 So one of the things that I've been doing with my data is, you've seen, pretty much anytime I wanted to show you the histogram, I would run the data, then I would change the data and run it again. Now, the reason for this is because what would happen if I said, oh, well, you know, I just want to show all the different charts, right? You know, show me the 15, show me the 10, show me the five, that way I can demonstrate all of them just sort of in a row. And the issue is, that's not exactly what would happen. You see, if I only work off of PLT, you know, if I'm doing my hist, or my plots, or my scatters, if I'm only doing PLT.that approach, it assumes, put it all into the same figure, same graphic. And that's obviously maybe not what I want to do for my data. In this case, you know, maybe I want to show all of my different data points. So this is where matplotlib happens to have the ability of scatter, not scatter plots, subplots. The entire idea is, oh, well, I'm going to make one big graphic that has many tiny graphics, many tiny graphs inside of it. And I can reference those using my arrays or my array indexing. And so the entire idea just to, so we can see this in action. Before I go in, I'm going to, one, get rid of these plots. There we are, get rid of these plots. Now, like I said, when we are generating our subplots, there's two things that actually get generated first. The first one is the overall figure. And then all of the different plots, and you'll typically see that people use acts or axes as the point or the variable name for this. And that entire idea is just a reference, which one are we looking at? And so instead of doing plt.show or his or scatter, subplots, plots. Now, the entire idea, as you can see from the examples, it's going to ask me how many rows and how many columns. And then there are other ones like whether or not you want them to all share the same x, y coordinates or not. And we'll go ahead and say true for those as well. So I want to say, oh, well, you know, I want three rows. I want my data on their individual rows. And I want, in our case, one column. So everything's just on the single column. Now, I'll go ahead and say that I do want to share the x and I do want to share the y because, again, we'll see why that comes into play when we expand on this. Now, how I would go about plotting this data is now instead of doing plt, I would actually reference the axes and specifically which axes. Now, I'm only working off of one column, so I only have to give it one number. Zero.his x. OK, well, let me just run it to see what happens with just my one data point. And so you can run this and you'll see that that's exactly what we're getting. We're seeing now three graphs on top of each other. Again, because I wanted them all on a vertical row. And the data is only appearing on the first one, in our case referenced at zero. You can already imagine what would happen if I came in and did the same thing for axes at one, not 10, and axes at two. And so you're seeing exactly that. The generate data with 15 is at the very top. The generate data with the 10 is in the middle. And the generate data with the five is on the bottom. And since they are sharing the same x and y coordinate or axes, they are all going to be relative to each other. So you can see that, oh, we're generating five fives here. That's the highest point. It's very low relative to the 15-15s. Now, we could expand this information. And how I'm going to do that is I'm going to add in something called a modifier, like modifier for this. And we'll say that the default value is 1. Now, if you remember from when we were talking about kurtosis and yeah, kurtosis, we were going upping down with our data. So I'm just going to say that that's what that modifier is. Now, the reason why is because then I want to say, well, as of right now, we're only looking at sort of this skew. But what if I wanted to also look at this skew, but while looking at it like this, what I'm going nuts? Well, what I mean is, let's say I want to add in a second column, where now I'm increasing and decreasing my kurtosis levels based on my modifier. So with that, I'm going to come in and this changes. Now, it's not just going to be a 1 or a 2. Because if I just ran this as is, I should get an error, because it's freaking out because what I'm trying to give it isn't working. You can already see it's going to at least still try and display it. It's not going to work. The reason why is because now what we need to reference is a particular unit. So let's say, for example, we start with 0 comma 0. Now we're effectively handing it a tuple of 0 comma 0, which it's going to reference, oh, that's you're talking about the top row, top left. And just for our sake, again, we're going to get errors, but let's see what happens. Oh, we'll take a look at that. Do you notice that the top row, top left, happens to show our normal 15. Again, that's what we were going for. Now, I'm going to go ahead and take that same data, right? And I'm going to go ahead and modify it so that the next column over, the next column over, is going to get skewed upward. So it now has a much higher chance of appearing. And again, it's an error, but you can notice that now that skew, that kurtosis, is appearing quite well. I can do this same thing with all of my data. So in this case, I'm going to come in. And I'm just going to do the same thing with my middle row, which was in the one section. And that'll be my tens. And then my two, the bottom row. So two, two, with a five, and a five. Now that I've kind of gone through and I've built out each one of my subplots, again, I've referenced each one of them, what I should see is no errors this time. And each one of these slots are filled. And wouldn't you know it? That's exactly what I see. Awesome. You can clearly imagine what I could do with this. I could start playing around with it. So for example, if I wanted, I could expand this. I'm showing you currently the normal modifier and then the high kurtosis. I could also show you the low kurtosis. So just I'll only do it and then I'll be done. I could go in and say 025. So again, that was a very flat kurtosis. And so that's exactly what you're starting to see. So here's that normal approach. Here's where it had a high kurtosis. Here's where it had a low kurtosis. And if we wanted to demonstrate them even further, we could remove that share to see this in a much, it's about the same. It's about the same. And that's where again, you'd have to play around with it on your end about that. I normally experiment with my charts and see what really works. So in this case, for example, if I was trying to give you this as an example, I wouldn't want this because the numbers are sort of superimposed everywhere. But when I did have that share x and share y, it didn't happen. So it's a little cleaner if I did include those parameters. But as you can see, this is a way for you to use subplots.