Metadata-Version: 1.1
Name: gastrodon
Version: 0.9.3
Summary: Toolkit to display,  analyze,  and visualize data and documents based on RDF graphs and the SPARQL query language using Pandas,  Jupyter, and other Python ecosystem tools.
Home-page: https://github.com/paulhoule/gastrodon
Author: Paul Houle
Author-email: paul.houle@ontology2.com
License: MIT
Description-Content-Type: UNKNOWN
Description: gastrodon

        =========

        

        Toolkit to display, analyze, and visualize data and documents based on

        RDF graphs and the SPARQL query language using Pandas, Jupyter, and

        other Python ecosystem tools.

        

        .. figure:: art/logo-hero.png

           :alt: Gastrodon Links SPARQL to Pandas

        

           Gastrodon Links SPARQL to Pandas

        

        Gastrodon links databases that support the SPARQL protocol (`more than

        ten! <https://www.w3.org/wiki/LargeTripleStores>`__) to

        `http://pandas.pydata.org/ <Pandas>`__, a popular Python library for

        analysis of tabular data. Pandas, in turn, is connected to a vast number

        of visualization, statistics, and machine learning tools, all of which

        work with `Jupyter <https://jupyter.org/>`__ notebooks. The result is an

        ideal environment for telling stories that reveal the value of data,

        ontologies, taxonomies, and models.

        

        In addition to remote databases, Gastrodon can do SPARQL queries over

        in-memory RDF graphs (from

        `rdflib <https://github.com/RDFLib/rdflib>`__). It has facilities to

        copy subgraphs from one graph to another, making it possible to assemble

        local graphs that contain facts relevant to a particular decision, work

        on them intimately, and then store results in a permanent triple store.

        

        Seamless Data Translation

        =========================

        

        .. figure:: https://github.com/paulhoule/gastrodon/blob/master/art/logo-hero.png

           :alt: Seamless Data Translation

        

           Seamless Data Translation

        

        Gastrodon mediates between three data models: (1) RDF, (2) Pandas/NumPy,

        and (3) Native Python. Gastrodon lets you use Python variables in your

        SPARQL queries simply by adding ``?_`` to the name of your variables.

        Unlike many RDF libraries, substitution works with both local and remote

        SPARQL endpoints. Gastrodon works with the Python type system to keep

        track of details such as "is this variable a URI or a String?" so that

        you don't have to.

        

        Query Intelligence

        ==================

        

        .. figure:: https://github.com/paulhoule/gastrodon/blob/master/art/query-intelligence.png

           :alt: Query Intelligence

        

           Query Intelligence

        

        Gastrodon always has your back because it understands SPARQL. Gastrodon

        automatically keeps track of namespaces and appends ``prefix``

        declarations to your queries to keep them short and sweet. Unlike many

        RDF libraries, Gastrodon supports variable substitution for queries in

        both local and remote stores. Gastrodon identifies ``GROUP BY``

        variables and automatically makes them the index of the resulting Pandas

        DataFrames so that you can make common visualizations automatically.

        

        Error messages you can understand

        =================================

        

        Many software packages ignore error handling, which is a big mistake,

        because poor error handling gets in the way of both everyday use and the

        learning process. Instead of making excuses, Gastrodon has intelligent

        error handling which adds to the convenience of data analysis and

        visualization with Gastrodon.

        

        Jupyter native error messages

        -----------------------------

        

        .. figure:: https://github.com/paulhoule/gastrodon/blob/master/art/awful-stack-trace.png

           :alt: Awful Stack Trace

        

           Awful Stack Trace

        

        Improved Error Messages with Gastrodon

        --------------------------------------

        

        .. figure:: https://github.com/paulhoule/gastrodon/blob/master/art/good-error-message.png

           :alt: Good Error Message

        

           Good Error Message

        

        The following are reference documentation for tools you will use

        

        -  `Pandas <http://pandas.pydata.org/pandas-docs/stable/>`__

        -  `Jupyter <http://jupyter.org/index.html>`__

        -  `rdflib <https://github.com/RDFLib/rdflib#readme>`__

        -  `SPARQL <http://www.w3.org/TR/2013/REC-sparql11-query-20130321/#basicpatterns>`__

        

        Example notebooks can be found in the `notebooks <notebooks>`__

        directory.

        
Keywords: sparql rdf rdflib pandas visualization
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Database :: Front-Ends
Classifier: Topic :: Documentation :: Sphinx
Classifier: Topic :: Multimedia :: Graphics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing :: Markup :: HTML
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
