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bokeh (0, 2018-12-06)
bokeh\.appveyor.yml (805, 2018-12-06)
bokeh\.bettercodehub.yml (392, 2018-12-06)
bokeh\.dockerignore (65, 2018-12-06)
bokeh\.travis.yml (4163, 2018-12-06)
bokeh\CHANGELOG (254978, 2018-12-06)
bokeh\CODE_OF_CONDUCT.md (4311, 2018-12-06)
bokeh\LICENSE.txt (1494, 2018-12-06)
bokeh\MAINTAINERS (151, 2018-12-06)
bokeh\MANIFEST.in (673, 2018-12-06)
bokeh\_setup_support.py (15167, 2018-12-06)
bokeh\bokeh (0, 2018-12-06)
bokeh\bokeh\.coveragerc (51, 2018-12-06)
bokeh\bokeh\__init__.py (3530, 2018-12-06)
bokeh\bokeh\__main__.py (2245, 2018-12-06)
bokeh\bokeh\_testing (0, 2018-12-06)
bokeh\bokeh\_testing\__init__.py (1810, 2018-12-06)
bokeh\bokeh\_testing\plugins (0, 2018-12-06)
bokeh\bokeh\_testing\plugins\__init__.py (1799, 2018-12-06)
bokeh\bokeh\_testing\plugins\bokeh.py (9417, 2018-12-06)
bokeh\bokeh\_testing\plugins\bokeh_server.py (3614, 2018-12-06)
bokeh\bokeh\_testing\plugins\examples_report.jinja (4087, 2018-12-06)
bokeh\bokeh\_testing\plugins\examples_report.py (8379, 2018-12-06)
bokeh\bokeh\_testing\plugins\file_server.py (5667, 2018-12-06)
bokeh\bokeh\_testing\plugins\implicit_mark.py (2586, 2018-12-06)
bokeh\bokeh\_testing\plugins\jupyter_notebook.py (5350, 2018-12-06)
bokeh\bokeh\_testing\plugins\log_file.py (2074, 2018-12-06)
bokeh\bokeh\_testing\plugins\pandas.py (2153, 2018-12-06)
bokeh\bokeh\_testing\plugins\selenium.py (4228, 2018-12-06)
bokeh\bokeh\_testing\util (0, 2018-12-06)
bokeh\bokeh\_testing\util\__init__.py (0, 2018-12-06)
bokeh\bokeh\_testing\util\api.py (2671, 2018-12-06)
bokeh\bokeh\_testing\util\chrome_screenshot.js (5103, 2018-12-06)
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Bokeh ===== *Bokeh is a fiscally sponsored project of [NumFOCUS](http://numfocus.org), a nonprofit dedicated to supporting the open-source scientific computing community. If you like Bokeh and would like to support our mission, please consider [making a donation](https://www.flipcause.com/secure/cause_pdetails/MzE5NjE=).*

Latest Release		Conda
License		PyPI
Sponsorship		Live Tutorial
Build Status		Gitter
Static Analyis		Twitter

Bokeh is an interactive visualization library for Python that enables beautiful and meaningful visual presentation of data in modern web browsers. With Bokeh, you can quickly and easily create interactive plots, dashboards, and data applications. Bokeh provides an elegant and concise way to construct versatile graphics while delivering **high-performance** interactivity for large or streamed datasets. [Interactive gallery](https://bokeh.pydata.org/en/latest/docs/gallery.html) ---------------------------------------------------------------------------

Installation ------------ The easiest way to install Bokeh is using the [Anaconda Python distribution](https://www.anaconda.com/what-is-anaconda/) and its included *Conda* package management system. To install Bokeh and its required dependencies, enter the following command at a Bash or Windows command prompt: ``` conda install bokeh ``` To install using pip, enter the following command at a Bash or Windows command prompt: ``` pip install bokeh ``` For more information, refer to the [installation documentation](https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html#quick-installation). Once Bokeh is installed, check out the [Getting Started](https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html#getting-started) section of the [Quickstart guide](https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html). Documentation ------------- Visit the [Bokeh site](https://bokeh.pydata.org/en/latest) for information and full documentation, or [launch the Bokeh tutorial](https://mybinder.org/v2/gh/bokeh/bokeh-notebooks/master?filepath=tutorial%2F00%20-%20Introduction%20and%20Setup.ipynb) to learn about Bokeh in live Jupyter Notebooks. Contribute to Bokeh ------------------- If you would like to contribute to Bokeh, please review the [Developer Guide](https://bokeh.pydata.org/en/latest/docs/dev_guide.html). Follow us --------- Follow us on Twitter [@bokehplots](https://twitter.com/BokehPlots)

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