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Dax filter on multiple columns

DAX: Distinctcount with two filters on same column. This can be applied to any number of months. And of course, they are qualified trainers, with more than 250 classes taught so far. However, the multiple filters will act at the same time. The following measure: Multiple columns in the same predicate should be used only when necessary.

RELATED and RELATEDTABLE are two elementary but powerful DAX. The critical difference between them is that RELATED works on the “many-side “ of the relationship, and RELATEDTABLE works on the “one-side” of the relationship. ... If we recall from the article FILTER & ROW Context, the calculated column automatically applies the row.

DAX now supports expressions where multiple columns belonging to the same table are part of the predicate expression in a CALCULATE filter argument. Thus, the following Big Sales Amount Overrides Filter measure is now a valid DAX expression: 1 2 3.

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This has two negative impacts. First the compression won’t be as good and second every time the cube is processed, for example when a new measure is created, Power BI Desktop will recreate the two columns, which will take more time. You can use Dax.RelativWeek or RelativWeek column as a filter in Power BI. Have fun. Update:. Solution: We can write a DAX code like this to solve this problem. In here I create a Calculated Measure called Other Products which filters all the records in Sales query for Category Accessories and Clothing. EVALUATE. CALCULATE (. SUM ( Sales [Sales Amount] ), FILTER ( Sales, Sales [Category] IN { "Accessories" , "Clothing" } ) ) You can.

Here, I go through a two-step approach: I use the LASTNONBLANK() function to get the latest date, for which there are Values in the SalesAmount column. This step considers the selected Month; Then, the sum of the SalesAmount column is calculated for all rows, which have this date; Now, I use a date table, which I added to the model.

SUMMARIZE AND SUMMARIZECOLUMNS DAX function examples. Often there is a need to (distinct) count or sum values based on multiple filtered tables over a selected variable like a product type. An example could be a KPI like the customer count of a company (per product) when different products have differences in the counting logic or data tables. In this.

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