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ItoDiaz
Helper I
Helper I

Translate SUMPRODUCT using a Query Power BI

Dear all,

 

I have a report in Excel and I need to “translate” it in Power BI.

 

I have data (loaded in P. BI)  with date column and the parameters (1=OK, 0=non OK) columns in yellow (see DATA picture). For each parameter I need to calculate a conformity average per day. In excel I use this :

 

Example for the first parameter (SOMMEPROD=SUMPRODUCT):

SOMMEPROD((Tableau2[[CP_CONDUCT_AA]:[CP_CONDUCT_AA]]=1)*(Tableau2[[Date 2]:[Date 2]]='RED P'!Q$1)*1)/SOMMEPROD((Tableau2[[CP_CONDUCT_AA]:[CP_CONDUCT_AA]]>=0)*(Tableau2[[Date 2]:[Date 2]]='RED P'!Q$1)*1)

 

DATA.png

 

 

 

 

 

 

 

 

And I obtain my expect result (see Expected result picture) where my parameters are presented as rows (important detail) with the conformity per day as columns.

 

So, my question is : how can I translate my calculation “SUMPRODUCT” described above and get a query Power BI where my parameters are presented as rows and their respective conformity per day as columns?

 

Expected result.png

 

 

 

 

 

 

 

 

 

 

Please, I just started using Power BI, could you give some ideas with good descriptions of steps to do it? And the useful DAX or query M functions…

2 ACCEPTED SOLUTIONS
v-eachen-msft
Community Support
Community Support

Hi @ItoDiaz ,

 

After importing the data, click "Transform data" to open qeury editor.

1. Remove "CHRONO" column.

2. Unpivot your parameter columns. You will get the "Attribute" column and the "Value" column.

3. Get a table with Value=1, calculate the row number.

4. Get a table with Value>=0, calculate the row number.

5. Merge these two tables, use division to get the percentage.

6. Remove useless columns.

7. Pivot date column.

Here is the codes in advanced editor.

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMlTSUTIyMDLQNTDSNQBxDKDYEI5jdaKVjNCVYWKQMmNsygxQaJAyhKXGCEsxTTNCV4aMIe5EtdQYp6WxAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [No = _t, Date = _t, A = _t, B = _t, C = _t, D = _t, E = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"No", Int64.Type}, {"Date", type date}, {"A", Int64.Type}, {"B", Int64.Type}, {"C", Int64.Type}, {"D", Int64.Type}, {"E", Int64.Type}}),
    #"Removed Columns" = Table.RemoveColumns(#"Changed Type",{"No"}),
    #"Unpivoted Columns" = Table.UnpivotOtherColumns(#"Removed Columns", {"Date"}, "Attribute", "Value"),
    #"Filtered Rows" = Table.SelectRows(#"Unpivoted Columns", each ([Value] = 1)),
    #"Grouped Rows1" = Table.Group(#"Filtered Rows", {"Attribute", "Date"}, {{"Count", each Table.RowCount(_), type number}}),
    #"Grouped Rows" = Table.Group(#"Unpivoted Columns", {"Attribute", "Date"}, {{"Count", each Table.RowCount(_), type number}}),
    #"Merged Queries" = Table.NestedJoin(#"Grouped Rows", {"Attribute", "Date"}, #"Grouped Rows1", {"Attribute", "Date"}, "Grouped Rows", JoinKind.Inner),
    #"Expanded Grouped Rows" = Table.ExpandTableColumn(#"Merged Queries", "Grouped Rows", {"Count"}, {"Grouped Rows.Count"}),
    #"Added Custom" = Table.AddColumn(#"Expanded Grouped Rows", "Custom", each [Grouped Rows.Count]/[Count]),
    #"Changed Type1" = Table.TransformColumnTypes(#"Added Custom",{{"Custom", Percentage.Type}}),
    #"Removed Columns1" = Table.RemoveColumns(#"Changed Type1",{"Count", "Grouped Rows.Count"}),
    #"Pivoted Column" = Table.Pivot(Table.TransformColumnTypes(#"Removed Columns1", {{"Date", type text}}, "en-US"), List.Distinct(Table.TransformColumnTypes(#"Removed Columns1", {{"Date", type text}}, "en-US")[Date]), "Date", "Custom")
in
    #"Pivoted Column"

Here are my test sample and result.

4-1.PNG4-2.PNG

Here is my test file for your reference.

 

Community Support Team _ Eads
If this post helps, then please consider Accept it as the solution to help the other members find it.

View solution in original post

Hi @ItoDiaz , I know it has already been answered but I liked your issue and i had a go at it.

 

Supposing dates change and also a product may be added or removed, I've made everything dynamic. I've used @v-eachen-msft's dummy table for the solution:

 

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMlTSUTIyMDLQNTDSNQBxDKDYEI5jdaKVjNCVYWKQMmNsygxQaJAyhKXGCEsxTTNCV4aMIe5EtdQYp6WxAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [No = _t, Date = _t, A = _t, B = _t, C = _t, D = _t, E = _t]),
    PreviousStep = Table.TransformColumnTypes(Source,{{"No", Int64.Type}, {"Date", type date}, {"A", Int64.Type}, {"B", Int64.Type}, {"C", Int64.Type}, {"D", Int64.Type}, {"E", Int64.Type}}),
    DateList = List.Transform(List.Distinct(PreviousStep[Date]), each {Text.From(_), Percentage.Type}),
    Columns = List.Select(Table.ColumnNames(PreviousStep), each _ <> "Date" and _ <> "No"),
    GroupList = List.Transform(Columns, each {_, Expression.Evaluate( "each List.Sum(["& _ & "]) / Table.RowCount(_)", [List.Sum  = List.Sum, Table.RowCount = Table.RowCount]), Percentage.Type}),
    #"Grouped Rows" = Table.Group(PreviousStep, {"Date"}, GroupList),
    #"Demoted Headers" = Table.DemoteHeaders(#"Grouped Rows"),
    #"Transposed Table" = Table.Transpose(#"Demoted Headers"),
    #"Promoted Headers" = Table.PromoteHeaders(#"Transposed Table", [PromoteAllScalars=true]),
    #"Renamed Columns" = Table.RenameColumns(#"Promoted Headers",{{"Date", "Product"}}),
    #"Changed Types" = Table.TransformColumnTypes(#"Renamed Columns", List.Combine({{{"Product", type text}}, DateList}))
in
    #"Changed Types"

 

Some explaining.

1) DateList is a list we'll later use to correct our datatypes. Dynamically taking the dates existing and creating a list which will be read for the type transformations.

2) Columns is a list of all our products, supposing here that the columns without products are [No] and [Date]

3) GroupList. Here is where the calculation actually happens. It's a list of list, each containing 3 elements: Name, function, type. This be read as new column calculations on the grouping step. Assuming that good is always 1 and bad is always 0 (no 0.5 values etc.), we calculate the % by suming all the values in every column and dividing by day's total count. I've also assumed there are no nulls.

The rest is a table transpose to actually have products as rows and dates as columns, with the last step being the column type correction.

 

This solution should also be faster, seeing that there is only one grouping and one transposition.

 

Cheers,

smauro




Feel free to connect with me:
LinkedIn

View solution in original post

5 REPLIES 5
v-eachen-msft
Community Support
Community Support

Hi @ItoDiaz ,

 

After importing the data, click "Transform data" to open qeury editor.

1. Remove "CHRONO" column.

2. Unpivot your parameter columns. You will get the "Attribute" column and the "Value" column.

3. Get a table with Value=1, calculate the row number.

4. Get a table with Value>=0, calculate the row number.

5. Merge these two tables, use division to get the percentage.

6. Remove useless columns.

7. Pivot date column.

Here is the codes in advanced editor.

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMlTSUTIyMDLQNTDSNQBxDKDYEI5jdaKVjNCVYWKQMmNsygxQaJAyhKXGCEsxTTNCV4aMIe5EtdQYp6WxAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [No = _t, Date = _t, A = _t, B = _t, C = _t, D = _t, E = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"No", Int64.Type}, {"Date", type date}, {"A", Int64.Type}, {"B", Int64.Type}, {"C", Int64.Type}, {"D", Int64.Type}, {"E", Int64.Type}}),
    #"Removed Columns" = Table.RemoveColumns(#"Changed Type",{"No"}),
    #"Unpivoted Columns" = Table.UnpivotOtherColumns(#"Removed Columns", {"Date"}, "Attribute", "Value"),
    #"Filtered Rows" = Table.SelectRows(#"Unpivoted Columns", each ([Value] = 1)),
    #"Grouped Rows1" = Table.Group(#"Filtered Rows", {"Attribute", "Date"}, {{"Count", each Table.RowCount(_), type number}}),
    #"Grouped Rows" = Table.Group(#"Unpivoted Columns", {"Attribute", "Date"}, {{"Count", each Table.RowCount(_), type number}}),
    #"Merged Queries" = Table.NestedJoin(#"Grouped Rows", {"Attribute", "Date"}, #"Grouped Rows1", {"Attribute", "Date"}, "Grouped Rows", JoinKind.Inner),
    #"Expanded Grouped Rows" = Table.ExpandTableColumn(#"Merged Queries", "Grouped Rows", {"Count"}, {"Grouped Rows.Count"}),
    #"Added Custom" = Table.AddColumn(#"Expanded Grouped Rows", "Custom", each [Grouped Rows.Count]/[Count]),
    #"Changed Type1" = Table.TransformColumnTypes(#"Added Custom",{{"Custom", Percentage.Type}}),
    #"Removed Columns1" = Table.RemoveColumns(#"Changed Type1",{"Count", "Grouped Rows.Count"}),
    #"Pivoted Column" = Table.Pivot(Table.TransformColumnTypes(#"Removed Columns1", {{"Date", type text}}, "en-US"), List.Distinct(Table.TransformColumnTypes(#"Removed Columns1", {{"Date", type text}}, "en-US")[Date]), "Date", "Custom")
in
    #"Pivoted Column"

Here are my test sample and result.

4-1.PNG4-2.PNG

Here is my test file for your reference.

 

Community Support Team _ Eads
If this post helps, then please consider Accept it as the solution to help the other members find it.

@v-eachen-msft 

 

It works. 

 

It helped me to undertand better the query M philosophy. 

 

With your code I have created weekly, monthly and yearly conformity queries. Now I will work in the comparison between current month and previous month and the creation of an "alarm" if the conformity rate is <80% in both months.  

 

H-WE

Hi @ItoDiaz , I know it has already been answered but I liked your issue and i had a go at it.

 

Supposing dates change and also a product may be added or removed, I've made everything dynamic. I've used @v-eachen-msft's dummy table for the solution:

 

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMlTSUTIyMDLQNTDSNQBxDKDYEI5jdaKVjNCVYWKQMmNsygxQaJAyhKXGCEsxTTNCV4aMIe5EtdQYp6WxAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [No = _t, Date = _t, A = _t, B = _t, C = _t, D = _t, E = _t]),
    PreviousStep = Table.TransformColumnTypes(Source,{{"No", Int64.Type}, {"Date", type date}, {"A", Int64.Type}, {"B", Int64.Type}, {"C", Int64.Type}, {"D", Int64.Type}, {"E", Int64.Type}}),
    DateList = List.Transform(List.Distinct(PreviousStep[Date]), each {Text.From(_), Percentage.Type}),
    Columns = List.Select(Table.ColumnNames(PreviousStep), each _ <> "Date" and _ <> "No"),
    GroupList = List.Transform(Columns, each {_, Expression.Evaluate( "each List.Sum(["& _ & "]) / Table.RowCount(_)", [List.Sum  = List.Sum, Table.RowCount = Table.RowCount]), Percentage.Type}),
    #"Grouped Rows" = Table.Group(PreviousStep, {"Date"}, GroupList),
    #"Demoted Headers" = Table.DemoteHeaders(#"Grouped Rows"),
    #"Transposed Table" = Table.Transpose(#"Demoted Headers"),
    #"Promoted Headers" = Table.PromoteHeaders(#"Transposed Table", [PromoteAllScalars=true]),
    #"Renamed Columns" = Table.RenameColumns(#"Promoted Headers",{{"Date", "Product"}}),
    #"Changed Types" = Table.TransformColumnTypes(#"Renamed Columns", List.Combine({{{"Product", type text}}, DateList}))
in
    #"Changed Types"

 

Some explaining.

1) DateList is a list we'll later use to correct our datatypes. Dynamically taking the dates existing and creating a list which will be read for the type transformations.

2) Columns is a list of all our products, supposing here that the columns without products are [No] and [Date]

3) GroupList. Here is where the calculation actually happens. It's a list of list, each containing 3 elements: Name, function, type. This be read as new column calculations on the grouping step. Assuming that good is always 1 and bad is always 0 (no 0.5 values etc.), we calculate the % by suming all the values in every column and dividing by day's total count. I've also assumed there are no nulls.

The rest is a table transpose to actually have products as rows and dates as columns, with the last step being the column type correction.

 

This solution should also be faster, seeing that there is only one grouping and one transposition.

 

Cheers,

smauro




Feel free to connect with me:
LinkedIn

@Smauro 

 

Thank you very much for your solution. I have applied your code, it works and as you thought it is faster. In my case 1' against 1'30'' for the first solution (important if you have a "lazy" laptop...)

 

Can I accept two solutions? I don't think so. 

 

Kind regards, 

 

ItoDiaz

Dear @v-eachen-msft ,

 

Thanks a lot for your advice. Your exemple seems answer to my questions.  

Now my turn, act (adapt) and see. I will tell you.

 

KR

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