The first solution (looking obvious) for this task that I found after some googling and thinking was using of “append queries” method. merge “Date” and “Sum” columns. When we look at result, we can see that our data from “repeating columns” become “repeating rows”: Unpivot “Inserted Modulo” column (select “Value” column as values and do not aggregate). About 80 columns was in repeating groups, named “Date”, “Sum”, “Date”, “Sum” …. No luck, Any suggestions would be appreciated. And standard unpivoting methods then do not works then. For some reasons, when a VBA solution couldn’t be applied (especially if you work in Power BI and want to pull data in it without voodoo dances), the only way to flatten this table was using the Power Query for Excel 2010-2013 (or “Get Data” in Power BI, “Get & Transform” in Excel 2016). For demonstration purposes I prepared a sample workbook, where source is in named range “UglyTable” (I was ruthless). #"Inserted Modulo"]), "Inserted Modulo", "Value"), Using the header of the report as the data for table columns in Power Query, BIN2DEC in Power Query: Implementations of List.Accumulate. Also, they are built once all Power Query tables are loaded, which can result in extended data refresh times. Hi Jerry Then create a … By default Group By happens on the selected columns. Group By Function in Power BI The idea is simple: as we cannot just unpivot this table because we have more than one column of the same data, we merge these columns in one, and then we get a pack of columns that can be unpivoted in usual way. Click here to read more about the December 2020 Updates! We can also rename this column to ID. But when I saw this “database” which he proudly presented to me, I was glad that the customer does not see my face and did not hear any comments passed my lips. Can you provide some guidance? Table.SelectColumns. What we have in source and what we need to get shown on the picture below: Actually we need to stack repeating groups one under other, keeping in place the common data from first columns. I love this trick – it is really interesting and not obvious. UnpivotDateSum = Table.UnpivotOtherColumns(Promoted, {"Customer", "Cust.Id"}, "Attribute", "Value"), I only want to keep the tuple with the most recent Changed Date of each ID. But “grouping to records” method is more technological and fast, so if you don’t afraid to edit M code, this is recommended approach. Choose the Group By Field Double click on the column heading to rename it. Now each pair of row has the same corresponding number. However, the Index column creates the row number regardless of any grouping or categorization. I like the looks of “Grouping by Indexes”. But in some case, we meet with this horror. Stacking (or unpivoting) non-nested groups of columns is not a common task, but sometimes it appears. You select Advanced only when you want to group by multiple variables or when you want to perform an operation on more than one variable. This week, I look at merging columns to show that not all ‘Merge Columns’ options are born equal. First group by at the lowest level, next group by the higher level, adding aggregation "all rows": This is the generated code, in which Value.Type (#"Grouped Rows") in step #"Grouped Rows1" is a manual replacement of the generated type table. That’s because Power BI is all about columns and tables. Power Query is known as Get & Transform in Excel 2016. This is the key step. Source = Excel.CurrentWorkbook(){[Name="UglyTable"]}[Content], Another interesting thing about Power BI is that we can’t change anything individually in any particular cell or row. I like your trick with Pivot+FillDown here – it seems not so obvoius, but very nice. My problem is with using the TABLE.GROUP( ) I have 3 columns: Year, Country, Score. Trick is to pivot without aggregation. Part 1, https://1drv.ms/x/s!AgBCQXHKKcyHg1luSpn4pu8j5Ddr, Power Query Connection to Excel PowerPivot Data Model, Classical Incremental Refresh For Cloud Data Sources in Power BI Service (for Pro accounts), Video: Semi-incremental refresh for Pro accounts in Power BI, Incremental Refresh For Pro Accounts With Power BI Service Dataflows. Now we only need to remove “Integer Divide” column and rename columns “0” and “1” to “Date” and “Sum”, and apply appropriate data types. In the previous article we looked at merging two tablesusing a single field to make the link. Sort data in the order that you want to index. You can download it here. This would be the best option as the dataset can be variable. Nicely done! A modulo column takes the values of an existing column, divides them by a specified amount and returns the remainder. Click on data tab in power query and then click on from table option. But this approach also has a limitation, the same as for previous method: if number of columns changed, you need to manually add or remove steps of columns merging. RemoveCols = Table.RemoveColumns(Filter,{"Attribute.2"}), You can download it here. Select a cell in the Table and click Data -> From Table/Range to load the data into Power Query. So let us use some trick with index column: Here is the full M code of this method from sample workbook: This method could be applied on tables with any number of paired columns. Extract first group of columns (included common columns and first repeating group of columns). Initially my data was 4 tables, but I have merged the Hostname table with the staff table and removed staff rows for which I didn't know the hostname. The problem is that I lose the country column. 3. Please use Group By in Edit Queries using Advanced Option, Group by column as ID and MAX of Changed Date, and All rows with Column name as "AllRows". score 1 score 2 score 3 A 6 25 50 B 8 30 20 C 15 15 30 D 20 0 10 I want to add a totals row (equivalent to "show totals for column" in a normal pivot table), so result would be like this. ; columns: The list of columns from the table table to return. And all we need then is just split data to separated columns. The algorithm used is simple, but very interesting. The solution I came up with so far is to split the data per month and then merge it together since pivoting data is working flawlessly when the dataset is small, but once it gets bigger the reload time takes a long time (5min+). Also for this “group to records” trick doesn’t matter, how many columns we will unpivot and group: 4, 8, 98, etc., so this approach is more versatile than first two. The VertiPaq storage engine stores model calculated columns (defined in DAX) just like regular Power Query-sourced columns. And the main lack of this method is that whether number of groups changed, you need to add/remove some queries and edit last step (appending). Note: The Query Editor only appears when you load, edit, or create a new query using Power Query.The following video shows the Query Editor window appearing after editing a query from an Excel workbook. Repeat this step for each pair of columns shall be stacked. Use Power Query's Query Editor to import data from a local Excel file that contains product information, and from an OData feed that contains product order information. When performing it with UI only, you need to create as much queries as number of repeating groups you have, and then append queries one by one in corresponding number of steps. There is a little Read more about Create Row Number for Each Group in Power BI using Power Query[…] In Power Query: Right-Click Category column and select Unpivot Other Columns. But what if a table we need to make flat has no nested headers for repeating groups of columns? So the algorithm for this method is in next steps: Here is a simple code in M language to perform this transformation: Although it can be done with UI on all stages, this method has a lot of manual work. Again, we need to go to the Add Column tab and press the Index Column command to create a new index column starting From 1. Click ok. Now the table will have ID, Date, AllRows columns. By telling Power Query to add a column using the All Rows operation, it adds a Table object to each row of the resulting column. Once in the Power Query Editor, the Group By feature is the first icon on the Transform tab: Transform -> Group By. 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