Stop plotting your data - annotate your data and let it visualize itself

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Stop plotting your data - annotate your data and let it visualize itself.

.. image:: http://assets.holoviews.org/demo.gif :target: http://www.holoviews.org

HoloViews requires Param <http://ioam.github.com/param/>_ and Numpy <http://www.numpy.org/>_ and is designed to work together with Matplotlib <http://matplotlib.org/>_ or Bokeh <http://bokeh.pydata.org>, making use of the Jupyter/IPython Notebook <http://jupyter.org>.

Clone holoviews directly from GitHub with::

git clone git://github.com/ioam/holoviews.git

Please visit our website <http://ioam.github.com/holoviews/>_ for official releases, installation instructions, documentation, and many detailed example notebooks and tutorials <http://holoviews.org/Tutorials>. Additional user contributed notebooks may be found in the holoviews-contrib <https://github.com/ioam/holoviews-contrib> repository including examples that may be run live on mybinder.org <http://mybinder.org/repo/ioam/holoviews-contrib>_.

For general discussion, we have a gitter channel <https://gitter.im/ioam/holoviews>. In addition we have a wiki page <https://github.com/ioam/holoviews/wiki/Experimental-Features> describing current work-in-progress and experimental features. If you find any bugs or have any feature suggestions please file a GitHub Issue or submit a pull request.



  • Lets you build data structures that both contain and visualize your data.
  • Includes a rich library of composable elements <https://ioam.github.io/holoviews/Tutorials/Elements>_ that can be overlaid, nested and positioned with ease.
  • Supports rapid data exploration <https://ioam.github.io/holoviews/Tutorials/Exploring_Data>_ that naturally develops into a fully reproducible workflow <Tutorials/Exporting>_.
  • You can create complex animated or interactive visualizations with minimal code.
  • Rich semantics for indexing and slicing of data in arbitrarily high-dimensional spaces <https://ioam.github.io/holoviews/Tutorials/Transforming_Data>_.
  • Every parameter of every object includes easy-to-access documentation.
  • All features available in vanilla Python 2 or 3 <https://ioam.github.io/holoviews/Tutorials/Options>_, with minimal dependencies.

Support for maintainable, reproducible research

  • Supports a truly reproducible workflow by minimizing the code needed for analysis and visualization.
  • Already used in a variety of research projects, from conception to final publication.
  • All HoloViews objects can be pickled and unpickled.
  • Provides comparison utilities for testing, so you know when your results have changed and why.
  • Core data structures only depend on the numpy and param libraries.
  • Provides export and archival facilities <https://ioam.github.io/holoviews/Tutorials/Exporting>_ for keeping track of your work throughout the lifetime of a project.

Analysis and data access features

  • Allows you to annotate your data with dimensions, units, labels and data ranges.
  • Easily slice and access <https://ioam.github.io/holoviews/Tutorials/Transforming_Data>_ regions of your data, no matter how high the dimensionality.
  • Apply any suitable function to collapse your data or reduce dimensionality.
  • Helpful textual representation to inform you how every level of your data may be accessed.
  • Includes small library of common operations for any scientific or engineering data.
  • Highly extensible: add new operations to easily apply the data transformations you need.

Visualization features

  • Useful default settings make it easy to inspect data, with minimal code.
  • Powerful normalization system to make understanding your data across plots easy.
  • Build complex animations or interactive visualizations in seconds <https://ioam.github.io/holoviews/Tutorials/Exploring_Data>_ instead of hours or days.
  • Refine the visualization of your data interactively and incrementally.
  • Separation of concerns: all visualization settings are kept separate from your data objects.
  • Support for interactive tooltips/panning/zooming, via the optional mpld3 backend.

IPython Notebook support

  • Support for both IPython 2 and 3.
  • Automatic tab-completion everywhere.
  • Exportable sliders and scrubber widgets.
  • Automatic display of animated formats in the notebook or for export, including gif, webm, and mp4.
  • Useful IPython magics for configuring global display options and for customizing objects.
  • Automatic archival and export of notebooks <https://ioam.github.io/holoviews/Tutorials/Exporting>_, including extracting figures as SVG, generating a static HTML copy of your results for reference, and storing your optional metadata like version control information.

Integration with third-party libraries

  • Flexible interface to both the pandas and Seaborn libraries <https://ioam.github.io/holoviews/Tutorials/Pandas_Seaborn>_
  • Immediately visualize pandas data as any HoloViews object.
  • Seamlessly combine and animate your Seaborn plots in HoloViews rich, compositional data-structures.

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