![]() In this dataset, there are no sales for 2009. If no customers have data for a specific year, this year will be absent from the pivoted result. I pivot on year, and if the first customer has non-empty data for 20 and the second customer for 20, the order of columns of the result pivot will be 2005, 2008, 20. I want to filter all the rows that have zero as the value, but after the filter, my pivoted results are skewed. The original dataset was much larger and came from a slow web source.Įvery customer/year combination has a row, but many rows have zero as the value. I created a dataset with annual total sales for customers. ![]() That is why I was happy to see that I can a solve a non-trivial problem without opening the Advanced Editor once. If you apply some tweaking on the formula bar, you are probably in a safe place. Nowadays, I’m more open to a limited kind of using M that tweaks the generated M, as opposed to actually writing new M statements.Īnytime you find yourself opening the Advanced Editor, ask yourself if you have a good reason for that, and if the person that will need to maintain this solution after you will be able to follow what you did. Many times in the past, I told people, “Instead of learning M, get a life.” What I meant was that M is hard, and Power Query is already very rich therefore, spend your time learning more useful tools such as DAX. ![]() ![]() A great example from way back when Power Query was not even called Power Query is this article from Chris Web (2013). This leads to some people turning to straight use of the M language. This UI is covering more and more areas of functionality, but there is still a lot more power locked in the language. The Power Query tool is a UI in front of the M language. Experts debate endlessly about the direct use of M language, which is the script language behind Power Query.
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