We've made a lot of progress with of our data visualization, 0:00 from looking at a flat spreadsheet. 0:03 And, I've been able to produce a nice looking scatter plot based on our data, 0:04 with continence specific colors and a useful legend. 0:08 We've also been able to add some meaningful data, with our hover tool tips, 0:13 to be able to gather insights on each data point 0:17 on our population versus life expectancy chart. 0:19 Our data set includes some other potentially interesting data as well, 0:23 like birth and death rates, land area and amount of coastline a country has, if any. 0:27 We can certainly alter our current plot to utilize some of the other data points and 0:33 plot them on a different page. 0:36 However, what if we want to see multiple charts together, 0:38 all based on the same data. 0:42 Bokeh provides a way to do that as well, and since we are using column data source 0:43 as our data type, our charts can be linked together to allow for exploration in 0:48 one chart, and have that simultaneously update the other charts as well. 0:53 We can have our charts displayed in rows, columns, or 0:59 even in separate tabs depending on the use case for 1:02 the data reporting or the sheer quantity of charts needing to be displayed. 1:04 In this video then, let's tap into some of the other bits of data 1:09 that we have in our county pops CSV file, and show multiple charts on the same page. 1:13 Let's head off to out editor, 1:18 and take a look at how easy Bokeh makes it to accomplish these tasks. 1:19 In order to display additional charts, 1:24 we need to think about how they will be displayed. 1:26 Bokeh allows us to display multiple charts in rows with the row method, 1:28 columns with the columns method, or a combination of the two. 1:33 We can bring that capability in by importing column and 1:36 or row from bokeh.layouts. 1:40 And we'll bring both of them in. 1:43 Now, we need to define our additional figure objects. 1:46 How about we generate one that looks at population versus birth rate. 1:50 Plot birth_rate, And 2:00 the figure object (x_axis label), and will be population. 2:05 Y_axis label, 2:17 Birth Rate. 2:22 We'll give this one a title of Population vs Birth Rate. 2:27 And we want those tools again. 2:38 And we'll zoom, box_zoom, 2:42 reset, hover, save. 2:48 How about we create one as well? 2:54 For population versus death rate. 2:58 Again, the x-axis will be population. 3:10 Death Rate title here will be Population vs Death Rate. 3:20 And we want this tool again, 3:33 box_zoom, reset, 3:38 hover, and save. 3:42 And we need to assign the correct data sources to each. 3:46 Further, let's use some different glyphs. 3:50 For plot_birth_rate, let's use circle, x will be Population. 3:54 Y is Birthrate. 4:03 Our source is still the same source from our country data. 4:08 We'll make this size 10, Our color is gonna be our same dict that we had before. 4:13 And we'll use the same color mapper. 4:31 And our legend here, will be Continent. 4:37 And mapper has two p's. 4:46 All right, for death_rate, let's make that a triangle. 4:52 And we want that to be Death Rate. 5:09 Our source again is our country data. 5:12 And now it looks like I forgot a closing, There. 5:19 Size again will be 10. 5:29 And the color is the same, Color mapper. 5:38 And for our legend, is Continent. 5:48 Okay, I see one thing here that we can do to improve our code a bit. 5:52 Notice the same tools are being used in this chart. 5:56 Let's re-factor that out to make it cleaner and more maintainable. 5:59 Let's go back up here. 6:03 And make it tool tips. 6:09 And just cut that whole thing. 6:14 Paste it in there. 6:18 Now we can use that for all of our tools. 6:24 There! 6:36 That's much cleaner. 6:37 And now when we call our show method to display our charts, 6:38 we can use the features of bokeh.layouts and display our charts in rows, columns, 6:41 or a combination. 6:45 Let's do rows here. 6:46 So row(plot, plot_birth_rate, 6:48 plot_death_rate). 6:53 Now we run our script. 6:59 It looks like I made a typo someplace, let's go take a look. 7:03 All right, and let's start on here again and there we have it. 7:12 Well, we do have some horizontal strolling but its all there. 7:18 Let's change what we are passing into our show method, so that we have a row that 7:24 includes a column of two of the plots, so that everything fits nicely on the screen. 7:28 So we'll have a column with plot and birth rate. 7:39 And a column of our death rate. 7:50 And we'll try running it again. 7:58 There, that looks much better. 8:01 No more horizontal scrolling. 8:03 Now as we look at our data here in our life expectancy chart, 8:05 when we start looking around again, we see that, wait a minute. 8:08 Our data isn't connected to each other. 8:12 Yes, well, that makes sense. 8:14 We haven't associated the plots with each other yet. 8:16 Since we are looking at these charts all based on population, and 8:19 all of those values are the same for 8:22 each data point, let's associate our graphs together, along the x axis here. 8:24 We do that by tapping in our figure objects x range value, and 8:28 equating them all to each other. 8:33 So plot_birth_rate, and we want the x_range. 8:37 We want that to be The same as the plot range. 8:46 And the plot_death_rate.x_range. 8:56 And actually, while we're here, Let's make 9:04 just the plot a little bit more meaningful, and 9:08 rename that plot_life_expect. 9:13 We'll update that everywhere. 9:30 Great, now all of our data is connected to each other through the population values. 9:49 And when we run our script again and zoom in on data in one of our charts, 9:56 Awesome, they all respond together, that's great. 10:04 You'll notice that our hover tools aren't providing great information 10:09 in the birth rate or death rate graphs. 10:12 I'll leave those tasks up to you to implement. 10:16 As it is just a matter of defining those tool tips like we did previously for 10:18 life expectancy. 10:23 Wow, this is great. 10:24 You've taken some data, stored it in a spreadsheet and have added a lot of 10:26 capabilities to visualize the data in a scatterplot, add color to your glyphs, and 10:29 add meaningful text and context to the data with legends and hover tool tips. 10:35 Then, to go even further, we've connected our plots 10:41 by using the same data to generate multiple charts and link them together. 10:44 This is a huge step towards visualizing our data and 10:48 been able to report our findings in a visually, interesting and meaningful way. 10:51 We're able to quickly and easily see multiple aspects of our data on 10:56 the same screen, in an interactive and quick way. 11:00 All by writing some Python code and using Bokeh to build our graphs for 11:04 us, and generate the necessary HTML, CSS and JavaScript. 11:09 For our next step, I want you to think about a different way in which 11:14 we can present our data using some of the plotting features available in in Bokeh 11:17 and integrating that with geospatial coordinate information. 11:21 This will take a little bit of work in Python itself, but 11:26 don't worry, we'll walk through the key points of the process. 11:29 So, let's take a break, and when we get back together, 11:33 let's have a look at how we can take our visualization even further. 11:36