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Python Data Visualization: Bokeh Cheat Sheet

December 6th, 2016 in Python

Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive 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 for enabling high-performance visual presentation of large 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 the wide variety of visualization customization options 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 reference or for those who need an extra push to get started.

In short, you'll see that this cheat sheet not only 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. 

Python Bokeh Cheat Sheet

In no time, this Bokeh cheat sheet will make you familiar with 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. Also, don't miss our Pandas cheat sheet or the Python cheat sheet for data science

Comments

danieltemkin
Thanks again for the cheat sheet. I have always been interested in Bokeh but never really sat down to learn it and this might give me the motivation to do so. I also pygal and plotly, the later I use most extensively because of the ease with which it can be embedded in my blog. But for this locally hosted project I have been working on Bokeh might be perfect. Do you know if it has an endpoint supporting interactive web elements?
02/15/17 3:24 AM |
karlijn
Hi there! That's very interesting to hear and I'm glad you're considering working with Bokeh for your locally hosted project. For what concerns your question, I don't have a lot of experience with this yet, but I think you will be able to find more information here: http://bokeh.pydata.org/en/latest/docs/user_guide/server.html . Hope this helps!
02/28/17 1:42 PM |