 Welcome back to another video looking at Power BI. So if you've seen our previous video where we're using or showing you how to connect the data on the web, then the dashboard we're looking at here will seem very familiar to you. I've just finished filming the video for that tutorial and there was some bits with the data that I felt would need to be updated. They didn't look quite right. So namely being, if we look at the chart here for India, you can see that we've got India and then the value of C surrounded by these two brackets and the same for other countries. And obviously it's not presented the data in the manner we'd like to see. So I thought it'd be a good opportunity with a real world example to show you how we can extract that information using the transform data or power query to be more specific within Power BI. So that is going to be the theme of this video. So picking up from where we left off, we've obviously got data into our dashboard and we now decided that we need to make some updates to this, namely being able to simply remove these values from our data. All we need to do is go transform data. So we just click that button overall and it'll open up our power query editor. And I'm just wrestling on in my other screen, there we go, just to find that query editor. And we won't go, there's some other bits you could probably play around with this data but our main ambition here is just to remove swiftly and quickly for every country that has these square brackets and letter just to remove that all together. So this is a really simple, quick trick we can use to really tidy up our data in this column in particular. So all we need to do is just select this column, go into transform, and you'll see there's this option about halfway across underneath the text column option of extract. So all we need to do is select that dropdown and you can see we've got a range or a range of options available to us. So we've got length, characters, range, but the ones we're interested in these text before, after and between delimiters. So I want to extract the text before a delimiter and that delimiter being this left square bracket. So all we need to do is go text before delimiter and we'll get a pop up but ask for some more information. And simply all I'm gonna do here is enter a square or a left square bracket. And you can see we've got some advanced options available to us here, but we don't need to worry about that. If in case you want to skip a number of delimiters, so you have the first, whatever it might be. But for us, we'll keep that closed, ignore that. We're simply gonna enter this left square bracket. Once we've done that, we'll click okay. And you can see how all of those bracketed letters have now been removed from our column. So this gives us a lot cleaner list of countries to work with. So if we now go back to our home tab, close and reply. So that is now been saved. That step has now been saved into our query. And what I should have done there is just probably showing you that step actually. So what I was gonna go back into transform data, so apologies for jumping over that and going back in. You can see our step, we just done that of extracted text before delimiter. You can see it's now been added to our applied steps. So every time our data is refreshed or we refresh our data, it's also gonna execute this step to make sure that our data retains this desired format we have here. No changes are made this time, so only that you're gonna just cross out of this screen. And you can now see with our countries we have on our bar chart here, Indonesia, Greece and Hong Kong are looking nicely formatted. It's obviously dropped the UK because obviously it had that other value in, so it's no longer able to find it. So if I scroll all the way down our list and let's find United Kingdom will be in here somewhere. They'll get United Kingdom, United States and you can get UAE as well. We can see how we've got a table or a chart and our table is all nicely formatted as well. You don't need to obviously worry about this too much, but it just bothers me. So I'm just gonna go into population here and I'm gonna literally separate by comma. And that way we can see we've got some more manageable populations we've got here. And yet we can see again, I don't know the populations for every country, but I know for United States and the UK, these populations look about correct. So I can see that we're happy our data is pulling in correctly as well. So the data set that we looked at in that previous video was simply connecting to Wikipedia and pulling out the population per every country. It's a really useful data set to use. So if you haven't seen that video, I suggest you go check it out. It's obviously just a previous video to this channel and hopefully a link to that might pop up during this video as well. Obviously using population data is always gonna be of use, whether it's just personal interest or you've actually got a business work connection to that as well. It's also just a really interesting data set to use when you're playing around with Power BI. So like I say, strongly suggest you check out that video even if it's just to get that data set. So you have something that you can play with in your future playing of Power BI. But if you did enjoy that video and it showed you something useful, all nonetheless, if you made it this far and you just did enjoy watching this video, please make sure you hit that like button. That way it will help the all-important YouTube algorithm to ensure more people are able to find this video and other videos on our channel so we can help them identify or answer the questions that they're looking for. 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