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 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 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 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 fully interactive plots using the Bokeh backend.

Jupyter Notebook support

  • Support for all recent releases of IPython and Jupyter Notebooks.

  • Automatic tab-completion everywhere.

  • Exportable sliders and scrubber widgets.

  • Custom interactivity using streams and notebook comms to dynamically updating plots.

  • 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, including extracting figures as SVG, generating a static HTML copy of your results for reference, and storing your optional metadata like version control information.