holoviews

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

HoloViews requires Param and Numpy and is designed to work together with Matplotlib or Bokeh, making use of the Jupyter/IPython Notebook.

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.

For general discussion, we have a gitter channel. In addition we have a wiki page 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.

Features

Overview

• Lets you build data structures that both contain and visualize your data.
• Includes a rich library of composable elements that can be overlaid, nested and positioned with ease.
• Supports rapid data exploration that naturally develops into a fully reproducible workflow.
• You can create complex animated or interactive visualizations with minimal code.
• Rich semantics for indexing and slicing of data in arbitrarily high-dimensional spaces.
• Every parameter of every object includes easy-to-access documentation.
• All features available in vanilla Python 2 or 3, 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 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 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, 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
• Immediately visualize pandas data as any HoloViews object.
• Seamlessly combine and animate your Seaborn plots in HoloViews rich, compositional data-structures.