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On GitHub, a resource for interactive graphics in Python has gone viral.

The tool, called Bokeh, reads large datasets or streams of data and provides elegant, interactive graphics for web pages in a simple and quick way, according to the company.

For example, someone made this diagram with it:

Someone made a graph like this:

There are various other images:

Some people have also made pictures of it for TED talks: \

“Beautiful and practical” was commented by many users, and some even tried to make the tool easier to use by removing the Chinese version of its official documents.

Now, the resource has a star rating of 9900+ and once topped the GitHub trend list.

Bokeh Usage Guide

Bokeh is supported by NumFocus, a non-profit organization, and is free to use.

bokeh.pydata.org/en/latest/

Bokeh opens three levels of interface to users:

  • Low-level interfaces provide highly flexible graphical representations for application developers (allowing customization of some top-level components)
  • The intermediate interface is mainly used to draw curves (some low-level components are loaded by default)
  • High-level interfaces are used to build complex graphics quickly and easily

Python versions 2.7 and 3.5+ are officially supported, but functionality may be limited on other versions of Python.

The most direct way to use this resource is to download it on GitHub. Project Address:

github.com/bokeh/bokeh

However, the official recommended installation is to use Anaconda Python and its accompanying Conda package management system, a data science platform built specifically for Python/R.

www.anaconda.com/distributio…

In terms of tool usage, detailed user guidance is provided, including quick installation and operation, understanding basic concepts, how to handle data, drawing, adding comments and interaction, etc. :

Someone is making Bokeh’s user guide Chinese:

Github.com/DonaldDai/B…

In terms of specific implementation, official tutorials and examples are provided:

The tutorials are based on Jupyter Notebook, and Bokeh itself is seamlessly integrated with Jupyter Notebook for ease of use. For each example, the code behind the implementation is also provided.

If you are interested in this tool, or just need one, try it out:

GitHub portal: github.com/bokeh/bokeh

Bokeh portal:

bokeh.pydata.org/en/latest/

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