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.. _gettingstarted:
===============================
Getting started with RDFLib
===============================
Installation
============
RDFLib is open source and is maintained in a
`GitHub <http://github.com/RDFLib/rdflib/>`_ repository. RDFLib releases, current and previous,
are listed on `PyPi <http://pypi.python.org/pypi/rdflib/>`_
The best way to install RDFLib is to use ``pip`` (sudo as required):
.. code-block :: bash
$ pip install rdflib
If you want the latest code to run, clone the ``main`` branch of the GitHub repo and use that or you can ``pip install``
directly from GitHub:
.. code-block :: bash
$ pip install git+https://github.com/RDFLib/rdflib.git@main#egg=rdflib
Support
=======
Usage support is available via questions tagged with ``[rdflib]`` on `StackOverflow <https://stackoverflow.com/questions/tagged/rdflib>`__
and development support, notifications and detailed discussion through the rdflib-dev group (mailing list):
http://groups.google.com/group/rdflib-dev
If you notice an bug or want to request an enhancement, please do so via our Issue Tracker in Github:
`<http://github.com/RDFLib/rdflib/issues>`_
How it all works
================
*The package uses various Python idioms
that offer an appropriate way to introduce RDF to a Python programmer
who hasn't worked with RDF before.*
The primary interface that RDFLib exposes for working with RDF is a
:class:`~rdflib.graph.Graph`.
RDFLib graphs are un-sorted containers; they have ordinary Python ``set``
operations (e.g. :meth:`~rdflib.Graph.add` to add a triple) plus
methods that search triples and return them in arbitrary order.
RDFLib graphs also redefine certain built-in Python methods in order
to behave in a predictable way. They do this by `emulating container types
<https://docs.python.org/3.8/reference/datamodel.html#emulating-container-types>`_ and
are best thought of as a set of 3-item tuples ("triples", in RDF-speak):
.. code-block:: text
[
(subject0, predicate0, object0),
(subject1, predicate1, object1),
...
(subjectN, predicateN, objectN)
]
A tiny example
==============
.. code-block:: python
from rdflib import Graph
# Create a Graph
g = Graph()
# Parse in an RDF file hosted on the Internet
g.parse("http://www.w3.org/People/Berners-Lee/card")
# Loop through each triple in the graph (subj, pred, obj)
for subj, pred, obj in g:
# Check if there is at least one triple in the Graph
if (subj, pred, obj) not in g:
raise Exception("It better be!")
# Print the number of "triples" in the Graph
print(f"Graph g has {len(g)} statements.")
# Prints: Graph g has 86 statements.
# Print out the entire Graph in the RDF Turtle format
print(g.serialize(format="turtle"))
Here a :class:`~rdflib.graph.Graph` is created and then an RDF file online, Tim Berners-Lee's social network details, is
parsed into that graph. The ``print()`` statement uses the ``len()`` function to count the number of triples in the
graph.
A more extensive example
========================
.. code-block:: python
from rdflib import Graph, Literal, RDF, URIRef
# rdflib knows about quite a few popular namespaces, like W3C ontologies, schema.org etc.
from rdflib.namespace import FOAF , XSD
# Create a Graph
g = Graph()
# Create an RDF URI node to use as the subject for multiple triples
donna = URIRef("http://example.org/donna")
# Add triples using store's add() method.
g.add((donna, RDF.type, FOAF.Person))
g.add((donna, FOAF.nick, Literal("donna", lang="en")))
g.add((donna, FOAF.name, Literal("Donna Fales")))
g.add((donna, FOAF.mbox, URIRef("mailto:donna@example.org")))
# Add another person
ed = URIRef("http://example.org/edward")
# Add triples using store's add() method.
g.add((ed, RDF.type, FOAF.Person))
g.add((ed, FOAF.nick, Literal("ed", datatype=XSD.string)))
g.add((ed, FOAF.name, Literal("Edward Scissorhands")))
g.add((ed, FOAF.mbox, Literal("e.scissorhands@example.org", datatype=XSD.anyURI)))
# Iterate over triples in store and print them out.
print("--- printing raw triples ---")
for s, p, o in g:
print((s, p, o))
# For each foaf:Person in the store, print out their mbox property's value.
print("--- printing mboxes ---")
for person in g.subjects(RDF.type, FOAF.Person):
for mbox in g.objects(person, FOAF.mbox):
print(mbox)
# Bind the FOAF namespace to a prefix for more readable output
g.bind("foaf", FOAF)
# print all the data in the Notation3 format
print("--- printing mboxes ---")
print(g.serialize(format='n3'))
A SPARQL query example
======================
.. code-block:: python
from rdflib import Graph
# Create a Graph, parse in Internet data
g = Graph().parse("http://www.w3.org/People/Berners-Lee/card")
# Query the data in g using SPARQL
# This query returns the 'name' of all ``foaf:Person`` instances
q = """
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name
WHERE {
?p rdf:type foaf:Person .
?p foaf:name ?name .
}
"""
# Apply the query to the graph and iterate through results
for r in g.query(q):
print(r["name"])
# prints: Timothy Berners-Lee
More examples
=============
There are many more :doc:`examples <apidocs/examples>` in the :file:`examples` folder in the source distribution.
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