04 November 2019 / Carolyn Hulsey
We are pleased to announce the release of Bokeh 1.4!
If you are using
Anaconda, Bokeh can most easily be installed
by executing the command
conda install -c bokeh bokeh . Otherwise, use
pip install bokeh.
This is the last Bokeh release to support Python 2. Starting with Bokeh 2.0, only Python version 3.6 and up will be supported.
Bokeh passed quite a milestone in October: our Github repo showed that in a month, there were over one million Bokeh package installs. That’s great to see!
We’ve seen some great examples of Bokeh in action on our Discourse site this month. For some inspiring data visualization case studies, you may want to check out:
If you’ve built something with Bokeh, we’d love to see it and share it with our community! The Showcase section of our Discourse site is dedicated to examples of Bokeh in action. It’s always exciting to see data visualizations done well, and tools and ideas used in innovative ways.
While you’re checking out that new search capability, take a look at our clean, fresh new theme for the documentation site. This beautiful makeover was made possible by Joris Van den Bossche, who shared some work he’s done on theming docs for pandas.
The new theme is also mobile-friendly! Click around, and again, let us know what you think– we’ve got a Discourse thread going to discuss ideas and observations, and we’d love to hear from you.
RequestHandler.get_current_usermethod. This enables developers to create private applications protected by a username/password. Information on how to implement this can be found in the docs, and we also have a working example on GitHub.
With the updated
jupyter_bokeh extension, it is now possible to render Bokeh figures and
widgets as ipywidgets or use them in ipywidget layouts. This allows for much more fluid,
programmatic interaction between Bokeh and Jupyter components without the need to use
push_notebook() or handle Jupyter comms manually. Installation instructions can be found here.
It’s now possible to embed Bokeh applications in Django, without need for a separate Tornado process. This is possible because Bokeh server code can now also make use of the Django Channels library for communicating with web browser sessions. Note that Django integration works only on Python 3.6+.
It’s now possible to create pre-compiled bokeh extensions using the
bokeh build CLI command and
bokeh.ext.build() API. Such extensions are compiled once and can be distributed with Python packages and/or as NPM packages. Note that this feature is provided as a preview and on a provisional basis only. Full and refined support will be available in Bokeh 2.0.
… and more! The complete list of changes for 1.4.0 can be seen on GitHub.
We’ve still got some great new features just around the corner. Here’s what you can expect in the upcoming 2.0 release:
… and all the many changes previously outlined in our post, Preview of 2.0 Changes.
Many thanks to all the new contributors to helped make this great release!
As always, anyone interested in helping out should drop by the Dev Chat Channel and say hello!