Although the conditional formatting by rules feature in Power BI was released a long time ago, one very common cause of confusion is with how to implement basic “greater than” or “less than” rules. For example, say you have a table with the following data in it:
I was doing some online shopping last week and saw a price slicer with a histogram to show the concentration of price points. I thought it was pretty cool, so decided to see if I could build this in Power BI. I came up with a solution that works, and in this article I show you how you can do it yourself.
There was a new feature released in the June 2019 version of Power BI Desktop that I really like and I think warrants some explanation and review. The new feature allows you to pre-filter a slicer so that it only contains a subset of values that would otherwise appear in that slicer.
There was an announcement made a few weeks ago by Microsoft that nearly slipped under my radar. I don’t always read the more technical announcements because I don’t have a highly technical background. Luckily I read this particular announcement as it contained a nugget of gold that is very useful for anyone with a business background that wants to learn more about the Power BI APIs.
Recently I found I needed to remove all the HTML tags from some text in Power Query. I searched and found a great – if complex – function from Marcel Beug here, but I realised that since that post was written the Html.Table M function has been added to the language and that it makes the task very easy. In fact it’s basically the same as the solution I blogged about here for extracting URLs from a web page.
I was delivering some training last week for a customer in Sydney and they participants had an interesting problem where they needed to be able to report on a rolling time window. Think of it a bit like Rolling 12 Months Sales, or average weekly sales over a rolling 4 week period, but instead this problem uses a rolling time window of just a few hours. I have decided to demonstrate how to solve this problem using my own solar electricity data and build a rolling average 3 hourly kWh consumption.
I have had a couple of requests for help during my live training classes and online training classes on how to build a Six Sigma Control Chart. Now I am not professing to be a Six Sigma black belt, but I do know how to write DAX and I do know how to use Power BI. So here goes. This is what I am going to build.