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justlogmein
Frequent Visitor

What is the 'proper' way to arrange my data and what is the code required?

I have a table where nearly all the headers are dates. This data source changes dynamically and the dates will also change, so if my queries (such as Change Type) reference the current headers I will get errors when the data source changes.  However, the rows in the Person column may also change, so I can't use them as headers either. So I am looking for a robust way to arrange the table so I don't get errors (I'm thinking Person, Date and Value as the column names?) I'd like to know if there are any better ways of doing this, and what the code for doing so would be.

 

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 [Person = _t, #"19/03/2021" = _t, #"20/03/2021" = _t, #"21/03/2021" = _t, #"22/03/2021" = _t, #"23/03/2021" = _t, #"24/03/2021" = _t, #"25/03/2021" = _t, #"26/03/2021" = _t, #"27/03/2021" = _t, #"28/03/2021" = _t, #"29/03/2021" = _t, #"30/03/2021" = _t, #"31/03/2021" = _t, #"1/04/2021" = _t, #"2/04/2021" = _t, #"3/04/2021" = _t, #"4/04/2021" = _t, #"5/04/2021" = _t, #"6/04/2021" = _t, #"7/04/2021" = _t, #"8/04/2021" = _t, #"9/04/2021" = _t, #"10/04/2021" = _t, #"11/04/2021" = _t, #"12/04/2021" = _t, #"13/04/2021" = _t, #"14/04/2021" = _t, #"15/04/2021" = _t, #"16/04/2021" = _t, #"17/04/2021" = _t, #"18/04/2021" = _t, #"19/04/2021" = _t, #"20/04/2021" = _t, #"21/04/2021" = _t, #"22/04/2021" = _t, #"23/04/2021" = _t, #"24/04/2021" = _t, #"25/04/2021" = _t, #"26/04/2021" = _t, #"27/04/2021" = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Person", type text}, {"19/03/2021", Int64.Type}, {"20/03/2021", Int64.Type}, {"21/03/2021", Int64.Type}, {"22/03/2021", Int64.Type}, {"23/03/2021", Int64.Type}, {"24/03/2021", Int64.Type}, {"25/03/2021", Int64.Type}, {"26/03/2021", Int64.Type}, {"27/03/2021", Int64.Type}, {"28/03/2021", Int64.Type}, {"29/03/2021", Int64.Type}, {"30/03/2021", Int64.Type}, {"31/03/2021", Int64.Type}, {"1/04/2021", Int64.Type}, {"2/04/2021", Int64.Type}, {"3/04/2021", Int64.Type}, {"4/04/2021", Int64.Type}, {"5/04/2021", Int64.Type}, {"6/04/2021", Int64.Type}, {"7/04/2021", Int64.Type}, {"8/04/2021", Int64.Type}, {"9/04/2021", Int64.Type}, {"10/04/2021", Int64.Type}, {"11/04/2021", Int64.Type}, {"12/04/2021", Int64.Type}, {"13/04/2021", Int64.Type}, {"14/04/2021", Int64.Type}, {"15/04/2021", Int64.Type}, {"16/04/2021", Int64.Type}, {"17/04/2021", Int64.Type}, {"18/04/2021", Int64.Type}, {"19/04/2021", Int64.Type}, {"20/04/2021", Int64.Type}, {"21/04/2021", Int64.Type}, {"22/04/2021", Int64.Type}, {"23/04/2021", Int64.Type}, {"24/04/2021", Int64.Type}, {"25/04/2021", Int64.Type}, {"26/04/2021", Int64.Type}, {"27/04/2021", Int64.Type}})
in
    #"Changed Type"

 

1 ACCEPTED SOLUTION
Fowmy
Super User IV
Super User IV

@justlogmein 

Please check this version. This way it will be very useful for your analysis

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 [Person = _t, #"19/03/2021" = _t, #"20/03/2021" = _t, #"21/03/2021" = _t, #"22/03/2021" = _t, #"23/03/2021" = _t, #"24/03/2021" = _t, #"25/03/2021" = _t, #"26/03/2021" = _t, #"27/03/2021" = _t, #"28/03/2021" = _t, #"29/03/2021" = _t, #"30/03/2021" = _t, #"31/03/2021" = _t, #"1/04/2021" = _t, #"2/04/2021" = _t, #"3/04/2021" = _t, #"4/04/2021" = _t, #"5/04/2021" = _t, #"6/04/2021" = _t, #"7/04/2021" = _t, #"8/04/2021" = _t, #"9/04/2021" = _t, #"10/04/2021" = _t, #"11/04/2021" = _t, #"12/04/2021" = _t, #"13/04/2021" = _t, #"14/04/2021" = _t, #"15/04/2021" = _t, #"16/04/2021" = _t, #"17/04/2021" = _t, #"18/04/2021" = _t, #"19/04/2021" = _t, #"20/04/2021" = _t, #"21/04/2021" = _t, #"22/04/2021" = _t, #"23/04/2021" = _t, #"24/04/2021" = _t, #"25/04/2021" = _t, #"26/04/2021" = _t, #"27/04/2021" = _t]),
    #"Unpivoted Other Columns" = Table.UnpivotOtherColumns(Source, {"Person"}, "Attribute", "Value"),
    #"Changed Type" = Table.TransformColumnTypes(#"Unpivoted Other Columns",{{"Value", Int64.Type}, {"Attribute", type date}},"en-gb"),
    #"Renamed Columns" = Table.RenameColumns(#"Changed Type",{{"Attribute", "Date"}})
in
    #"Renamed Columns"

 

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1 REPLY 1
Fowmy
Super User IV
Super User IV

@justlogmein 

Please check this version. This way it will be very useful for your analysis

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 [Person = _t, #"19/03/2021" = _t, #"20/03/2021" = _t, #"21/03/2021" = _t, #"22/03/2021" = _t, #"23/03/2021" = _t, #"24/03/2021" = _t, #"25/03/2021" = _t, #"26/03/2021" = _t, #"27/03/2021" = _t, #"28/03/2021" = _t, #"29/03/2021" = _t, #"30/03/2021" = _t, #"31/03/2021" = _t, #"1/04/2021" = _t, #"2/04/2021" = _t, #"3/04/2021" = _t, #"4/04/2021" = _t, #"5/04/2021" = _t, #"6/04/2021" = _t, #"7/04/2021" = _t, #"8/04/2021" = _t, #"9/04/2021" = _t, #"10/04/2021" = _t, #"11/04/2021" = _t, #"12/04/2021" = _t, #"13/04/2021" = _t, #"14/04/2021" = _t, #"15/04/2021" = _t, #"16/04/2021" = _t, #"17/04/2021" = _t, #"18/04/2021" = _t, #"19/04/2021" = _t, #"20/04/2021" = _t, #"21/04/2021" = _t, #"22/04/2021" = _t, #"23/04/2021" = _t, #"24/04/2021" = _t, #"25/04/2021" = _t, #"26/04/2021" = _t, #"27/04/2021" = _t]),
    #"Unpivoted Other Columns" = Table.UnpivotOtherColumns(Source, {"Person"}, "Attribute", "Value"),
    #"Changed Type" = Table.TransformColumnTypes(#"Unpivoted Other Columns",{{"Value", Int64.Type}, {"Attribute", type date}},"en-gb"),
    #"Renamed Columns" = Table.RenameColumns(#"Changed Type",{{"Attribute", "Date"}})
in
    #"Renamed Columns"

 

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