Category: DAX

Matt Allington takes us through the REMOVEFILTERS() function in DAX:

Recently Microsoft introduced a new function in DAX called REMOVEFILTERS(). This is a very useful and well named function and it does exactly what its name suggests. Its purpose is to act as a table filter parameter inside CALCULATE() as shown in the following example.

Total Sales All Products REMOVEFILTERS() = CALCULATE([Total Sales],REMOVEFILTERS(Products))

Read on to see how this compares to the prior/alternative solution and for more information on REMOVEFILTERS().

Rob Collie has some fun with DIVIDE():

A blank cell in a report is sometimes a source of confusion for those human beings consuming our work. “What does a blank cell mean,” they ask.  “It’s a division by zero,” we reply.  “Wut,” they then ask.  “Trust me,” we say, “you don’t want to see the alternative.”  “But I don’t trust you, and now I don’t trust this whole report,” is what they sometimes say next – whether under their breath or out loud.

But “N/A” is a lovely value to display.  It raises far fewer eyebrows.  “Oh, it says our Profit Margin % for electric blankets sold in Cancun is “N/A” – I get it, we’ve never sold that product there.”  No convo required.

Click through for the full story.

Alberto Ferrari lets us compare up to specific dates between years:

Unfortunately, the calculation is not perfect. At the year level, it compares the full previous year against an incomplete current year – in this example there are no sales after September 5th in the current year.

Besides, the problem appears not only at the year level, but also at the month level. Indeed, in September the Previous Year measure returns sales for the entire month of September in the previous year. The comparison is unfair, as there are only five days’ worth of sales in September of the current year.

Read on for a better technique.

Teo Lachev shows how we can implement asymmetric crosstabs in Power BI:

The Internet column shows the sales amount from FactInternetSales. Then, the matrix pivots on the BusinessType column in the FactResellerSales. Because, Internet sales don’t relate to BusinessType, it doesn’t make sense to pivot it. Instead, we want to show Internet sales in a single static column before the crosstab portion starts.

Implementing such a report in SSRS is easy thanks to its support of adjacent groups and static columns but not so much in Power BI. The issue is that Matrix would happily pivot both measures and the InternetSalesAmount would be repeated for each business type.

The solution isn’t awful, but it does involve knowledge of DAX.

Alberto Ferrari explains how different DAX functions treat blank rows differently:

DAX offers two functions to retrieve the list of values of a column: VALUES and DISTINCT. The difference between the two is subtle. To understand it better, we first need to introduce the concept of the blank row. The blank row is a special row added to a table, in case the table is on the one-side of a strong relationship and the relationship is invalid. Let us elaborate on this.

Click through for the detailed explanation, along with plenty of examples.

Matt Allington continues a discussion on the FILTER() function in DAX:

The new formula follows the rule “don’t filter a table if you can filter a column”. But in this case the column and the table have the same cardinality, so there is little benefit there. Also, the new formula requires a second CALCULATE() and SUM() inside the FILTER() function. This is required because the column Customers[YearlyIncome] is no longer in the same table that FILTER() is iterating. The FILTER() function is iterating a virtual, single column table that contains all customer keys in the customer table. The column Customers[YearlyIncome] doesn’t exist in this virtual table, it exists in the Customers table, so you must wrap the column in an aggregation function, SUM() in this case. Further, as the FILTER() function iterates in a row context through the virtual table, the virtual relationship does not filter the connected tables UNLESS you specifically tell the formula to do so. Technically, to make the filter propagate from the new virtual table created by ALL(Customers[CustomerKey]), we need to convert the row context into an equivalent filter context via context transition. Context transition is triggered by the inner CALCULATE() inside the FILTER() function in this case.

Read on for several tips for efficient filtering.

Matt Allington takes us through the FILTER() function in DAX:

FILTER() is most often used as a filter parameter within the calculate function. OK, so now it is getting confusing. FILTER() is a function. There is another use of the word filter in DAX, and that is as a parameter in CALCULATE. Let’s look at the syntax

FILTER Syntax: FILTER(<table>, <true/false test>) CALCULATE Syntax: CALCULATE (<measure expression>, <filter parameter>, <filter parameter>,…)

This is one of the most useful functions in DAX and one you really want to know well.