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So now that these data makes sense to you
I can go ahead and explain the candlestick

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chart, so as you can see it's not quite
an intuitive chart to read so you need

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some time to get a hand of it, but this
chart as you can imagine, this chart

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represents this table, these data, so you
can also choose to represent all these

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columns as simple line plots, so you'd
have you'd have a chart with four lines

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so one for each of these attributes.
Open, high, low, close.

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So you could have a line that lies along
the date time axis, so date time as x axis.

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And then you have this value so let's
say you have a red line for open and

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then you have a green line for high and
then another color for low and

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another colorful close, so you'd have a
chart with four lines. So that would be a

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simple representation that I'm sure you
can make by yourself, but this chart here

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is more difficult.
Actually there are two difficulties

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there. The first one as I said is
difficulty in reading this chart so I'll

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explain that and the second difficulty
is the way of building this, so as you

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can see you have actually two kinds of
glyphs, two kinds of figures here which

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are called glyphs in the Bokeh
vocabulary, so you have rectangle glyphs

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and which can also be quadrants glyphs,
so you have to choose whether you want

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to use quadrants or rectangles to
represent these boxes. And also you have

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some lines which could be segments, so
you have to build these glyphs, but

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before building them of course as
a developer of this chart you have to

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understand what these figures mean, so
here is a table and what I'd like to put

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that here so these data are plotted
exactly in this chart, so this chart is

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a representation of these data.
Let's start by explaining the first

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rectangle, so as you can see the
rectangle has a lower border and an

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upper border, so let's say top and bottom.
So the bottom has a value of

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somewhere 704 here and 718, 719 maybe,
so 704 which corresponds with this open

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value, so this first figure represents
the first row of the data frame, and

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every figure represents a row of the
data frame one by one, so as you can

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imagine now the bottom of the rectangle
is open price and the top is the closing

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price, so these two values, and then you
have the low and the high. The low now is

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the lower coordinate, so the lower value
of the segment and the high value is the

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upper value of the segment which happens
to be behind the box in this case, so

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this is a special case. And then you have
the date so this is for 1st of March so

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the month and the day. So before going into
further detail, let's go to another

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glyphs, so let's explain this on here.
So the lower border is somewhere at 712 which

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is this closing price here, and then 718 for
the open price. So closing price here,

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open price here. But for this we had open
price your, closing price here, and that

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brings us to the point that a rectangles
colored in red represent those days

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where the closing price was lower than
the opening price, which means that for

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that particular company the price has
decreased throughout the day, so for this

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company, so for Google,
this is a Google company

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the opening of the day it was 718 and
then it went down to 712, so you have a

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red rectangle, but for this day the price
opened at 703 and it closed at 718.

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Si the price was positive for the day.
It went up. That's why you have a different

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color, so you have to put the color as well
with Bokeh when you build the chart and

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then again you have the segment which
always represents the highest price for

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the upper point and the lowest price
for the upper point for that particular day.

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And yeah, that's. It it's quite
a sophisticated representation of data and

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it can be of great interest if you're
working with stock market data, and if

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you're not working with stock market
data then it's still good because you

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learn a few tricks with pandas and also
the Bokeh library, so for sure you want

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to follow the next lectures. So that's
about explaining things. In the next

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lecture we'll go ahead and build
this chart, so see you there!