{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to [Bokeh](http://bokeh.pydata.org/en/latest) in IPython Notebook!\n", "\n", "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quickstart\n", "\n", "Get started with a 5-min introduction to Bokeh [here](../master/quickstart/quickstart.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notebook Gallery\n", "\n", "Some examples of Bokeh's interactive plots in IPython Notebooks:\n", "\n", "[Texas unemployment](../master/gallery/texas.ipynb) | [Linked brushing](../master/gallery/linked_brushing.ipynb) | [Lorenz](../master/gallery/lorenz.ipynb) | [Candlestick](../master/gallery/candlestick.ipynb) | [Annular wedge](../master/gallery/burtin.ipynb) | [Vector](../master/gallery/vector.ipynb) | [Rectangular](../master/gallery/rect.ipynb) | [Glyphs](../master/gallery/glyphs.ipynb) | [Glucose](../master/gallery/glucose.ipynb) | [Correlation](../master/gallery/correlation.ipynb) | [Bollinger](../master/gallery/bollinger.ipynb)\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\"texas\" \"lorenz\" \"image\" \"annular\" \"vector\"
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial\n", "\n", "* [Introduction](../master/tutorial/00 - intro.ipynb)\n", "* [Basic Plotting](../master/tutorial/01 - plotting.ipynb)\n", "* [High Level Charts](../master/tutorial/02 - charts.ipynb)\n", "* [Low Level Models](../master/tutorial/03 - models.ipynb)\n", "* [Styling and Visual Appearance](../master/tutorial/04 - styling.ipynb)\n", "* [Sharing and Embedding Bokeh Plots](../master/tutorial/05 - sharing.ipynb)\n", "* [Adding Interactions](../master/tutorial/06 - interactions.ipynb)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More information\n", "\n", "For the full documentation, see http://bokeh.pydata.org/en/latest\n", "\n", "To see the Bokeh source code, visit the GitHub repository: https://github.com/bokeh/bokeh\n", "\n", "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)!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contact\n", "\n", "For questions, please join the bokeh mailing list: https://groups.google.com/a/continuum.io/forum/#!forum/bokeh\n", "\n", "You can also ask questions on StackOverflow and use the ``#bokeh`` tag: http://stackoverflow.com/questions/tagged/bokeh\n", "\n", "For information about commercial development, custom visualization development or embedding Bokeh in your applications, please contact [sales@continuum.io](mailto:sales@continuum.io)\n", "\n", "To donate funds to support the development of Bokeh, please contact [info@pydata.org](mailto:info@pydata.org)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Thanks\n", "\n", "Bokeh is developed in part with funding from the [DARPA](http://www.darpa.mil) [XDATA](http://www.darpa.mil/Our_Work/I2O/Programs/XDATA.aspx) program. Additionally, many thanks to [all of the Bokeh Github contributors](ttps://github.com/bokeh/bokeh/graphs/contributors)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }