 Okay, so I just spent a lot of time talking about all the different parts of the chart, but I didn't say how we take a chart and make them in Python, and that's actually where we will use at least the de facto standard of Python graphical representation, a library known as Matplotlib. This is, again, one of the most common libraries when we think about graphic representation with Python. There are others, Seaborn and Plotly and things of that nature, but almost all of them, I'm not going to say this like 100%, but a lot of them have Matplotlib just sitting in the background anyway. And so the entire idea is, again, this is allowing us now to suddenly make graphs. So how do we even kind of begin this process? The first thing is that we got to understand Matplotlib, it's a massive library. So we're not even going to just do a simple import Matplotlib, right? We're actually expanding this by utilizing the dot notation. And we're focusing only on a smaller library within Matplotlib known as Pyplot. And just like we saw with NumPy, we're doing the same kind of concept where we're going to add in sort of an alias. And again, the most common alias for Matplotlib is PLT or short for plot. And again, okay, let's take that and throw it in. And let's create ourselves a library, or not a library. So from Matplotlib dot Pyplot as PLT. Here we go. Matplotlib as PLT. Oh, that's not a from that's an import statement. There we go. Okay, so now congratulations, you've imported Matplotlib. Now what do we do with this? Well, this is where we can start to begin to make our graphics. And to work off of this, the simplest way we can do this is we can establish both our x and our y data points. So we'll start with just making say a small little x, five, 10, 15, 20, 25, 30, 35, 40, 45, 50, just going all the way to 50. And I'll add a one there just for, you know, continuity sake. So that's going to give us how many is that 1234567891011. Okay, maybe we don't need the zero. So we now have 10 values. And well, I'm just going to actually add in a little bit more to this. So you know, we can sort of play around a little bit. So I'm actually going to import our random library as well. Random is going to be allowing us to create random numbers. I'm going to just generate some random numbers for my y axes, if you will. So very quick way to do this. You can either do the starting off of just with an empty empty list and then for I in range, Lynn, x, y dot pinned, r dot random, I don't know, something between, I'll go with 10 to 50. And this works, this is step way of or way one of doing this and R. Oh, yeah, got to redo this. This will build it. And, you know, if we run it, we see that y has produced some random numbers. This is one approach. When you're dealing with sort of just a very quick data creation or list creation, another approach that you can work off of that I often employ as well is you can actually do some list comprehension. So in our case, we're just generating a random number for, you know, 10 times, I can do that inside of my square bracket. So r dot rand int 10 to 50 for I in range, or Lynn 10 or x just so we are still kind of following that same structure. And what this is going to do is again, it's going to infer all the same processes, but this is one of those little shortcut techniques that Python has at its disposal. That way we can just very quickly do something to some data. So again, basically run a for loop in this case 10 times, and each time do this. Okay, so we take it, we run it again, I generated random numbers, all is right in the world. I still haven't plotted, I still haven't made a graphic for you. Okay, well, this is actually pretty straightforward. If we're working off of just going with the bare bones, most basic form of a line chart or a scatter plot diagram, all we need to do all we need to do is PLT dot plot. And then it's expecting two parameters, our x values, and our y values. And I shift Enter. And congratulations, I now suddenly have a line chart that is showing if you can tell, there's my little tick saying that that is five at 3110 at 1015 at 16, same kind of thing. We can expand on this as well. Like I said, this will produce in our case a line chart. But I can also do something like a scatter plot diagram. And same kind of approach comes into play PLT dot scatter x and y. And what do you know now instead of it being a line chart, it's producing dots that will appear in a scatter plot diagram. So the entire idea to this is again, this is allowing us to again, produce some values and say what those values are. But we can also add in additional functionality. Once again, one of the things I was talking about in our sort of here are the things that, you know, make a chart a chart, or all of the meta information, we can say, for example, make that y label by doing something like PLT dot y label, we can also in this case, do something called dot show. Now dot show, this really depends on what your environment is. If you're using something like Jupiter or spider, sort of, they will automatically just show you the graphic. But if you're using a traditional IDE or a text editor, you need to explicitly do the dot show. So as you can tell, we don't need to for Jupiter. Sometimes you do. And that's going to produce our graphic. We can also save our graphics. So same kind of concept, instead of it being PLT dot show, PLT save fig, and this will you give it the file name, it will save that as a PNG file. We can also begin to expand this again, like I said, we can do things like scatter plot diagrams. But one of the big things that I'm trying to get to is the idea of a number of different additional key arguments. So for example, right now, those dots, they're blue. But I can come in and do something like color and make them red. Or if I want, I can do something like KK is sort of matplotlibs way to represent the color black. But the entire idea is we can add in additional commands to something like the scatter function. And if we know sort of the parameter name, once again, we can pass that in. And that's going to give us new features and new functionalities. And again, we can add in those colors. If we want, we can also add in more data to our chart. And finally, the idea of something like markers, this is again, telling us that we can take our data and customize it. So instead of it working off of circles, like we were just seeing, we can also do something like marker. In this case, a little Chevron or I call it a carrot. But the entire idea here is now make it triangles rather than circles. You can also do an X and it'll make them X's. And then you can also do something like s. And this is in this case, as you can sort of guess from the graphic, it will produce a larger size. So again, if I come in, marker is going to equal, I'll say X, and then s is going to equal, do that 500. So we're saying, take your markers, color them black, make them an X instead of a circle, and then have a size of 500, whatever that sort of represents. And that's what we get.