Forum Discussion
Flattening multiple related rows in Power Query
We are loading some JSON data, which holds some arrays with varying property-value pairs. Due to the JSON structure, once you start expanding the fields you end up with something along these lines:
id | key1 | key2 | key3 | key4 0 | a | null | null | null 0 | null | b | null | null 0 | null | null | c | null 1 | a | null | null | null 1 | null | b | null | null 1 | null | null | c | null 1 | null | null | null | d
We'd like to flatten these rows by ID to end up with something like:
id | key1 | key2 | key3 | key4 0 | a | b | c | null 1 | a | b | c | d
The simplest way I found to solve this is by unpivotting the key columns, and then pivotting it back. However in some cases, we actually have quite a few more columns, and thousands of rows, in a quite sparse table. From previous trials I found that unpivot/pivot can be a big performance hit.
Is there a better way to solve this issue? Either in Power Query or with an R script?
EDIT: I beleive I found a way to do this with Table.Group:
Table.Group(sourceTable, {"id"}, {{"key1", each List.RemoveNulls([key1]){0}, type none}, {"key2", ...}, ...})I'd like to do this across all columns (except ID), or from a List of column names - how would I go about making Table.Group work from a list of values dynamically?
Thanks for the replies! I actually managed to solve this on my own in the end :)
ImkeF - your solution is interesting, I'm guessing FillUp will find the bottom-most non-null value and fill any rows above with it?
My solution - wrote a function that will find and return the first non-null value in a list (or a default if all null), and use that as the aggregator. I then build a list from the original list of column names that will run the operation on each named column:
//FirstNotNull let Source = (sourceList as list) => let firstNotNull = List.First(List.RemoveNulls(sourceList), "Not Applicable") in firstNotNull in Source //DynamicTableGroupColumns let Source = (sourceTable as table, columns as list, aggregateFunction as function) => let result = List.Transform(columns, each // build lists with {columnName, aggregateFunction} let //save current _ (column name) to use in next each statement columnName = _, columnToFunctionList = {columnName, each //_ will be the grouping table as it's called by Table.Group aggregateFunction(Table.Column(_, columnName))} in columnToFunctionList) in result in Source //DynamicTableGroup let Source = (sourceTable as table, groupBy as list, columns as list, aggregateFunction as function) => let result = Table.Group(sourceTable , groupBy, DynamicTableGroupColumns(sourceTable, columns, aggregateFunction)) in result in SourceAny comments on one method being better than the other? Will your method of FillUp into a single column and then expanding the relevant fields be more performant that preparing a list of lists to feed to Table.Group?
EDIT: ImkeF just timed the 2 queries, and filling up into one column and then expanding seemed to take 2min20s, while my approach took 58s! Yesterday I had also timed doing an unpivot/pivot over all columns, and that was taking about 1min45s. I'm not sure how the unpivot/pivot scales with more columns and rows, but I'd assume our 2 methods would scale similarly.
Feel free to use the set of functions I put up in case you find use for them to speed up any queries! Or let me know if don't see similar results :)
16 Replies
- ImkeF
Community Champion
A possible solution with dynamic column headers is this:
let Source = YourTable, #"Grouped Rows" = Table.Group(Source, {"id"}, {{"FillUp", each Table.FirstN(Table.FillUp(_,Table.ColumnNames(_)), 1), type table}}), #"Expanded FillUp" = Table.ExpandTableColumn(#"Grouped Rows", "FillUp", List.Skip(Table.ColumnNames(Source),1), List.Skip(Table.ColumnNames(Source),1)) in #"Expanded FillUp"It pushes all items to the first row and then just keeps that.
- jPinhao
Advocate II
Thanks for the replies! I actually managed to solve this on my own in the end :)
ImkeF - your solution is interesting, I'm guessing FillUp will find the bottom-most non-null value and fill any rows above with it?
My solution - wrote a function that will find and return the first non-null value in a list (or a default if all null), and use that as the aggregator. I then build a list from the original list of column names that will run the operation on each named column:
//FirstNotNull let Source = (sourceList as list) => let firstNotNull = List.First(List.RemoveNulls(sourceList), "Not Applicable") in firstNotNull in Source //DynamicTableGroupColumns let Source = (sourceTable as table, columns as list, aggregateFunction as function) => let result = List.Transform(columns, each // build lists with {columnName, aggregateFunction} let //save current _ (column name) to use in next each statement columnName = _, columnToFunctionList = {columnName, each //_ will be the grouping table as it's called by Table.Group aggregateFunction(Table.Column(_, columnName))} in columnToFunctionList) in result in Source //DynamicTableGroup let Source = (sourceTable as table, groupBy as list, columns as list, aggregateFunction as function) => let result = Table.Group(sourceTable , groupBy, DynamicTableGroupColumns(sourceTable, columns, aggregateFunction)) in result in SourceAny comments on one method being better than the other? Will your method of FillUp into a single column and then expanding the relevant fields be more performant that preparing a list of lists to feed to Table.Group?
EDIT: ImkeF just timed the 2 queries, and filling up into one column and then expanding seemed to take 2min20s, while my approach took 58s! Yesterday I had also timed doing an unpivot/pivot over all columns, and that was taking about 1min45s. I'm not sure how the unpivot/pivot scales with more columns and rows, but I'd assume our 2 methods would scale similarly.
Feel free to use the set of functions I put up in case you find use for them to speed up any queries! Or let me know if don't see similar results :)
- jfclark27Regular Visitor
I'm late to the party here, but was wondering if you could help me. I'm trying to use the functions OP posted above. Unfortunately, I cannot figure out how to call them in my query.
I have a list containing my "groupBy" columns
I have a list containing my "columns" I'd like summed
I just cant figure out the "aggregateFunction" argument.
Do you know how to actually use these functions in a query to List.Sum the dynamic columns? If I can impliment these functions, it would save me a lot of steps and an expensive unpivot.
- ImkeF
Community Champion
Sounds to me that what you jfclark27 are asking for is a bit different. Does this code do the job?:
let GroupColumns = {"Group"}, SumColumn = {"Col1", "Col2"}, AggregationFunctions = List.Transform(SumColumn, each {_, (x)=> List.Sum(Table.Column(x,_)), type number}), Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WclTSUTIEYmOlWB0IzwiITcA8JyDLHIhN4TwLIDZTio0FAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [Group = _t, Col1 = _t, Col2 = _t]), #"Changed Type" = Table.TransformColumnTypes(Source,{{"Group", type text}, {"Col1", Int64.Type}, {"Col2", Int64.Type}}), DynamicAggregation = Table.Group(#"Changed Type", GroupColumns, AggregationFunctions) in DynamicAggregationThe tricky part is how to create the AggregationFunction:
List.Transform(SumColumn, each {_, (x)=> List.Sum(Table.Column(x,_)), type number})
There you have to work with different environments: The _ represents each element from your list with columns to be aggregated ("SumColumn") and will actually be "used" in the step "AggregationFunction", while the "x" represents the table that will be passed into the function once it is called in step "DynamicAggregation".
If you are interested to learn more about Power Query's environment-concept, I recommend this article-series: http://ssbi-blog.de/technical-topics-english/the-environment-concept-in-m-for-power-query-and-power-bi-desktop-part-1/
- v-haibl-msft
Microsoft Employee
Please try with following Power Query in Advanced Editor.
let Source = Excel.Workbook(File.Contents("C:\11032016\Flattening multiple related rows in Power Query.xlsx"), null, true), Table1_Table = Source{[Item="Table1",Kind="Table"]}[Data], #"Changed Type" = Table.TransformColumnTypes(Table1_Table,{{"id", Int64.Type}, {"key1", type text}, {"key2", type text}, {"key3", type text}, {"key4", type text}}), #"Combine" = Combiner.CombineTextByDelimiter(""), #"GroupRows" = Table.Group( #"Changed Type", {"id"}, {{"key1", each Combine([key1]), type text}, {"key2", each Combine([key2]), type text}, {"key3", each Combine([key3]), type text}, {"key4", each Combine([key4]), type text}} ) in #"GroupRows"Best Regards,
Herbert