gastrodon 1.0.0

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.

gastrodon

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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.

Gastrodon links databases that support the SPARQL protocol (more than ten!) to

http://pandas.pydata.org/, 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 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). 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

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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

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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

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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

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Improved Error Messages with Gastrodon

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Getting Started

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Installation

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Gastrodon requires Python 3.7 and is registered in the Python Package Index and can

be installed by typing:

pip install gastrodon

on the command line. Note: Gastrodon downloads packages it requires via pip. If you are running Anancoda

(which works great with Gastrodon) you have a second package manager, running parallel with pip, which can install

better versions of important software packages than the ones you can get from pip. In Anaconda, you should type the following

to create an environment for gastrodon:

conda create -n gastrodonSandbox python=3.6 anaconda

activate gastrodonSandbox

conda install jupyter IPython pandas matplotlib

pip install gastrodon

Documentation

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The major documentation resources for Gastrodon itself are:

• Example Notebooks

• Gastrodon Reference Documentation

The following are reference documentation for tools you will use:

• Pandas

• Jupyter

• rdflib

• SPARQL