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