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Bokeh Course: Recap and Review

Interactive Data Visualization in Python With Bokeh Christopher Bailey 03:59

Congratulations on completing the course! This video is a recap and review of the course.

To explore even more of what Bokeh is capable of, the official Bokeh User Guide is an excellent place to dig into some more advanced topics. I’d also recommend checking out Bokeh’s Gallery for tons of examples and inspiration.

I hope you enjoy creating many more interactive visualizations using Bokeh!

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Course Slides (PDF)

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Congratulations, you made it to the end of the course! What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the discussion section and let us know.

00:00 Great job! You’ve completed the course! This is a course recap and review. In Section 1, I welcomed you to Bokeh and after a quick overview, you discussed the six steps of taking your data to a visualization.

00:13 You practiced these six steps over and over again: Prepare the data, determine where the visualization is going to be rendered, set up the figures, connect to and draw the data, organize the layout, and then preview and save your beautiful data creation. Next, we talked about setting up your Bokeh environment

00:33 and how you could install Bokeh using a virtual environment. Then you generated your first figure to a static HTML file, which is a huge advantage of Bokeh—the fact that when you’re done you have a complete HTML file that’s been written out automatically for you with all the JavaScript and everything included.

00:53 You learned how to generate a figure inline into a Jupyter Notebook.

01:01 And then you looked at getting your figure ready for data and how to set up plot heights and widths and backgrounds. With the figure ready, now you could draw some data with glyphs, and you learned all the various types of glyphs that are available.

01:19 Next, you created a visualization with multiple glyphs and then added a legend. And then a quick review. Section 2 went much deeper into working with data and layouts. After the overview, I took you on a quick aside about data.

01:33 There was a quick discussion about sources of public data to use for your own visualizations, and talking about pandas and loading in CSV files and how you can wrangle your data outside using a module and then import it in.

01:48 Next, you looked at one of the major features of Bokeh and its ColumnDataSource object—how all your data flows in and out through that to create the visualizations.

02:00 You learned a couple of additional features of the ColumnDataSource object and how to use its GroupFilter and CDSView to do data wrangling right inside your scripts.

02:11 Then it was all about multiple visualizations and how you could lay them out on a single page using a column layout or a row layout.

02:20 And if you want to put a grid of visualizations, there’s something called a gridplot layout that you got to practice with.

02:29 And next, if you have lots of visualizations that you want to use fullscreen, you learned how to create panels and a tabbed layout to switch between them.

02:36 Then a short review. Section 3 was all about adding that interaction, letting your users play with the data. After an overview, you learned about configuring the toolbar and all of Bokeh’s tools that you can choose from.

02:52 Next, you used a few of those tools to select data points, using things like the lasso tool.

03:01 Then you added actions for when you hover over the data and how it can create call-outs to tell you information about the data as you’re looking at it. And then how to emphasize those inspections so they stand out even more to the user.

03:17 Then, when you’re working with multiple visualizations, how you could link the axes so as someone pans across the data on one visualization, all the other visualizations move along.

03:28 Then you learned about how you can link selections and how the ColumnDataSource plays a vital role in that, and as you select data in one visualization how it’ll show those selections in another.

03:41 And then the video you just completed—how you could either hide data points or mute data points using the legend.

03:50 And, of course, this video. Congratulations! I hope you enjoy making many of your own interactive visualizations using Bokeh, and thanks for joining me!

ddwollan on July 16, 2019

A very good course on bokeh. I have not used it for over a year. This will become very useful for my data analysis.

Najmeh on Sept. 14, 2019

Great! Thanks!

Marco Belo on Nov. 19, 2019

Really nice library to know.

lichengzhi75 on Jan. 8, 2020

Very good tutorial, thanks

ssouthern88 on April 13, 2020

Great introduction to Bokeh and very easy to follow. Because of this it’d be really good to see you continue further to include interactivity in the form of dropdown or multiselect widgets. Itd be also good to see where linking can be done between two difference sources (a geo datasource and a column datasource) Thank you!

ananthvp on July 22, 2020

Hi Chris, Course was informative and thank you for doing a great job explaining the nitty gritties, Can you also give examples of on how to use factor_cmap and other palettes that can be leveraged?

Michal on June 15, 2021

Great video course on a cool Python library. Thank you.

aniketbarphe on Sept. 26, 2021

Great Course!

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