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Let's familiarize ourselves with some Bokeh concepts, imports, and terminology. Then we can generate a basic plot and show what Bokeh generates for us.
Before we get to our data source,
0:00
let's familiarize ourselves with
some concepts of the Bokeh Library.
0:02
Some of the common imports we'll use and
some terminology in Bokeh.
0:05
We'll start by generating a basic
plot with some Python lists.
0:09
Simulated showcase,
our tools, and some options.
0:13
To get started,
we need to have our libraries available.
0:16
I've included a requirements.txt file
that has the libraries we'll be using for
0:19
this course.
0:23
Download and unzip that file, and
0:24
install the requirements
by running the following
0:28
python3 -m pip install
-r requirements.txt.
0:34
Once we have those libraries set up and
installed,
0:41
we can start a new project file in Python.
0:44
Stage1_2.py.
0:53
Let's begin with our imports,
and talk briefly about them.
0:58
From bokeh.io,
import output file and show.
1:02
And from bokeh.plotting,
And import figure.
1:07
Great, output file defines
the name of our output file
1:16
to be generated when the show method.
1:21
When the show method is called,
it will generate the HTML and
1:24
display it in a browser.
1:27
There are other possibilities for output,
such as to a Jupiter notebook, but
1:29
that is beyond the scope of this course.
1:33
Jupiter notebooks are amazing though,
and you should check the links
1:35
in the teacher's notes to
learn more about those later.
1:38
The next import, figure, allows us to
create figure objects for plotting and
1:41
has options for things such as plot width,
height, tool selection, and many more.
1:47
But what is a figure object?
1:52
It is where our
visualization will be drawn.
1:55
It is essentially Bokeh's built in graph
paper if you will, and provides us
1:58
with some default access grids and
tools that will simplify plot creation.
2:03
Let's define our output file and
name it test.html.
2:09
Then, we'll set up a basic
figure object and call it plot.
2:19
That is 600 pixels wide
2:27
600 pixels tall,
2:32
And includes a few of Bokeh's
built-in tools for figures.
2:40
These tools will be used in
the browser which we'll see and
2:51
talk about in a little bit.
2:55
Now, we need something to plot or
visualize, right?
2:57
We can pass into our plot,
a variety of data types.
3:01
Things like lists, arrays,
and panda's data frames.
3:05
Really, anything that is a sequence can
be passed in as x and y coordinates.
3:09
One thing to remember here is that
the sequences must be of the same length.
3:14
If you pass in five values for x,
you will need to pass five value for y.
3:17
We also have several options for
the shape of our plot point or glyph.
3:23
Let's plot some points with
a square shape of size 20.
3:26
Let's choose square for x values,
3:34
lets do (x=[1, 2, 4, 8, 10]).
3:38
Our y values, Can be ([6,
3:43
2, 18, 4, 9]).
3:49
And we want them to have a size, 20.
3:55
These are a sequence of x and y pairs.
3:57
So the first point will be 1,
6, the second, 2, 2 and so on.
4:00
Now, we just need to pass our plot into
the show method and call it, like so.
4:05
Then we can run our script.
4:12
And there it is,
automatically in the browser.
4:20
Now, depending on your version of your
operating system, you may see an execution
4:23
error about not being able to
understand the open location message.
4:28
Don't worry about this if you see it.
4:33
So long as the plot loads correctly,
your code works.
4:35
You will notice,
off on the upper right-hand side,
4:38
the tools we requested when configuring
our plot, Pan, Box Zoom, and Reset.
4:40
Pan allows us to move the plot around.
4:48
Box Zoom allows us to zoom in on
a specific location in the plot.
4:52
And Reset, well, it resets the plot.
4:57
Try them out on your own to get a feel for
them.
5:00
I put a link in the teacher's notes for
other tools you can include as well.
5:03
Now, if we go back and take a look
at what was generated by Bokeh,
5:07
in our test.html output file, and
let Python do a bit of code reformatting
5:12
with Control+Alt+L on Windows or
Alt+Command+L on Mac.
5:18
We see that it generates a standard HTML
file with calls to Bokeh's CSS style sheet
5:25
and JavaScript on lines seven and
nine, respectively.
5:30
If we scroll down to around line 36,
we see
5:36
that Bokeh generated a JSON document which
is used to describe our visualization.
5:40
We won't spend a lot of time going over
this, but I wanted to point out that this
5:46
JSON object includes all of
the configuration for our plot.
5:50
For example,
if we look at around line 222,
5:54
We'll see our column names and
the associated data that we provided.
6:00
And if we look at around line 145, We see
that our data is of type ColumnDataSource.
6:03
As you can see,
having Bokeh generate all of this for
6:12
us to built our plot is a huge time saver
and all done on our end in native Python.
6:15
Let's take a short break before we start
working with more complex data structures
6:22
then basic lists and introduce a powerful
Bokeh data object, ColumnDataSource.
6:26
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