Forum Discussion
Flattening multiple related rows in Power Query
- 9 years ago
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 :)
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.
- jPinhao9 years agoAdvocate 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 :)
- ImkeF9 years agoCommunity Champion
Hi jPinhao, that's pretty cool!
Wasn't aware that FillUp is even slower than pivoting :-)
You can further play around with List or Table.Buffer to see if this speeds it up even more.
- jPinhao9 years agoAdvocate II
Yea, I thought it was curious too :) But again, I'm not sure if that approach would scale better than pivoting, it might be a case of one approach being better in particular scenarios.
I do intend to play with Buffering at some point to see where it can help improve performance. Do you know of any general rules where using Buffer will help?
- jfclark277 years agoRegular 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.
- ImkeF7 years agoCommunity 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/
- jfclark277 years agoRegular Visitor
You are so awesome. I adapted your code to my Query, and it works perfect!!!
I do have a couple columns that will need a MAX aggrigation, but I'm pretty sure I can do another List.Transform, then List.Combine then run the new list through Table.Group.
I look forward to a new pot of coffee and the rabbit hole you have showed me. Time to see how deep it goes :smileyvery-happy: