RDFLib is a pure Python package for working with RDF. RDFLib contains most things you need to work with RDF, including:
The RDFlib community maintains many RDF-related Python code repositories with different purposes. For example:
Please see the list for all packages/repositories here:
Help with maintenance of all of the RDFLib family of packages is always welcome and appreciated.
6.x.ycurrent release and support Python 3.7+ only. Many improvements over 5.0.0
5.x.ysupports Python 2.7 and 3.4+ and is mostly backwards compatible with 4.2.2.
See https://rdflib.dev for the release overview.
See https://rdflib.readthedocs.io for our documentation built from the code. Note that there are
4.2.2 documentation versions, matching releases.
The stable release of RDFLib may be installed with Python's package management tool pip:
$ pip install rdflib
Alternatively manually download the package from the Python Package Index (PyPI) at https://pypi.python.org/pypi/rdflib
The current version of RDFLib is 6.1.1, see the
CHANGELOG.md file for what's new in this release.
With pip you can also install rdflib from the git repository with one of the following options:
$ pip install git+https://github.com/rdflib/rdflib@master
$ pip install -e git+https://github.com/rdflib/rdflib@master#egg=rdflib
or from your locally cloned repository you can install it with one of the following options:
$ python setup.py install
$ pip install -e .
RDFLib aims to be a pythonic RDF API. RDFLib's main data object is a
Graph which is a Python collection of RDF Subject, Predicate, Object Triples:
To create graph and load it with RDF data from DBPedia then print the results:
from rdflib import Graph g = Graph() g.parse('http://dbpedia.org/resource/Semantic_Web') for s, p, o in g: print(s, p, o)
The components of the triples are URIs (resources) or Literals (values).
URIs are grouped together by namespace, common namespaces are included in RDFLib:
from rdflib.namespace import DC, DCTERMS, DOAP, FOAF, SKOS, OWL, RDF, RDFS, VOID, XMLNS, XSD
You can use them like this:
from rdflib import Graph, URIRef, Literal from rdflib.namespace import RDFS, XSD g = Graph() semweb = URIRef('http://dbpedia.org/resource/Semantic_Web') type = g.value(semweb, RDFS.label)
RDFS is the RDFS namespace,
XSD the XML Schema Datatypes namespace and
g.value returns an object of the triple-pattern given (or an arbitrary one if multiple exist).
Or like this, adding a triple to a graph
g.add(( URIRef("http://example.com/person/nick"), FOAF.givenName, Literal("Nick", datatype=XSD.string) ))
The triple (in n-triples notation)
<http://example.com/person/nick> <http://xmlns.com/foaf/0.1/givenName> "Nick"^^<http://www.w3.org/2001/XMLSchema#string> . is created where the property
FOAF.givenName is the URI
XSD.string is the URI
You can bind namespaces to prefixes to shorten the URIs for RDF/XML, Turtle, N3, TriG, TriX & JSON-LD serializations:
g.bind("foaf", FOAF) g.bind("xsd", XSD)
This will allow the n-triples triple above to be serialised like this:
With these results:
PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> <http://example.com/person/nick> foaf:givenName "Nick"^^xsd:string .
New Namespaces can also be defined:
dbpedia = Namespace('http://dbpedia.org/ontology/') abstracts = list(x for x in g.objects(semweb, dbpedia['abstract']) if x.language=='en')
See also ./examples
The library contains parsers and serializers for RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, JSON-LD, RDFa and Microdata.
The library presents a Graph interface which can be backed by any one of a number of Store implementations.
This core RDFLib package includes store implementations for in-memory storage and persistent storage on top of the Berkeley DB.
A SPARQL 1.1 implementation is included - supporting SPARQL 1.1 Queries and Update statements.
RDFLib is open source and is maintained on GitHub. RDFLib releases, current and previous are listed on PyPI
Multiple other projects are contained within the RDFlib "family", see https://github.com/RDFLib/.
Run the test suite with
Run the test suite and generate a HTML coverage report with
Run the test suite inside a Docker container for cross-platform support. This resolves issues such as installing BerkeleyDB on Windows and avoids the host and port issues on macOS.
Tip: If the underlying Dockerfile for the test runner changes, use
Run the test suite inside a Docker container with HTML coverage report.
Once tests have produced HTML output of the coverage report, view it by running:
pytest --cov --cov-report term --cov-report html python -m http.server --directory=htmlcov
RDFLib survives and grows via user contributions! Please read our contributing guide to get started. Please consider lodging Pull Requests here:
You can also raise issues here:
For general "how do I..." queries, please use https://stackoverflow.com and tag your question with
rdflib. Existing questions:
If you want to contact the rdflib maintainers, please do so via:
Source Code: https://github.com/RDFLib/rdflib
License: BSD-3-Clause License
March 25, 2021 Deepak@321 0 Comments
Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.
Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:
Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.
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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
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Module: It is a simple Python file that contains collections of functions and global variables and has a “.py” extension file. It’s an executable file and we have something called a “Package” in Python to organize all these modules.
Package: It is a simple directory which has collections of modules, i.e., a package is a directory of Python modules containing an additional init.py file. It is the init.py which maintains the distinction between a package and a directory that contains a bunch of Python scripts. A Package simply is a namespace. A package can also contain sub-packages.
When we import a module or a package, Python creates a corresponding object which is always of type module . This means that the dissimilarity is just at the file system level between module and package.
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What is Pyenv?
Pyenv is a fantastic tool for installing and managing multiple Python versions. It enables a developer to quickly gain access to newer versions of Python and keeps the system clean and free of unnecessary package bloat. It also offers the ability to quickly switch from one version of Python to another, as well as specify the version of Python a given project uses and can automatically switch to that version. This tutorial covers how to install pyenv on Ubuntu 18.04.
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When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
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