Bokeh

Bokeh is a data visualization library that allows a developer to code in Python and output JavaScript charts and visuals in web browsers.

Why is Bokeh a useful library?

Web browsers are ideal clients for consuming interactive visualizations. However, libraries such as d3.js can be difficult to learn and time consuming to connect to your Python backend web app. Bokeh instead generates the JavaScript for your application while you write all your code in Python. The removal of context switching between the two programming languages can make it easier and faster to create charts and visualizations.

What do Bokeh visualizations look like?

Bokeh can create any type of custom graph or visualization. For example, here is a screenshot of a bar chart created with the figure plot:

For more references, including interactive live demonstrations, check out these sites:

• The official Bokeh gallery has many example Bokeh visual formats.

• Bokeh Applications hosts numerous data visualizations built with Bokeh.

• bokeh-notebooks has a nice collection of Bokeh visualizations within Jupyter Noteboks.

Bokeh resources

Bokeh is under heavy development ahead of the upcoming 1.0 release. Note that while all of the following tutorials are useful, it is possible some of the basic syntax will change as the library's API is not yet stable.

• Responsive Bar Charts with Bokeh, Flask and Python 3 is my recommended tutorial for those new to Bokeh who want to try out the library and get an example project running quickly with Flask.

• Creating Bar Chart Visuals with Bokeh, Bottle and Python 3 is a tutorial that combines the Bottle web framework

• Integrating Pandas, Django REST Framework and Bokeh has a full example of integrating a web API with a Bokeh front end visual.

• Visualization with Bokeh

• Using Bokeh at NIST contains a real-world example of building an interactive visual to show firefighter gear data.

• Drawing a Brain with Bokeh is a fun example of a chord diagram that represents neural connections in the brain.

• Bryan Van de Ven on Bokeh is a podcast episode by one of the main Bokeh maintainers.

• The Python Visualization Landscape by Jake VanderPlas at PyCon 2017 covers many Python data visualization tools, including Bokeh.

• Enjoying the bokeh.models API explains the bokeh.models.plots.Plot object and data ranges.

• This flask-bokeh-example project has the code to create a simple chart with Bokeh and Flask.

• Styling Bokeh Visualizations shows how to use themes and style dictionaries to customize your Bokeh visuals.

What else would you like to learn about Python and data?