ANN: Bokeh 0.6 release

Community Support
1

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6!

Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many bug fixes and improvements over our most recent 0.5.2 release:

  * Abstract Rendering recipes for large data sets: isocontour, heatmap
  * New charts in bokeh.charts: Time Series and Categorical Heatmap
  * Full Python 3 support for bokeh-server
  * Much expanded User and Dev Guides
  * Multiple axes and ranges capability
  * Plot object graph query interface
  * Hit-testing (hover tool support) for patch glyphs

See the CHANGELOG for full details.

I'd also like to announce a new Github Organization for Bokeh: Bokeh · GitHub. Currently it is home to Scala and and Julia language bindings for Bokeh, but the Bokeh project itself will be moved there before the next 0.7 release. Any implementors of new language bindings who are interested in hosting your project under this organization are encouraged to contact us.

In upcoming releases, you should expect to see more new layout capabilities (colorbar axes, better grid plots and improved annotations), additional tools, even more widgets and more charts, R language bindings, Blaze integration and cloud hosting for Bokeh apps.

Don't forget to check out the full documentation, interactive gallery, and tutorial at

    http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

    Jupyter Notebook Viewer

If you are using Anaconda, you can install with conda:

    conda install bokeh

Alternatively, you can install with pip:

    pip install bokeh

BokehJS is also available by CDN for use in standalone javascript applications:

    http://cdn.pydata.org/bokeh-0.6.min.js
    http://cdn.pydata.org/bokeh-0.6.min.css

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page:

    GitHub - bokeh/bokeh: Interactive Data Visualization in the browser, from Python

Questions can be directed to the Bokeh mailing list: [email protected]

If you have interest in helping to develop Bokeh, please get involved!

Thanks,

Bryan Van de Ven
Continuum Analytics

2

Congratulations guys. I’m very excited about this release.

···

On Wed, Sep 10, 2014 at 9:05 AM, Bryan Van de Ven [email protected] wrote:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6!

Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many bug fixes and improvements over our most recent 0.5.2 release:

  • Abstract Rendering recipes for large data sets: isocontour, heatmap

  • New charts in bokeh.charts: Time Series and Categorical Heatmap

  • Full Python 3 support for bokeh-server

  • Much expanded User and Dev Guides

  • Multiple axes and ranges capability

  • Plot object graph query interface

  • Hit-testing (hover tool support) for patch glyphs

See the CHANGELOG for full details.

I’d also like to announce a new Github Organization for Bokeh: https://github.com/bokeh. Currently it is home to Scala and and Julia language bindings for Bokeh, but the Bokeh project itself will be moved there before the next 0.7 release. Any implementors of new language bindings who are interested in hosting your project under this organization are encouraged to contact us.

In upcoming releases, you should expect to see more new layout capabilities (colorbar axes, better grid plots and improved annotations), additional tools, even more widgets and more charts, R language bindings, Blaze integration and cloud hosting for Bokeh apps.

Don’t forget to check out the full documentation, interactive gallery, and tutorial at

[http://bokeh.pydata.org](http://bokeh.pydata.org)

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

[http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/index.ipynb](http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/index.ipynb)

If you are using Anaconda, you can install with conda:

conda install bokeh

Alternatively, you can install with pip:

pip install bokeh

BokehJS is also available by CDN for use in standalone javascript applications:

[http://cdn.pydata.org/bokeh-0.6.min.js](http://cdn.pydata.org/bokeh-0.6.min.js)

[http://cdn.pydata.org/bokeh-0.6.min.css](http://cdn.pydata.org/bokeh-0.6.min.css)

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page:

[https://github.com/continuumio/bokeh](https://github.com/continuumio/bokeh)

Questions can be directed to the Bokeh mailing list: [email protected]

If you have interest in helping to develop Bokeh, please get involved!

Thanks,

Bryan Van de Ven

Continuum Analytics

http://continuum.io

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3

Thanks, Paddy! Btw, we’ve been seeing more interest from people on Angular integration and I’m wondering if you’ve given more thought to or experimented with more things in this area?

Thanks,
Peter

···

On Wed, Sep 10, 2014 at 9:05 AM, Bryan Van de Ven [email protected] wrote:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6!

Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many bug fixes and improvements over our most recent 0.5.2 release:

  • Abstract Rendering recipes for large data sets: isocontour, heatmap

  • New charts in bokeh.charts: Time Series and Categorical Heatmap

  • Full Python 3 support for bokeh-server

  • Much expanded User and Dev Guides

  • Multiple axes and ranges capability

  • Plot object graph query interface

  • Hit-testing (hover tool support) for patch glyphs

See the CHANGELOG for full details.

I’d also like to announce a new Github Organization for Bokeh: https://github.com/bokeh. Currently it is home to Scala and and Julia language bindings for Bokeh, but the Bokeh project itself will be moved there before the next 0.7 release. Any implementors of new language bindings who are interested in hosting your project under this organization are encouraged to contact us.

In upcoming releases, you should expect to see more new layout capabilities (colorbar axes, better grid plots and improved annotations), additional tools, even more widgets and more charts, R language bindings, Blaze integration and cloud hosting for Bokeh apps.

Don’t forget to check out the full documentation, interactive gallery, and tutorial at

[http://bokeh.pydata.org](http://bokeh.pydata.org)

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

[http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/index.ipynb](http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/index.ipynb)

If you are using Anaconda, you can install with conda:

conda install bokeh

Alternatively, you can install with pip:

pip install bokeh

BokehJS is also available by CDN for use in standalone javascript applications:

[http://cdn.pydata.org/bokeh-0.6.min.js](http://cdn.pydata.org/bokeh-0.6.min.js)

[http://cdn.pydata.org/bokeh-0.6.min.css](http://cdn.pydata.org/bokeh-0.6.min.css)

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page:

[https://github.com/continuumio/bokeh](https://github.com/continuumio/bokeh)

Questions can be directed to the Bokeh mailing list: [email protected]

If you have interest in helping to develop Bokeh, please get involved!

Thanks,

Bryan Van de Ven

Continuum Analytics

http://continuum.io

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To post to this group, send email to [email protected].

To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/A05F16C1-6509-4B7A-BC55-49244E2E8BB2%40continuum.io.

For more options, visit https://groups.google.com/a/continuum.io/d/optout.