bandicoot

by computationalprivacy

computationalprivacy / bandicoot

an open-source python toolbox to analyze mobile phone metadata

206 Stars 56 Forks Last release: 5 months ago (0.6) MIT License 353 Commits 6 Releases

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bandicoot

.. image:: https://img.shields.io/pypi/v/bandicoot.svg :target: https://pypi.python.org/pypi/bandicoot :alt: Version

.. image:: https://img.shields.io/pypi/l/bandicoot.svg :target: https://github.com/computationalprivacy/bandicoot/blob/master/LICENSE :alt: MIT License

.. image:: https://img.shields.io/pypi/dm/bandicoot.svg :target: https://pypi.python.org/pypi/bandicoot :alt: PyPI downloads

.. image:: https://img.shields.io/travis/computationalprivacy/bandicoot.svg :target: https://travis-ci.org/computationalprivacy/bandicoot :alt: Continuous integration

.. begin

bandicoot (http://bandicoot.mit.edu) is Python toolbox to analyze mobile phone metadata. It provides a complete, easy-to-use environment for data-scientist to analyze mobile phone metadata. With only a few lines of code, load your datasets, visualize the data, perform analyses, and export the results.

.. image:: https://raw.githubusercontent.com/computationalprivacy/bandicoot/master/docs/_static/bandicoot-dashboard.png :alt: Bandicoot interactive visualization


Where to get it

The source code is currently hosted on Github at https://github.com/computationalprivacy/bandicoot. Binary installers for the latest released version are available at the Python package index:

http://pypi.python.org/pypi/bandicoot/

And via

easy_install
:

.. code-block:: sh

easy_install bandicoot

or

pip
:

.. code-block:: sh

pip install bandicoot

Dependencies

bandicoot has no dependencies, which allows users to easily compute indicators on a production machine. To run tests and compile the visualization, optional dependencies are needed:

  • nose 
    ,
    numpy 
    ,
    scipy 
    , and
    networkx 
    for tests,
  • npm 
    _ to compile the js and css files of the dashboard.

Licence

MIT


Documentation

The official documentation is hosted on http://bandicoot.mit.edu/docs. It includes a quickstart tutorial, a detailed reference for all functions, and guides on how to use and extend bandicoot. You can also check out our

interactive training notebooks 
_ to learn how to download your own data from your mobile phone and load it into bandicoot to visualize it or to learn how to use bandicoot indicators in scikit-learn.

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