 So welcome to this new tutorial, we're going to look at line charts, something that you might want to use quite often. So first cell, of course, we're going to just run our cascading style sheet. There we have a header one and a header two, setting up our plotting plotly library. And as per your we're going to import iplot and initialize the notebook mode. So let's just do that initialize the notebook mode. And then we're going to import plotly graph objects graph underscore objs as jo. And there we go. Let's create some data point values. I'm going to create two computer variables. They're going to be days and sales. And day is going to go from Monday through Sunday. And sales, I'm just going to have these seven values 11, 14, 11, 14, 10, 11, 10. And that's it. Let's run that to execute. And we now have our two computer variables. Let's just do a simple line chart. So to do a line chart, we actually going to use the scatter. So scatter is in scatter plots. So what we're going to pass is just these three arguments x being the days y being the sales but the mode just being lines. So our data's trace. And we're going to use this key value pair diction Python dictionary here to do the iplot. And let's run that and we see that we have the lines and we go from 11 up to 14 down to 11 up to 14 down to 10 up to 11 and down to 10 for Sunday. So beautiful there. What we can also do is just to fill up the area under this curve under the lines that we have here. So we're going to have full and we're going to full to zero x. So whatever the lowest line here is, we're going to full to there. The full color I'm using a hex code in this instance, my mode is still lines and let's plot that. And we see it's just going to use the default color and it's going to to zero meaning this bottom line of the y value has going to fill up everything below that. Now the different line types that you can try there's actually dash dot and dash dot. So let's do mode as lines again. And the line we're going to pass to that a dictionary because it has these sub key words that it can use. So color we're going to stick to this color. The width we're going to do with the four and the type we're just going to do a dash. And what we're also going to do is just to lay out the x axis key value pair keeping x axis the value being another dictionary with a key value pair zero line being false. We don't want to have the zero line at the bottom. And there we go. We see this orange color. We see it's quite thick with a four and everything is still there. We just change this line type. Now we can also add some markers. So instead of the mode just being lines, we can have lines plus markers and we're going to give the size to these markers. So markers, marker, we're going to equal a dict with one of the arguments being size and the size being 16. And this time I'm going to add a layout. The layout I'm going to do as a just as a Python dictionary here. So it's going to have a title and an x axis. So the key value pair is title and the title sales for last week. But what you can see here, I've got some HTML code in here. So I for italics and close to italics. So we can even do that as far as titles concerned in the x axis key is value is another dictionary with a key value pair the zero line being false again. And then for iPod, I'm just using this dictionary way to do it. And as I say mentioned before, there's so many ways to do things in plotly, which might make it confusing initially, but actually makes it much more powerful and you can actually choose, you know, what works for you. So there we go. We've added the sales for last week. So we've got this title, we can see the last week is in italics. And now we've added these markers that might give it a bit more clarity as to what is going on here. Now we can also do some interpolation. These are just straight lines. So let's do a spline interpolation. So what we've got here is mode again being lines plus markers, the marker having a size, but the line, we're going to do one of its arguments there being shape, and we're going to make the shape a spline. So let's run that. And there we go. We can see instead of these straight lines, we have the spline curve in between these values. We can also do vertical and then horizontal. So the shape is VH vertical and then horizontal. So let's run that so that you can see. So what it would do from this value, it will go vertical vertically first until we get to the level of the second one and then go horizontal. So it's going to go vertical to the level of the next one and then go horizontal as opposed to HV, which is horizontal and then vertical. So now it's going to go vertical to this line of the second one and horizontal, I mean, and then vertical up. So horizontal first and then vertical. So you can play with those two and there's actually a few more ways that you can go about this. Filling of the gaps. So let's do that. Let's take the third value there for sales. Remember it's actually fourth because it's Python and it counts from zero. And we're just going to make that value in the list being none. And if we were to plot this, we see that we have this gap. So there was nothing for Thursday and this gap there exists this gap now. And we can actually just fill in that gap by this connect gaps keyword in our scatter plot here and everything else being exactly the same. What it'll do now is it'll just fill that gap beyond this data point, which does not exist. It's none now and it'll just fill that gap. So you can see line charts quite a bit of fun and quite a useful thing, something that we use quite often. And there we go. Line charts or line plots. I'll see you in the next tutorial.