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 :)
No, most often it is trial & error.
Only for List.Generate I will always use it for the input-tables or -lists to the function
Maybe it's too early in the morning that I tackled this, but I can't yet visualize how to flatten this data.
What I start with is as follows:
| Column10 | Column3 | Column14 |
| 18/05/2017 | As75 | 489.044189453125 |
| 18/05/2017 | As75 | 529.010314941406 |
| 18/05/2017 | As75 | 40914.140625 |
| 18/05/2017 | As75 | 3145.38793945313 |
| 18/05/2017 | As75 | 43844.9296875 |
| 18/05/2017 | Ca44 | 2365.14038085938 |
| 18/05/2017 | Ca44 | 20024.193359375 |
| 18/05/2017 | Ca44 | 6499.56396484375 |
| 18/05/2017 | Ca44 | 49550.2265625 |
| 18/05/2017 | Ca44 | 125394.3984375 |
| 18/05/2017 | Cu65 | 818.863037109375 |
| 18/05/2017 | Cu65 | 120588.8828125 |
| 18/05/2017 | Cu65 | 5401.640625 |
| 18/05/2017 | Cu65 | 1566.38427734375 |
| 18/05/2017 | Cu65 | 119667.9921875 |
What I would like to do is turn it into
| Date | As75 | Ca44 | Cu65 |
| 18/05/2017 | 489.044189453125 | 2365.14038085938 | 818.863037109375 |
| 18/05/2017 | 529.010314941406 | 20024.193359375 | 120588.8828125 |
| 18/05/2017 | 40914.140625 | 6499.56396484375 | 5401.640625 |
| 18/05/2017 | 3145.38793945313 | 49550.2265625 | 1566.38427734375 |
| 18/05/2017 | 43844.9296875 | 125394.3984375 | 119667.9921875 |
Any ideas?
- ImkeF9 years ago
Community Champion
Thats actually not trivial if you want to make it dynamic. Check out this code:
let Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("fZE7DsNQCATv4hopfBYelFGOYbnIGaLcP9hJOj9XNKNhYdd1kbyx35RlLLTcX8N7IIsYkCy4ifqy0Tno2qCwCQoCjikILgHtyIWtNU6Wo+zYa3ObJUClFTlOdI8n0EMtfF9pyellOQeZtcOVWWMXwkAVeVgFElcgyp1JNfz03B/Vj+1DyWome8d+bEpShrEN4Um8LyjKns2m5mlnP8zBQjFp4q/yiG4COoZdhhOpiEFVKkcV2wc=", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [Column10 = _t, Column3 = _t, Column14 = _t]), #"Changed Type" = Table.TransformColumnTypes(Source,{{"Column10", type date}, {"Column3", type text}, {"Column14", type number}}), #"Added Index" = Table.AddIndexColumn(#"Changed Type", "Index", 0, 1), #"Added to Column" = Table.TransformColumns(#"Added Index", {{"Index", each Number.Mod(_, List.Count(#"Added Index"[Column3])/List.Count(List.Distinct(#"Added Index"[Column3]))), type number}}), #"Pivoted Column" = Table.Pivot(#"Added to Column", List.Distinct(#"Added to Column"[Column3]), "Column3", "Column14"), #"Removed Columns" = Table.RemoveColumns(#"Pivoted Column",{"Index"}) in #"Removed Columns"If you don't know how to apply this code, check out this video: https://www.youtube.com/watch?v=S9xlq5KUZ60
- ImkeF9 years ago
Community Champion
There is a more elegant version for it, which should also perform faster:
let Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("fZExDsMwCEXv4hlFgAHDWPUYUYaeoer96ypul0Anhv/gf2DfG/mGujHSaNBuz6GziAegtANyWXnKVMqCQQJU93cShR71gO4iEP0K3B8is3A3TQ2+OiLPBInDAkwiQK3UJVQRuE5ArD0EBBPiZZ8lnBw8czhlYlSfhJeEChJYtuSaoGbQ636iMBsQ6wrxJwN7yawUaKwJs45xJhEe48qsn/7SMLfjeAM=", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [Column10 = _t, Column3 = _t, Column14 = _t]), Group = Table.Group(Source, {"Column10", "Column3"}, {{"All", each _[Column14], type table}}), ToColumns = Table.Group(Group, {"Column10"}, {{"All", each Table.FromColumns(_[All], _[Column3]), type table}}), Expand = Table.ExpandTableColumn(ToColumns, "All", List.Distinct(Group[Column3])) in Expand