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Python Data Visualization 2018: Why So Many Libraries? - Anaconda
This post is the first in a three-part series on the state of Python data visualization tools and the trends that emerged from SciPy 2018.

By James A. Bednar

At a special session of SciPy 2018 in Austin, representatives of a wide range of open-source Python visualization tools shared their visions for the future of data visualization in Python. We heard updates on Matplotlib, Plotly, VisPy, and many more. I attended SciPy 2018 as a representative of PyViz, GeoViews, Datashader, Panel, hvPlot and Bokeh, and my Anaconda colleague Jean-Luc Stevens attended representing HoloViews. This first post surveys the packages currently available and shows how they are linked, and subsequent posts will discuss how these tools have been evolving in recent years, and how they will go forward from here.
dataviz  visualization  python  anaconda 
15 minutes ago by euler
The Hitchhiker’s Guide to Python! — The Hitchhiker's Guide to Python
This handcrafted guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a daily basis.

This guide is opinionated in a way that is almost, but not quite, entirely unlike Python’s official documentation. You won’t find a list of every Python web framework available here. Rather, you’ll find a nice concise list of highly recommended options.
Kenneth  Reitz  book  documentation  python 
3 hours ago by gdw

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