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So as you saw in the previous lecture we
were able to build this graph, and you

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also saw that there are various ways to
achieve your results, but what I prefer

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is to have intermediate results so in
this case this was the initial data

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frame and then I made some operations
here to end up with this 3 additional

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columns. Now you could calculate this
columns on the fly here and you don't have to

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write all these lines of code, but I
prefer to have control over the

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operations, over the script so it's
up to you whether you want to use

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intermediate results or not. So that was
a couple of notes about the previous lecture.

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Now let's focus on this lecture
and what we will do is we will add the

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segments vertically and also we will
give these grid lines a low profile, so

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we could either remove them completely
or we could add some transparency there.

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So let's work all the grid lines first.
This is what we have so far, so here let

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me join these two cells like that. This
is the new data frame, let me do it this

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cell, we don't need this, and here is our code.
So once we create the figure then

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we have a title, and here we can apply
the grid class which has a property

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called grid line alpha so as you can
imagine this is the alpha factor of the

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grid lines, so alpha means level of
transparency and you can pass a number

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from 0 to 1 so if you pass 0 there,
you will get a chart with 100%

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transparent grid a soup,
so a good number would be 0.3.

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So this looks better. The gridlines have
a low profile now. I think they are

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enough to be able to see where this box
is falling in the X and Y axis. That's good.

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Now let's go ahead and add another glyph
for the vertical lines, and the glyph that

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you want to go for is the segment glyph.
Now a segment is defined by four

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mandatory parameters. The first one is
the x value of its highest point, and the

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next one is y value of the highest point,
and then you have the x value of the

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lower point, and Y value of the point as
well. And when you have lots of segments

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like we do in this case you can pass
lists and data frame columns, so the x

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value of the highest point would be
df.index, so we have data timing in

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the x axis and yeah, that's it, then
you have df.high. Now we don't have to

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filter out anything here because the
segments will be the same for every row.

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So X Y and then you know the X will be
the same for the next point because

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those are vertical segments so X remains
the same, but this Y value changes to

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df.low and maybe as a color there.
Let's say black and execute.

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Yeah, we're almost there so I'm sure
you're wondering now how do we get this

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segments behind the boxes. Yeah, I can
tell you how to plot these segments

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behind the rectangles, but first let me
tell you the reason that this segments

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were plotted in front of the rectangles.
So if you look here you see that we

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first plotted the rectangles in the
figure object and then the segments.

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So this means that you have like several
layers and the first layer is added

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there and then every layer after that
goes on top over the first layer, so that

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main segments will be on top of the
rectangles. If you own the segments to

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be behind you have to add them first, so.
Here we go. So that is looking good and

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we can also improve the visual parts a
little bit by changing these colors. They

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are a bit intense, so in the next lecture
we'll work a little bit on the plot area

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and make it better, so I'll see you
in the next lecture!