On GitHub, a fire resources to do the interactive graphics with Python.
This tool is called Bokeh, said the official introduction, which reads large data sets or data stream to provide a simple and quick way to beautiful pages, highly interactive graphics performance.
For example, some people use it to make such a map:
Someone made this chart:
There are other various map:
Some people use it to make a chart to go on the TED lecture:
"Beautiful and practical" is the evaluation given by many users, and some even want to use this tool more convenient, trying to finished its official documents.
Now, this resource has starred 9900+, was rushed to GitHub trend list first.
Bokeh Guide
Bokeh, is provided by non-profit organizations NumFocus support, we can use for free, the official website address:
https://bokeh.pydata.org/en/latest/
Bokeh oriented user interfaces open three levels:
Low-level interface to provide highly flexible graphical representation (support for some of the top custom components) for application developers
Intermediate interface is mainly used to draw the curve (default load some low-level components)
Advanced interface is used to quickly and easily build complex graphics
official support for Python 2.7 and 3.5+ version, on the other versions of Python functionality may be limited.
Want to use this resource, the most direct way is to download on GitHub. project address:
https://github.com/bokeh/bokeh
However, the official recommended installation is to use Anaconda Python and its accompanying Conda package management system, which is a specialized Python / R language to create a data science platform, download address:
https://www.anaconda.com/distribution/
In terms of tools, the government has also provided a detailed user's guide, including fast installation and operation, the fundamental concepts, how to deal with data, graphics, add comments, interaction, and so on:
Some people are being Bokeh user guide localization:
https://github.com/DonaldDai/Bokeh-CN
In terms of specific implementation, the official tutorial and example:
Based Jupyter Notebook tutorial is provided, Bokeh itself seamlessly integrated with Jupyter Notebook, is also more convenient. For each example given, the official also shows the code behind the implementation.