Metadata-Version: 1.1
Name: gastrodon
Version: 0.9.2
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
