Stop plotting your data - annotate your data and let it visualize
HoloViews requires Param and
Numpy and is designed to work together with
Bokeh, making use of the Jupyter/IPython
Clone holoviews directly from GitHub with:
git clone git://github.com/ioam/holoviews.git
Please visit our website for
official releases, installation instructions, documentation, and many
detailed example notebooks and
tutorials. Additional user contributed
notebooks may be found in the
repository including examples that may be run live on
For general discussion, we have a gitter
channel. In addition we have a wiki
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.
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
- 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
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
- Easily slice and
regions of your data, no matter how high the dimensionality.
- Apply any suitable function to collapse your data or
- 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
- Highly extensible: add new operations to easily apply the data
transformations you need.
- Useful default settings make it easy to inspect data, with
- Powerful normalization system to make understanding your data across
- Build complex animations or interactive visualizations in
instead of hours or days.
- Refine the visualization of your data interactively
- Separation of concerns: all visualization settings are kept separate
from your data objects.
- Support for interactive tooltips/panning/zooming, via the optional
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
- Automatic archival and export of
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
- Immediately visualize pandas data as any HoloViews object.
- Seamlessly combine and animate your Seaborn plots in HoloViews rich,