#!/usr/bin/env python
# coding: utf-8

# <div>
# <a href="http://bokeh.pydata.org/"><img src="images/bokeh-header.png"></a>
# </div>

# Welcome to [Bokeh](http://bokeh.pydata.org/en/latest) in the Jupyter Notebook!
# 
# Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients.

# ## Quickstart
# 
# Get started with a 5-min introduction to Bokeh [here](quickstart/quickstart.ipynb).

# ## Notebook Gallery
# 
# Some examples of Bokeh's interactive plots in IPython Notebooks:
# 
# [Texas unemployment](gallery/texas.ipynb) | [Linked brushing](gallery/linked_brushing.ipynb) | [Linked panning](gallery/linked_panning.ipynb) | [Lorenz](gallery/lorenz.ipynb) | [Candlestick](gallery/candlestick.ipynb) | [Annular wedge](gallery/burtin.ipynb) | [Rectangular](gallery/rect.ipynb) | [Glucose](gallery/glucose.ipynb) | [Correlation](gallery/correlation.ipynb) | [Bollinger](gallery/bollinger.ipynb) | [Color Scatter](gallery/color_scatterplot.ipynb)
# 
# <table cellspacing="20" style="border:0px">
# <tbody style="border:0px"><tr style="border:0px">
# <td style="border:0px">  <a class="reference external" href="gallery/texas.ipynb"><img alt="texas" src="http://bokeh.pydata.org/en/latest/_images/choropleth_t.png"></a>         <span class="raw-html"></span></td>
# <td style="border:0px">  <a class="reference external" href="gallery/lorenz.ipynb"><img alt="lorenz" src="http://bokeh.pydata.org/en/latest/_images/lorenz_t.png"></a>       <span class="raw-html"></span></td>
# <td style="border:0px">  <a class="reference external" href="gallery/candlestick.ipynb"><img alt="image" src="http://bokeh.pydata.org/en/latest/_images/candlestick_t.png"></a>         <span class="raw-html"></span></td>
# <td style="border:0px">  <a class="reference external" href="gallery/burtin.ipynb"><img alt="annular" src="http://bokeh.pydata.org/en/latest/_images/burtin_t.png"></a>         <span class="raw-html"></span></td>
# <td style="border:0px">  <a class="reference external" href="gallery/vector.ipynb"><img alt="vector" src="http://bokeh.pydata.org/en/latest/_images/streamline_t.png"></a>         <span class="raw-html"></span></td>
# </tr><tr style="border:0px">
# </tbody></table>

# ## Tutorial
# 
# Start with the [Tutorial Introduction](tutorial/00%20-%20intro.ipynb) and jump to any of the specific topic sections from there.

# ## More information
# 
# For the full documentation, see http://bokeh.pydata.org/en/latest
# 
# To see the Bokeh source code, visit the GitHub repository: https://github.com/bokeh/bokeh 
# 
# Be sure to follow us on Twitter [@BokehPlots](http://twitter.com/BokehPlots), as well as on [Youtube](https://www.youtube.com/channel/UCK0rSk29mmg4UT4bIOvPYhw) and [Vine](https://vine.co/bokehplots)!

# ## Contact
# 
# For questions, please join the [Bokeh mailing list](https://groups.google.com/a/continuum.io/forum/#!forum/bokeh) or visit the [Gitter chat room](https://gitter.im/bokeh/bokeh)
# 
# You can also ask questions on StackOverflow and use the [``#bokeh`` tag](http://stackoverflow.com/questions/tagged/bokeh)
# 
# For information about commercial development, custom visualization development or embedding Bokeh in your applications, please contact [sales@continuum.io](mailto:sales@continuum.io)
# 
# To donate funds to support the development of Bokeh, please contact [info@pydata.org](mailto:info@pydata.org)

# ## Thanks
# 
# Bokeh is developed in part with funding from the DARPA XDATA program. Additionally, many thanks to [all of the Bokeh Github contributors](https://github.com/bokeh/bokeh/graphs/contributors).

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