mTable = #"Removed Columns",
    nonValueColumns = {"ID", "CD"},
    
    fTransformRecord = (mRecord as record, mNonValueColumns as list)=>
        let 
            recordsToProcess = Record.RemoveFields(mRecord, mNonValueColumns),
            sumTotal = List.Sum(Record.ToList(recordsToProcess)),
            TransformRecord = List.Accumulate(Record.FieldNames(recordsToProcess), recordsToProcess, (a, n)=> Record.TransformFields(a, {n, (x)=>x / sumTotal}))   & Record.SelectFields(mRecord, nonValueColumns)     
        in TransformRecord,
    
    Output = Table.FromRecords(Table.TransformRows(mTable, (x)=> fTransformRecord(x, nonValueColumns))) 

in Output

 

Kind regards,

JB

Anonymous
Not applicable

Hi @Anonymous ,

 

this statement of yours

"Any query in M can be written as one line this way, it is just a matter of time and persistence :)." (*)

is an incontrovertible truth of which I am well aware.

 

But it was not from this general aspect that my enthusiasm for the 1-line solution arose.

 

The purpose of my previous post, after seeing that @Anonymous 's 1-line solution was performing, was to generalize it, for example, to be able to deal with a generic number of columns.

But, in a first attempt I was unable to make the part of setting column types parametric and in the same expression.

After reading Bengraud's blog, I was able, albeit with the use of the expression.evaluate function, to put in the same expression as it was in the 1-line solution of @Anonymous  also the part of setting column types.

 

 

from this two

 

 

 

    PercentOfRow = Table.FromRecords( Table.TransformRows(Origine, (r)=>  let sum=List.Sum(Record.FieldValues(r)) in  Record.FromList(List.Transform(Record.FieldValues(r), each _/sum), {"0".."9"})& [Counts=sum])),
    pc = Table.TransformColumnTypes(PercentOfRow,List.Transform(Table.ColumnNames(Origine),each {_, Percentage.Type}))

 

 

 

 

to this one

 

 

 

 

    PercentOfRow = Table.FromRecords( Table.TransformRows(Origine, (r)=>  let sum=List.Sum(Record.FieldValues(r)) in  Record.FromList(List.Transform(Record.FieldValues(r), each _/sum) , {"0".."9"})& [Counts=sum]),   Expression.Evaluate("type table"& "["&Text.Combine(List.Transform({"0".."9"}, each _&"="&"Percentage.Type"),",")&", Counts=number]",#shared))

 

 

 

 

just this 🙂

 

 

(*)

the reduction to a single line was not simply incorporating one expression into another: so for example.

 

    pc = Table.TransformColumnTypes(    PercentOfRow = Table.FromRecords( Table.TransformRows(Origine, (r)=>  let sum=List.Sum(Record.FieldValues(r)) in  Record.FromList(List.Transform(Record.FieldValues(r), each _/sum), {"0".."9"})& [Counts=sum])),,List.Transform(Table.ColumnNames(Origine),each {_, Percentage.Type}))

 

I would never have been thrilled with a solution put like this.

 

PS

In general I believe that it is not the task of power query to do these "aesthetic" operations, I mean to change the number type to percentage type.

Perhaps it is preferable to do only mash-up and not make-up.

 

 

Anonymous
Not applicable

This is fast. However, once you start adding additional calculated columns (see sample workbook & sample csv in TEST folder in my previous reply), then loading the table each time a calculated column gets added, goes for a toss! That is my concern and the desired output i am trying to achieve in a fast loading manner.

Anonymous
Not applicable

Wow! This is incredible.

 

@Anonymous, I created a `sample workbook` and the `sample input csv` showing the Input and the desired Output just for your experienced eyes to see why the data is loading so slowly. I have added the percentile function that i had googled earlier (it seems there is no inbuilt functions in PQ for Percentile, Rank etc.)

 

Here are the files in this TEST folder. I have removed adding the other Columns code (as shown in desired output) as it was taking a long time to finish and just kept the code till where the row percentage of total gets calculated. The calculations for the additional columns are shown in the desired output table.

 

I am not sure what you meant by creating the columns separately and then joining them. I hope you can provide some clear logic on how to speed up the loading once the entire desired output report gets created.

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.

Top Solution Authors
Top Kudoed Authors