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# Utilities and convenience functions

For RDF programming, RDFLib and Python may not execute the fastest, but we try hard to make it the fastest and most convenient way to write!

This is a collection of hints and pointers for hassle free RDF-coding.

## User-friendly labels

Use `label()` to quickly look up the RDFS label of something, or better use `preferredLabel()` to find a label using several different properties (i.e. either `rdfs:label`, `skos:preferredLabel`, `dc:title`, etc.).

## Functional properties

Use `value()` and `set()` to work with functional properties, i.e. properties than can only occur once for a resource.

## Slicing graphs

Python allows slicing arrays with a `slice` object, a triple of `start`, `stop` index and step-size:

    >>> range(10)[2:9:3]
    [2, 5, 8]

RDFLib graphs override `__getitem__` and we pervert the slice triple to be a RDF triple instead. This lets slice syntax be a shortcut for `triples()`, `subject_predicates()`, `contains()`, and other Graph query-methods:

    graph[:]
    # same as
    iter(graph)

    graph[bob]
    # same as
    graph.predicate_objects(bob)

    graph[bob : FOAF.knows]
    # same as
    graph.objects(bob, FOAF.knows)

    graph[bob : FOAF.knows : bill]
    # same as
    (bob, FOAF.knows, bill) in graph

    graph[:FOAF.knows]
    # same as
    graph.subject_objects(FOAF.knows)

    ...

See `examples.slice` for a complete example.

Note

Slicing is convenient for run-once scripts of playing around in the Python `REPL`. However, since slicing returns tuples of varying length depending on which parts of the slice are bound, you should be careful using it in more complicated programs. If you pass in variables, and they are `None` or `False`, you may suddenly get a generator of different length tuples back than you expect.

## SPARQL Paths

SPARQL property paths are possible using overridden operators on URIRefs. See `examples.foafpaths` and `rdflib.paths`.

## Serializing a single term to N3

For simple output, or simple serialisation, you often want a nice readable representation of a term. All terms have a `.n3(namespace_manager = None)` method, which will return a suitable N3 format:

    >>> from rdflib import Graph, URIRef, Literal, BNode
    >>> from rdflib.namespace import FOAF, NamespaceManager

    >>> person = URIRef('http://xmlns.com/foaf/0.1/Person')
    >>> person.n3()
    u'<http://xmlns.com/foaf/0.1/Person>'

    >>> g = Graph()
    >>> g.bind("foaf", FOAF)

    >>> person.n3(g.namespace_manager)
    u'foaf:Person'

    >>> l = Literal(2)
    >>> l.n3()
    u'"2"^^<http://www.w3.org/2001/XMLSchema#integer>'

    >>> l.n3(g.namespace_manager)
    u'"2"^^xsd:integer'

## Parsing data from a string

You can parse data from a string with the `data` param:

    graph.parse(data = '<urn:a> <urn:p> <urn:b>.', format='n3')

## Commandline-tools

RDFLib includes a handful of commandline tools, see `rdflib.tools`.

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