[MUSIC] Hi, in this segment I'm going to show you a quick example of how to take data and analyze it in a couple of different ways. First, we're going to go through and just take a look at what's built into Jupyter and Anaconda using matplotlib and take a look and see what visualizing your data will actually help you see. Then I'm going to show you a website called rawgraphs.io, which is a nice way when you're not versed in your mapplotlib, or D3, or plotly, or the myriad of other visualization tools that are out there and you just kind of want something quick. So we'll take a look at both of those. All right, so first we're going to go through and set up a style I'm just wanting to use for right now for matplotlib. So I'm going to set that up. And then I'm going to go through and take a look at a quick histogram. So just along with some of the functions that we described last time when we looked at Pandas. Another one here is hist so I can just do a very quick histogram in this example of the created at field. This will just give me a view and similar to what we were seeing before, we just gussied up just a little bit to look nicer. You can kind of see in a bar chart type format, a histogram format, where the distribution of his tweets, of Elon Musk's tweets, actually land, in which years. This next one here is going to take a look at tweet frequency per hour versus its count. So we can say with this what time of day does he tweet mostly? So we can see over the course of, what do we add here, eight years or so, typically is tweeting quite a bit after midnight, not so much during lunch. A lot in the evenings, and a fair amount throughout the day. But as we hit dinner, or actually, sorry, as we hit kind of his normal work day, it kind of goes down. It's nice to nice to see that. Next one, we're going to take a look at that by month to say, okay, are there specific months where he seems to tweet a lot more? Well, apparently, May is the winner for that. Maybe that corresponds to releases of products, might be something worth investigating in the future. Another one we'll look at is just the the year, right? So we kind of looked at that early on, but this is just another one just to kind of close that out. So these were great, you can go through, you have lots and lots of functionality built in lots of ways to slice and dice matplotlib. I can do multiple graphs on a page. I can have multiple axes. I can tweak my ranges and datasets and put extra lines and a myriad of different things. But if you're not versed, and you're going through and you're just trying to say well, how do I just take a look at some of this without other tools. Or maybe you don't have Excel, or whatever. Let's take a look at that. So here I'm going to take the tweet frequency by hour and I'm going to use the to CSV function I talked about earlier, and I'm going to dump it out to a CSV file. So simple dump. So here I've got that. Now in Jupyter, I'm just going to go through and do a download, right? So now I've got that file. I'm going to just open that file, there we go. And so now I can go through and just copy my data. And now that I've got my data copied, I can move this out of the way. Let's jump over to RawGraphs. So here I can paste in data upload a file grab it from a URL or take a look at some of their samples. We're just going to paste it in. Okay, so going to start with that. Let's just start with a simple scatterplot, right? And I've got my first dimension, oops, here we go. And then my second dimension. And I can just come down here and say okay, here we go. Similar view to what we were seeing here because we're looking at the tweet by hour, right? Okay, so yep, same kind of thing here. But I can also do other graphs. Maybe I want to do this contour plot, this dispersion graph, so we can see if there's some concentrations available, right? Drag those things in and take a look. Very nice, right? So you can see you can see some of the concentrations of amounts of tweets for specific specific periods of time. Let's see, what else could we do here? We can take a look at maybe a stream graph, or maybe actually let's look at hexagonal binning, I think I like that one. So we can do our x-axis, our y-axis here. And then you can start to see hexagons around multiple points, right? Kind of looking at things that are nearby, right, within these hexagons. So just a quick way to kind of take a look at ways to go through and do visualization if you're exporting data, wanting to dump things in, its simple as going into rawgraphs.io and entering in your data, right? You can, like I said, paste it in, upload a file, etc. And don't feel like you're stuck, right? You can even look at it in a table format if that works for you better. Okay, until next time.
