Overwriting Power BI Filters

Scenario: You’ve created a dashboard-looking Power BI report that has a filter. Most visuals on a report page need to show only data for the selected filter but some must ignore the filter.

Solution: There are several solutions to achieve this goal depending on requirements. Let’s start with the no-brainer and move down in complexity.

  1. Move the unfiltered visuals to another report page — Slicers and page-level filters filter visuals on the same page (note that the Power BI Desktop February update allows you to configure a report-level slicer). By moving the visuals that should remain unaffected by the slicer to another page, you’re effectively ignoring the page-level filtering.
  2. Use a slicer and “Edit Interactions” – If you use a slicer, you can use the Edit Interactions feature (Format ribbon) to specify which visuals ignore the slicer selection. Edit Interactions is described in more detail here.
  3. Consider using time calculations – For example, if most charts on the report must show data for the last 12 months, but some need to show data for the current month, you can apply the most common filter, which in this case is 12 months. For those charts that shows the last month, create a Month-To-Date (MTD) calculation and bind it to those charts.
  4. Overwrite page-level and report-level filters – Sometimes you can’t use slicers, such as in the case where you need more advanced filtering options or when you need to filter the report by URL (you can pass filters on the report URL only if you have page-level and report-level slicers). This is the most complex case because it requires some changes to the model as I describe below.

Currently, Power BI doesn’t allow you ignore page-level or report-level filters. As you’d quickly find out, Edit Interactions doesn’t work for them. When you apply such a filter, Power BI generates a query with SUMMARIZE that applies the filter as an argument to SUMMARIZE. Think of that filter as a WHERE clause that is applied after all measure-level filters. So, you might have a measure that ignores the filter, but the report would still return the rows matching the filter condition. However, you can use the following workaround to achieve the desired effect. Consider this report.


In this case, a page-level filter filters CalendarYear from the Date table to values 2007 and 2008. The first Table visual returns data only for these years (no issues here). But the Chart visual shows data for all years. Follow these steps to get the Chart visual to work as shown:

You need a new measure to ignore the dimension filter. However, the key to get this to work is to use the field form the fact table on the report.

  1. Add the filter column (CalendarYear in this case) to the fact table. You can do this by different means, such as creating a DAX calculated column, creating a PowerQuery calculated column, or by PowerQuery merged queries (to look up the value from another table). The important thing is that you need to have the filtered field on the fact table.
  2. Use that field to the chart (not the one from the dimension table that the page-level filter uses).
  3. Create a measure that overwrites the filter and use that measure on the chart:

    NetProfitAcrossYears = CALCULATE(SUM(ResellerSales[NetProfit]), ALL(‘Date'[CalendarYear]))

    The measure ignores the filter on the CalendarYear field. This expands the filter context on the table to all years. Because the Year field used on the report is from the fact table, the net effect is that the data for all years is shown. This approach won’t work if the chart shows measures from two fact tables because you probably won’t have a relationship between them.

    In this post, I outlined several options to overwrite Power BI filters. It wouldn’t be nice if Microsoft enhances Edit Interactions to page and report-level filters to avoid changes to the data model.

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Visual-level Formatting in Power BI

Scenario: You want to overwrite the formatting of some field in a Power BI Visual, such as in the case of showing the field in one visual with decimals and  then in another without decimals. Or, you might want to overwrite the default format when connected live to a semantic model. You search left and right, bing the Internet, and still no clue as to why Power BI doesn’t have this feature.

Answer (that you probably won’t like): Most visuals already support display units and decimals. Table and matrix would probably get this feature very soon. We don’t have ETA for full custom format string override (per visual). Your vote counts so vote on ideas.powerbi.com.

As a best practice, I recommend applying format settings in the model so that it’s applied consistently to all reports.

In the narrower case of formatting measures, you can create a new measure and change its formatting. This also works when Power BI Desktop connects to a Tabular semantic model because you can create local DAX measures on top of the semantic model. Just create a measure that piggy-backs on an existing measure, whose format you want to change. Then, use the Formatting section to change its format.


Mark as Data Table in Power BI

In my “Understanding Dates in Power BI Quick Measures” blog, I mentioned that having a Date table is a best practice but Quick Measures didn’t support it. Fortunately, the February update of Power BI Desktop fixes this. Now you can mark a Data table as such, as you’ve been able to do in Excel Power Pivot since the beginning.


Marking a date table accomplishes three things:

  1. Disables the Power BI-generated date hierarchies (aka as inline hierarchies). Good riddance! Check the same blog for details of why inline hierarchies should be avoided.
  2. Allows you to use your Date table for time calculations in Quick Measures.
  3. Makes DAX time calculations work even if the relationship between a fact table and the Date table is created on a field that is not a date field, such as a smart integer key (YYYYMMDD). Previously, you had to create the relationship on a field of Date data type.

Reporting on Concatenated Field in DAX

Scenario: You have a concatenated field stored in a table. For example, a medical claim might have several denial reasons. Instead of representing this as a Many-to-Many relationship, you’ve decided to store this a comma-delimited field, such as to allow the user to see all codes on one row. However, users are asking to produce counts grouped by each reason code, such as this one:

Solution: Follow these steps to implement a DAX measure that dynamically parses the string.

  1. Implement a DenialReason table with a single column DenialReason that stores the distinct reason codes. Add the table to your Power BI Desktop/Tabular model. Leaving it hanging without a relationship.
  2. Add a CountByDenialReason DAX measure that parses the string:

    CountByDenialReason :=
        SUMX (
            IF (
                NOT ISEMPTY (
                    FILTER (
                        VALUES ( DenialReason[DenialReason] ),
                        PATHCONTAINS (
                            SUBSTITUTE ( Claim[DenialReason], “,”“|” ),

The NOT ISMPTY clause checks if the row contains any reasons. The PATHCONTAINS checks if the row has the denial reason whose context is passed from the report. In my case, the DenialReason field has a comma-delimited string if multiple reasons are stored on the row. Because PATHCONTAINS requires a pipe “|” delimiter, I use the SUBSTITUTE function to replace commas with pipes. If match is found for that reason, the IF operator returns one, which is summed in SUMX to return the actual count.

If you have a large table, you might get a better performance if you do the replacement in a wrapper SQL view or in Query Editor and avoid SUBSTITUTE altogether.