Bokeh distinguishes itself from other python visualization libraries such as Matplotlib or Seabornin the fact that it is aninteractive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
Bokeh is also known forenabling high-performance visual presentation oflarge data sets in modern web browsers.
For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let's not forget that thewide variety of visualization customizationoptions makes this Python library an indispensable tool for your data science toolbox.
As you might know, DataCamp recently launched the Interactive Data Visualization with Bokeh course together with Bryan Van de Ven , Bokeh core contributor.
Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page referenceor for those whoneed an extra push to get started.
In short, you'll see that this cheat sheet notonly presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts.
In no time, this Bokeh cheat sheet will make you familiarwith how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer.
Boost your Python data visualizations now with the help of Bokeh! :)
PS. Check out our Interactive Data Visualization with Bokeh course or visit the Bokeh documentation website .