I have two tables.
Table1
Category
A
B
...
Table2
Companies | Indistries
1 | A,D,X
2 | Z,B,X
3 | N,D,R,B,Q
I would like to have a slicer with different categories (A-Z). When clicking A all Diagrams should be filtered according to the Companies that "contain" industry A.
Long story short: it would be like a normal relationship but instead of finding the same, it would be a "contains".
Thank you for your help! Really appreciated.
Please down load the sample report file from link - HERE
Follow-
Created a Index column in table your_table_name
Created a new table slicer_new with this below code-
let
Source = your_table_name,
#"Split Column by Delimiter" = Table.SplitColumn(Source, "Indistries", Splitter.SplitTextByDelimiter(", ", QuoteStyle.Csv), {"Indistries.1", "Indistries.2", "Indistries.3", "Indistries.4"}),
#"Changed Type" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"Indistries.1", type text}, {"Indistries.2", type text}, {"Indistries.3", type text}, {"Indistries.4", type text}}),
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(#"Changed Type", {"Index"}, "Attribute", "Value"),
#"Removed Columns" = Table.RemoveColumns(#"Unpivoted Other Columns",{"Attribute"})
in
#"Removed Columns"
Here below is the final output of slicer_new table-
Get back to report and check the relation between table your_table_name and slicer_new.
Create slicer from table slicer_new
Add table visual for column Indistries from table your_table_name
Now select value in slicer, everything should work as expected now.
Related
Hello beautiful people,
Could someone please help me with my below request, (noting that I am working with Power Query Editor). So I need it to be done using creating conditional columns in power query maybe?, please help.
I need to group users in a table based on a category with showing their count in multiple different fields, As per the below example:
I need results to be:
Muuuuuuuuch Appreciated
In powerquery, try this
Click select period and name columns
Right click, unpivot other columns
Click select period, attribute and value columns
Right click, group ... new column name Count, operation Count rows
Click select attribute column. Transform ... pivot column... values column Count, Advanced options, do not aggregate
let Source = Excel.CurrentWorkbook(){[Name="Table1"]}[Content],
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(Source, {"Period", "Name"}, "Attribute", "Value"),
#"Grouped Rows" = Table.Group(#"Unpivoted Other Columns", {"Period", "Attribute", "Value"}, {{"Count", each Table.RowCount(_), type number}}),
#"Pivoted Column" = Table.Pivot(#"Grouped Rows", List.Distinct(#"Grouped Rows"[Attribute]), "Attribute", "Count", List.Sum)
in #"Pivoted Column"
Alternately,
Click select period and name columns
Right click, unpivot other columns
Right click name column and remove
Right click value column and duplicate
Click select attribute column .. Transform pivot column ... Values column:Values Copy
let Source = Excel.CurrentWorkbook(){[Name="Table1"]}[Content],
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(Source, {"Period", "Name"}, "Attribute", "Value"),
#"Removed Columns" = Table.RemoveColumns(#"Unpivoted Other Columns",{"Name"}),
#"Duplicated Column" = Table.DuplicateColumn(#"Removed Columns", "Value", "Value - Copy"),
#"Pivoted Column" = Table.Pivot(#"Duplicated Column", List.Distinct(#"Duplicated Column"[Attribute]), "Attribute", "Value - Copy", List.Count)
in #"Pivoted Column"
Neither one is going to put in a blank row for a combination that does not exist like Dec No. Thats more complicated if required
There is a scenario as attached excel file, an issue progress daily tracker.
In Power BI, if I use a table visual, and want to only show the latest update(in this case will be 11/30/2018), how could I let it be done auto instead of selecting the date column manually?
You'd be better unpivoting that source data and storing the date as a value of a "date" column, rather than having separate columns for each date. Then you can use all the DAX time intelligence functionality.
You could use a query like this, to normalise your source data:
let
Source = MyTable,
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(Source, {"#", "Issue", "Owner"}, "Date", "Value"),
#"Changed Type" = Table.TransformColumnTypes(#"Unpivoted Other Columns",{{"#", Int64.Type}, {"Issue", type text}, {"Owner", type text}, {"Value", type text}, {"Date", type date}}),
#"Filtered Rows" = Table.SelectRows(#"Changed Type", each [Value] <> null and [Value] <> "")
in
#"Filtered Rows"
Now your table view can be filtered using the Date field - if you want the latest date, then a Top 1 by Latest Date filter would work:
In Power BI, I have a table that looks like this:
ID
234
435
3435
58
48504
7820
I want to convert it to a table that looks like this:
ID
234-101
234-102
435-101
435-102
3435-101
343-102
58-101
58-102
48504-101
48504-102
7820-101
7820-102
Is this even possible within Power BI?
I thought of two ways to do this, though there are probably others.
NOTE - I prefer the second method as it lets the "101" and "102" be data driven allowing them to be changed or added to in the future more easily.
A) Through the Query Editor (requires hard-coding the "101"/"102" values)
Step 1: Start with your data in the Query Editor
Step 2: Add two additional columns for your suffixes. Click on the "Custom Column from Examples" button and then type in "234-101" in the first cell. After arrowing down to the next cell, it should auto-populate the rest. Do this again for "-102".
Step 3: Unpivot the two new columns to get them into one. With the "ID" column selected, click on the dropdown for "Unpivot Columns" and click on "Unpivot Other Columns".
Step 4: Remove extra columns. In the resulting data, you will have the original "ID" column, along with two new ones; "Attribute" and "Value". Since the "Value" column contains the desired values, select the "ID" and "Attribute" columns, right click one of their headers, and select "Remove Columns".
Step 5: Rename the "Value" column to "ID" and you're finished.
Here is the resulting M code for all of those actions.
let
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMjI2UYrViVYyMTYF08YwhqkFRNzC1ACiwtzCyEApNhYA", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [ID = _t]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"ID", Int64.Type}}),
#"Inserted Merged Column" = Table.AddColumn(#"Changed Type", "Merged", each Text.Combine({Text.From([ID], "en-US"), "-101"}), type text),
#"Inserted Merged Column1" = Table.AddColumn(#"Inserted Merged Column", "Merged.1", each Text.Combine({Text.From([ID], "en-US"), "-102"}), type text),
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(#"Inserted Merged Column1", {"ID"}, "Attribute", "Value"),
#"Removed Columns" = Table.RemoveColumns(#"Unpivoted Other Columns",{"ID", "Attribute"}),
#"Renamed Columns" = Table.RenameColumns(#"Removed Columns",{{"Value", "ID"}})
in
#"Renamed Columns"
B) Through DAX
Step 1: Start with your data in the data view.
Step 2: Click on "Enter Data" and add the data for the suffixes. (Skip this if these numbers are sourced somewhere else)
Step 3: Click on "New Table" and enter the following formula.
NewData = CROSSJOIN(Data, Suffixes)
Step 4: Click on "New Column and enter the following formula.
NewID = CONCATENATE(CONCATENATE(NewData[ID], "-"), NewData[Value])
If you want the new column to be named "ID", you'll need to rename the old "ID" column first, as you can't simply remove it like was done in the first method.
If you're okay with using Power BI's query editor (Power Query) for this, you can do it with this query code:
let
Source = Table1,
#"Inserted Merged Column3" = Table.AddColumn(Source, "DelimitedListWithSuffixes", each Text.Combine({[ID], "-101,", [ID],"-102"}), type text),
#"Split Column by Delimiter" = Table.ExpandListColumn(Table.TransformColumns(#"Inserted Merged Column3", {{"DelimitedListWithSuffixes", Splitter.SplitTextByDelimiter(",", QuoteStyle.Csv), let itemType = (type nullable text) meta [Serialized.Text = true] in type {itemType}}}), "DelimitedListWithSuffixes"),
#"Changed Type" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"DelimitedListWithSuffixes", type text}}),
#"Removed Other Columns" = Table.SelectColumns(#"Changed Type",{"DelimitedListWithSuffixes"}),
#"Renamed Columns" = Table.RenameColumns(#"Removed Other Columns",{{"DelimitedListWithSuffixes", "ID"}})
in
#"Renamed Columns"
(Table1 is your original ID column table.)
I need to sum all values in each row and display them in a calculated column. As I deal with lots of columns in lots of tables, adding something like
CalculatedColumn = 'public table_name'[column1] + 'public table_name'[column2] + ... + 'public table_name'[column528]
is really inefficient. Is there a shorter way of doing this?
Yes, there is. You should "Unpivot other columns" and then "Group By" using the Query Editor.
Suppose this dataset:
item;col1;col2;col3;col4;col5
apple;1;2;3;4;5
orange;1;2;3;5;8
banana;1;2;4;6;8
Load it up, and open the query editor.
Choose "Unpivot Other Columns":
You should now see this:
On the "Transform" tab in the ribbon, choose the leftmost "Group By" option. And fill out the dialog like so:
You should now have the wanted end result:
You could also skip the Group By step and let your visualization handle that.
PS. Should you need a few non-summed columns too I recommend either creating a duplicate dataset with the same source and either linking it to the original table with a relationship, or merging it so you get a final table with all wanted columns.
Footnote, this is the Power Query that is generated for you:
let
Source = Csv.Document(File.Contents("D:\Experiments\PowerBi\denormalized.csv"),[Delimiter=";", Columns=6, Encoding=1252, QuoteStyle=QuoteStyle.None]),
#"Promoted Headers" = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),
#"Changed Type" = Table.TransformColumnTypes(#"Promoted Headers",{{"item", type text}, {"col1", Int64.Type}, {"col2", Int64.Type}, {"col3", Int64.Type}, {"col4", Int64.Type}, {"col5", Int64.Type}}),
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(#"Changed Type", {"item"}, "Attribute", "Value"),
#"Grouped Rows" = Table.Group(#"Unpivoted Other Columns", {"item"}, {{"SumCol", each List.Sum([Value]), type number}})
in
#"Grouped Rows"
I'm an Excel VBA programmer who's recently discovered Power Query and Power BI.
Often, I will receive requests to change something like:
Into a sort of un-pivoted version that looks like:
This can absolutely be done in VBA, but watching several Power Query videos (and now ordered three books!) It seems it would be much more efficient to do it in Power Query.
I get the over-all idea (create a table from the delimited list, for one) but don't know the language of the actual steps. And I really want to learn these new tools.
Can I buy a vowel? Phone a friend?
Import the table into Power Query using From Table
Click on the second column and choose "Split Column | By Delimiter" from the ribbon. Choose Comma as the delimiter and click OK.
Right click on the first column and choose "Unpivot Other Columns".
Click on the Attribute column and click on "Remove Columns" from the ribbon.
If you go to the Advanced Editor, you can see the code used to generate this:
let
Source = Excel.CurrentWorkbook(){[Name="Table1"]}[Content],
#"Changed Type" = Table.TransformColumnTypes(Source,{{"Column1", type text}, {"Column2", type text}}),
#"Split Column by Delimiter" = Table.SplitColumn(#"Changed Type","Column2",Splitter.SplitTextByDelimiter(",", QuoteStyle.Csv),{"Column2.1", "Column2.2", "Column2.3"}),
#"Changed Type1" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"Column2.1", type text}, {"Column2.2", type text}, {"Column2.3", type text}}),
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(#"Changed Type1", {"Column1"}, "Attribute", "Value"),
#"Removed Columns" = Table.RemoveColumns(#"Unpivoted Other Columns",{"Attribute"})
in
#"Removed Columns"