ANN: Bokeh 0.6.1 release

Community Support
1

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6.1!

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 point release includes several bug fixes and improvements over our most recent 0.6.0 release:

  • Toolbar enhancements

  • bokeh-server fixes

  • Improved documentation

  • Button widgets

  • Google map support in the Python side

  • Code cleanup in the JS side and examples

  • New examples

See the CHANGELOG for full details.

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:

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

If you are using Anaconda or miniconda, 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-0.6.1.min.js

http://cdn.pydata.org/bokeh-0.6.1.min.css

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

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!

Cheers,

Damián Avila

[email protected]

2

Congrats on the release. I’ve been playing with Bokeh the last couple weeks and it’s really good.

There was some talk about expanding the ecosystem page in the pandas docs to show off brief examples for the various projects (btw I think we should rename that to pydata ecosystem, instead of pandas ecosystem).

Would you say the API for something like a ColumnDataSource example (like the car example) is stable enough to include in our docs?

3

Hi, thanks for your interest!!

About the stability of the API, we try to keep backward compatibility as far as we can, but I can not promise you that we are not going to change a little bit how the user can interact with the plotting API. In fact, currently, there is a discussion about that, and how we could use context manager to make easier the interaction with the plotting interface.

In any case, I think we are pretty solid as a project to be mentioned in your docs, because right now we offer a nice and big set of features which people from the pandas/pydata ecosystem can use successfully… now. And if there is some change in the future, we will do the best to help people to get updated without pain :wink:

If you need more info about this, or other topic, let us know…

Damian

PS: I made a mistake and I did the ANN in cc instead of bcc, so please, any further question, just replay to bokeh to avoid noise in other list. Thanks!

···

On Fri, Sep 26, 2014 at 11:22 AM, Tom Augspurger [email protected] wrote:

Congrats on the release. I’ve been playing with Bokeh the last couple weeks and it’s really good.

There was some talk about expanding the ecosystem page in the pandas docs to show off brief examples for the various projects (btw I think we should rename that to pydata ecosystem, instead of pandas ecosystem).

Would you say the API for something like a ColumnDataSource example (like the car example) is stable enough to include in our docs?

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4

Why are we apparently stuck with blaze 0.6.0 on Pypi ?

···

On Friday, September 26, 2014 5:34:39 AM UTC+2, Damian Avila wrote:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6.1!

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 point release includes several bug fixes and improvements over our most recent 0.6.0 release:

  • Toolbar enhancements
  • bokeh-server fixes
  • Improved documentation
  • Button widgets
  • Google map support in the Python side
  • Code cleanup in the JS side and examples
  • New examples

See the CHANGELOG for full details.

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:

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

If you are using Anaconda or miniconda, 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-0.6.1.min.js

http://cdn.pydata.org/bokeh-0.6.1.min.css

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

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!

Cheers,

Damián Avila

[email protected]

5

Hi Damian, how you doing?

The IPy nb Link is broken… =/

Cheers,

Arnaldo.

···

Em sexta-feira, 26 de setembro de 2014 00h34min39s UTC-3, Damian Avila escreveu:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6.1!

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 point release includes several bug fixes and improvements over our most recent 0.6.0 release:

  • Toolbar enhancements
  • bokeh-server fixes
  • Improved documentation
  • Button widgets
  • Google map support in the Python side
  • Code cleanup in the JS side and examples
  • New examples

See the CHANGELOG for full details.

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:

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

If you are using Anaconda or miniconda, 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-0.6.1.min.js

http://cdn.pydata.org/bokeh-0.6.1.min.css

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

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!

Cheers,

Damián Avila

[email protected]

6

People, please request any question directly to [email protected] so we stop annoying other list (sorry to the other list involved here, next time I will not forget to sent the ANN as BCC).

Cheers.

Damian

···

2014-12-01 9:46 GMT-03:00 Arnaldo Russo [email protected]:

Hi Damian, how you doing?

The IPy nb Link is broken… =/

Cheers,

Arnaldo.

Em sexta-feira, 26 de setembro de 2014 00h34min39s UTC-3, Damian Avila escreveu:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6.1!

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 point release includes several bug fixes and improvements over our most recent 0.6.0 release:

  • Toolbar enhancements
  • bokeh-server fixes
  • Improved documentation
  • Button widgets
  • Google map support in the Python side
  • Code cleanup in the JS side and examples
  • New examples

See the CHANGELOG for full details.

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:

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

If you are using Anaconda or miniconda, 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-0.6.1.min.js

http://cdn.pydata.org/bokeh-0.6.1.min.css

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

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!

Cheers,

Damián Avila

[email protected]

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Damián