Saravanan666/pypi-org huggingface dataset:
Package: wikinetworks
WikiNetworksWikiNetworks is a user-friendly Python package to process pathways from the WikiPathways database into machine-readable network representations.SummarySummary: WikiPathways is a database of 2979 biological pathways across 31 species created using the drawing software PathVisio. These pathways are not directly usable for network-based topological analyses due to differences in curation styles and drawings. We developed the WikiNetworks package to standardize and construct directed networks from these pathway representations.
The WikiNetworks software reconstructs networks from these pathways by combining geometric information and manual annotations from the hand-drawn pathways. WikiNetworks performs significantly better than existing tools. This enables the use of high-quality WikiPathways resources for network-based topological analysis of high-throughput data.  WikiNetworks allows users to (a) download manually created gpml files from WikiPathways by specifying pathway id and (b) convert them to machine-interpretable networks suitable for use with pathway/network analysis tools and (c) for import into pathway visualization tools.Availability and Implementation:WikiNetworks is written entirely in Python and is available on github.com/Thakar-Lab and on pip. Tested for use with Python 3.7 in Windows and Linux environments.Contact:Juilee_Thakar@URMC.Rochester.edu;Mukta_Palshikar@URMC.Rochester.eduBug Tracker:https://github.com/Thakar-Lab/WikiNetworks/issuesTutorials:https://github.com/Thakar-Lab/WikiNetworks/tree/main/Tutorials
Package: wikipack
wikipackThis pack adds a small Wiki to Royalnet, allowing communities to create their own small Wikis.Configuration options[Packs."wikipack"]# The roles that are authorized by default to complete certain actions.# Setting them to * disables the authentication requirement, allowing unauthenticated users that privilege[Packs."wikipack".roles]# Users with this role will be able to view wiki pages that do not have a different role set.view="*"# Users with this role will be able to create new wiki pages.create="wiki_create"# Users with this role will be able to edit wiki pages that do not have a different role set.edit="wiki_edit"# Users with this role will be able to delete wiki pages.delete="wiki_delete"# Users with this role will override all other privileges.admin="wiki_admin"
Package: wikipedia
Wikipediais a Python library that makes it easy to access and parse
data from Wikipedia.Search Wikipedia, get article summaries, get data like links and images
from a page, and more. Wikipedia wraps theMediaWiki
APIso you can focus on using
Wikipedia data, not getting it.>>>importwikipedia>>>printwikipedia.summary("Wikipedia")# Wikipedia (/ˌwɪkɨˈpiːdiə/ or /ˌwɪkiˈpiːdiə/ WIK-i-PEE-dee-ə) is a collaboratively edited, multilingual, free Internet encyclopedia supported by the non-profit Wikimedia Foundation...>>>wikipedia.search("Barack")# [u'Barak (given name)', u'Barack Obama', u'Barack (brandy)', u'Presidency of Barack Obama', u'Family of Barack Obama', u'First inauguration of Barack Obama', u'Barack Obama presidential campaign, 2008', u'Barack Obama, Sr.', u'Barack Obama citizenship conspiracy theories', u'Presidential transition of Barack Obama']>>>ny=wikipedia.page("New York")>>>ny.title# u'New York'>>>ny.url# u'http://en.wikipedia.org/wiki/New_York'>>>ny.content# u'New York is a state in the Northeastern region of the United States. New York is the 27th-most exten'...>>>ny.links[0]# u'1790 United States Census'>>>wikipedia.set_lang("fr")>>>wikipedia.summary("Facebook",sentences=1)# Facebook est un service de réseautage social en ligne sur Internet permettant d'y publier des informations (photographies, liens, textes, etc.) en contrôlant leur visibilité par différentes catégories de personnes.Note: this library was designed for ease of use and simplicity, not for advanced use. If you plan on doing serious scraping or automated requests, please usePywikipediabot(or one of the other more advancedPython MediaWiki API wrappers), which has a larger API, rate limiting, and other features so we can be considerate of the MediaWiki infrastructure.InstallationTo install Wikipedia, simply run:$ pip install wikipediaWikipedia is compatible with Python 2.6+ (2.7+ to run unittest discover) and Python 3.3+.DocumentationRead the docs athttps://wikipedia.readthedocs.org/en/latest/.QuickstartFull APITo run tests, clone therespository on GitHub, then run:$ pip install -r requirements.txt
$ bash runtests  # will run tests for python and python3
$ python -m unittest discover tests/ '*test.py'  # manual stylein the root project directory.To build the documentation yourself, after installing requirements.txt, run:$ pip install sphinx
$ cd docs/
$ make htmlLicenseMIT licensed. See theLICENSE
filefor
full details.Creditswiki-apiby
@richardasaurus for inspiration@nmoroze and @themichaelyang for feedback and suggestionsTheWikimedia
Foundationfor giving
the world free access to data
Package: wikipedia2vecsm
Wikipedia2VecWikipedia2Vec is a tool used for obtaining embeddings (or vector representations) of words and entities (i.e., concepts that have corresponding pages in Wikipedia) from Wikipedia.
It is developed and maintained byStudio Ousia.This tool enables you to learn embeddings of words and entities simultaneously, and places similar words and entities close to one another in a continuous vector space.
Embeddings can be easily trained by a single command with a publicly available Wikipedia dump as input.This tool implements theconventional skip-gram modelto learn the embeddings of words, and its extension proposed inYamada et al. (2016)to learn the embeddings of entities.
This tool has been used in several state-of-the-art NLP models such asentity linking,named entity recognition,knowledge graph completion,entity relatedness, andquestion answering.This tool has been tested on Linux, Windows, and macOS.An empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is availablehere.Documentation and pretrained embeddings for 12 languages (English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) are available online athttp://wikipedia2vec.github.io/.Basic UsageWikipedia2Vec can be installed via PyPI:%pipinstallwikipedia2vecWith this tool, embeddings can be learned by running atraincommand with a Wikipedia dump as input.
For example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:%wgethttps://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
%wikipedia2vectrainenwiki-latest-pages-articles.xml.bz2MODEL_FILEThen, the learned embeddings are written toMODEL_FILE.
Note that this command can take many optional parameters.
Please refer toour documentationfor further details.ReferenceIf you use Wikipedia2Vec in a scientific publication, please cite the following paper:Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji,Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia.@article{yamada2018wikipedia2vec,
  title={Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia},
  author={Yamada, Ikuya and Asai, Akari and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
  journal={arXiv preprint 1812.06280},
  year={2018}
}LicenseApache License 2.0
Package: wikipedia2vecSM002583
Wikipedia2VecWikipedia2Vec is a tool used for obtaining embeddings (or vector representations) of words and entities (i.e., concepts that have corresponding pages in Wikipedia) from Wikipedia.
It is developed and maintained byStudio Ousia.This tool enables you to learn embeddings of words and entities simultaneously, and places similar words and entities close to one another in a continuous vector space.
Embeddings can be easily trained by a single command with a publicly available Wikipedia dump as input.This tool implements theconventional skip-gram modelto learn the embeddings of words, and its extension proposed inYamada et al. (2016)to learn the embeddings of entities.
This tool has been used in several state-of-the-art NLP models such asentity linking,named entity recognition,knowledge graph completion,entity relatedness, andquestion answering.This tool has been tested on Linux, Windows, and macOS.An empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is availablehere.Documentation and pretrained embeddings for 12 languages (English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) are available online athttp://wikipedia2vec.github.io/.Basic UsageWikipedia2Vec can be installed via PyPI:%pipinstallwikipedia2vecWith this tool, embeddings can be learned by running atraincommand with a Wikipedia dump as input.
For example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:%wgethttps://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
%wikipedia2vectrainenwiki-latest-pages-articles.xml.bz2MODEL_FILEThen, the learned embeddings are written toMODEL_FILE.
Note that this command can take many optional parameters.
Please refer toour documentationfor further details.ReferenceIf you use Wikipedia2Vec in a scientific publication, please cite the following paper:Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji,Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia.@article{yamada2018wikipedia2vec,
  title={Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia},
  author={Yamada, Ikuya and Asai, Akari and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
  journal={arXiv preprint 1812.06280},
  year={2018}
}LicenseApache License 2.0
Package: wikipedia2vec-SM002583
Wikipedia2VecWikipedia2Vec is a tool used for obtaining embeddings (or vector representations) of words and entities (i.e., concepts that have corresponding pages in Wikipedia) from Wikipedia.
It is developed and maintained byStudio Ousia.This tool enables you to learn embeddings of words and entities simultaneously, and places similar words and entities close to one another in a continuous vector space.
Embeddings can be easily trained by a single command with a publicly available Wikipedia dump as input.This tool implements theconventional skip-gram modelto learn the embeddings of words, and its extension proposed inYamada et al. (2016)to learn the embeddings of entities.
This tool has been used in several state-of-the-art NLP models such asentity linking,named entity recognition,knowledge graph completion,entity relatedness, andquestion answering.This tool has been tested on Linux, Windows, and macOS.An empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is availablehere.Documentation and pretrained embeddings for 12 languages (English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) are available online athttp://wikipedia2vec.github.io/.Basic UsageWikipedia2Vec can be installed via PyPI:%pipinstallwikipedia2vecWith this tool, embeddings can be learned by running atraincommand with a Wikipedia dump as input.
For example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:%wgethttps://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
%wikipedia2vectrainenwiki-latest-pages-articles.xml.bz2MODEL_FILEThen, the learned embeddings are written toMODEL_FILE.
Note that this command can take many optional parameters.
Please refer toour documentationfor further details.ReferenceIf you use Wikipedia2Vec in a scientific publication, please cite the following paper:Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji,Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia.@article{yamada2018wikipedia2vec,
  title={Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia},
  author={Yamada, Ikuya and Asai, Akari and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
  journal={arXiv preprint 1812.06280},
  year={2018}
}LicenseApache License 2.0
Package: wikipedia-cli
A command-line interface for fetching summaries of Wikipedia pages. It is built
over Jonathan Goldsmith’swikipedia library.Takes a query to Wikipedia as an argument and an optional language flag.InstallationThe program is available onpypi,
so you can just run:$ pip install wikipedia-cliExamplesThe language default is English:$ wikipedia helsinki

Helsinki

Helsinki (/hɛlˈsɪŋki/; Finnish pronunciation: [ˈhelsiŋki]; Swedish:
Helsingfors) is the capital and largest city of Finland. It is in the
...
and worst cities to live in globally, Helsinki placed among the
world's top ten cities.

https://en.wikipedia.org/wiki/Helsinki

Open URL (y/n)?The desired language is specified with the-lflag:$ wikipedia -l fi helsinki

Helsinki

Helsinki (ruots. Helsingfors) on Suomen pääkaupunki ja Uudenmaan
maakuntakeskus. Se sijaitsee Suomenlahden pohjoisrannalla Uudenmaan
...
vuonna 1640. Helsingistä tuli Suomen suuriruhtinaskunnan pääkaupunki
vuonna 1812 ja Suomen pääkaupunki maan itsenäistyessä vuonna 1917.

https://fi.wikipedia.org/wiki/Helsinki

Open URL (y/n)?
Package: wikipedia-downloader
Wikipedia downloaderwikipedia_downloaderis a Python module that makes it easy to download Wikipedia data dumps.InstallationTo installwikipedia_downloader, simply run:pip install wikipedia_downloaderDocumentationFunctionswikipedia_downloader.download_sql_dump(language,file,dump="latest",target_dir=".")Downloads and decompresses a Wikipedia SQL dump.Arguments:language: Wikipedia name (language code).file: File name.dump: Dump version.target_dir: Target directory.Exampleimportwikipedia_downloaderaswpdwpd.download_sql_dump("en","pagelinks",dump="20190101",target_dir="./dumps")wikipedia_downloader.get_dataframe(language,file,dump="latest",select=None,where=None)Builds a pandas.DataFrame from a Wikipedia SQL dump.Arguments:language: Wikipedia name (language code).file: File name.dump: Dump version.select: Columns to be kept.where: Functions used to filter records.Returns:pandas.DataFrameExampleimportwikipedia_downloaderaswpdselect=["page_id","page_namespace","page_title"]where={"page_namespace":lambdax:x==0orx==14}df=wpd.get_dataframe("en","page",dump="20190101",select=select,where=where)LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.
Package: wikipedia-multistream-extractor
WikipediaMultistreamExtractorThis is a simple application that extracts articles from Wikipedia backup files.Installationpip install wikipedia_multistream_extractorUsageFrom the CLI:wikipedia_multistream_extractor SRC DESTAs a library:from wikipedia_multistream_extractor import dump_wiki_pages, extract_wiki_pages


dump_wiki_pages(src, dst, preprocessor=None, writer=None)

# Or

pages = extract_wiki_pages(src)You can pass apreprocessorto dump_wiki_pages if you wanted to transform the data.Your function should accept two argumentspageandtitle.It should return the modifiedpageas a str, unless your writer expects something different.If you return None the page will be skipped by the writer.You can also pass awriterif the intended output is not xml.Your function should accept four argumentspage,title,dest_pathandsafe_title.IF you pass awriterit's up to you two write the file, not further action is taken.safe_titleshouldbe a safe filename to use when writing the file,titleis almost certainly not.This library was built on Windows using Python 3.9.13.Itshouldwork on other platforms but it has not beentested.LicenseThe license is MIT, see the LICENSE file for details.ContributingPRs welcome, please have type annotations and docstrings.TodosExtracting a single article, based on the index offsets.Extracting the index files.Create a UI that displays the index file contents and allows the users to select articles.Extracting multiple articles at offsets.Use multiprocessing?Write some tests.
Package: wikipedia-ner
Wikipedia NER-------------Tool to train and obtain named entity recognition labeled examplesfrom Wikipedia dumps.Usage in [IPython notebook](http://nbviewer.ipython.org/github/JonathanRaiman/wikipedia_ner/blob/master/Wikipedia%20to%20Named%20Entity%20Recognition.ipynb) (*nbviewer* link).## UsageHere is an example usage with the first 200 articles from the english wikipedia dump (dated lated 2013):parseresult = wikipedia_ner.parse_dump("enwiki.bz2",max_articles = 200)most_common_category = wikipedia_ner.ParsedPage.categories_counter.most_common(1)[0][0]most_common_category_children = [parseresult.index2target[child] for child in list(wikipedia_ner.ParsedPage.categories[most_common_category].children)]"In '%s' the children are %r" % (most_common_category,", ".join(most_common_category_children))#=> "In 'Category : Member states of the United Nations' the children are 'Afghanistan, Algeria, Andorra, Antigua and Barbuda, Azerbaijan, Angola, Albania'"
Package: wikipedia-ql
WikipediaQL: querying structured data from WikipediaWikipediaQLis anexperimental query languageand, executable script, and Python library for querying structured data from Wikipedia. It looks like this:$ wikipedia_ql --page "Guardians of the Galaxy (film)" \
    '{
      page@title as "title";
      section[heading="Cast"] as "cast" >> {
          li >> text:matches("^(.+?) as (.+?):") >> {
              text-group[group=1] as "actor";
              text-group[group=2] as "character"
          }
      };
      section[heading="Critical response"] >> {
          sentence:contains("Rotten Tomatoes") as "RT ratings" >> {
              text:matches("\d+%") as "percent";
              text:matches("(\d+) (critic|review)") >> text-group[group=1] as "reviews";
              text:matches("[\d.]+/10") as "overall"
          }
      }
    }'

title: Guardians of the Galaxy (film)
RT ratings:
  overall: 7.8/10
  percent: 92%
  reviews: '334'
cast:
- actor: Chris Pratt
  character: Peter Quill / Star-Lord
- actor: Zoe Saldaña
  character: Gamora
...Read full README.md on GitHub
Package: wikipedia-searcher
wikipedia-searcherwikipedia-searcheris a library for scraping wikipedia information.>>>fromwikisearch.wikisearcherimportWikiSearcher>>>searcher=WikiSearcher()>>>searcher.search('React.js')'React (also known as React.js or ReactJS) is a free and open-source front-end JavaScript library[3] for building user interfaces or UI components.'If a search has has many options it returns a dictionary with a list of articles. You can can also pass the article to search.>>>frompprintimportpprint>>>search_result=searcher.search('The Hills')>>>pprint(search_result)>>>{'Places':[<wikisearch.article.ArticleLinkobjectat0x00000208A22EFAC0>,<wikisearch.article.ArticleLinkobjectat0x00000208A22EF7F0>,<wikisearch.article.ArticleLinkobjectat0x00000208A22EF6A0>,<wikisearch.article.ArticleLinkobjectat0x00000208A22EF400>,<wikisearch.article.ArticleLinkobjectat0x00000208A22EF3D0>,<wikisearch.article.ArticleLinkobjectat0x00000208A22EF370>],'Popular culture':[<wikisearch.article.ArticleLinkobjectat0x00000208A22EF940>,<wikisearch.article.ArticleLinkobjectat0x00000208A22EF880>],'See also':[<wikisearch.article.ArticleLinkobjectat0x00000208A22EF340>,<wikisearch.article.ArticleLinkobjectat0x00000208A22EF8E0>]}>>>article=search_result['Places'][0]>>>article.name'Santa Monica Mountains'>>>article.link'/wiki/Santa_Monica_Mountains'>>>searcher.search(article)'The Santa Monica Mountains is a coastal mountain range in Southern....'How to InstallRequests is available on PyPI:$ pip install wikipedia-searcher
Package: wikipedia-sections
Wikipedia with sectionsis a Python library that makes it easy to access and parse
data from Wikipedia.This is a fork from Jonathan Goldsmith’sWikipedia package>>>importwikipedia>>>ny=wikipedia.page("New York")>>>ny.sections[:5]# ['Etymology', 'History', 'Early history', 'Dutch rule', 'English rule']InstallationTo install Wikipedia, simply run:$ pip install wikipedia_sectionsWikipedia is compatible with Python 2.6+ (2.7+ to run unittest discover) and Python 3.3+.DocumentationSeehttps://github.com/goldsmith/Wikipediafor full dcumentation.LicenseMIT licensed. See theLICENSE
filefor
full details.CreditsWikipediaby Jonathan Goldsmith for the original repositorywiki-apiby
@richardasaurus for inspiration@nmoroze and @themichaelyang for feedback and suggestionsTheWikimedia
Foundationfor giving
the world free access to data
Package: wikipediasummary
Wikipedia SummaryA small utility package I wrote for a side-project for fetching summaries of Wikipedia pages.InstallationYou can install the package with:pip install wikipediasummaryUsagefrom wikipedia_summary import WikipediaSummary
wiki = WikipediaSummary()
eindje = wiki.get_summary("Eindhoven")

>>> eindje.url
https://en.wikipedia.org/wiki/Eindhoven
>>> eindje.thumbnail_url
https://upload.wikimedia.org/wikipedia/commons/thumb/0/07/Lichttoren_Eindhoven_1_-_Cropped.jpg/320px-Lichttoren_Eindhoven_1_-_Cropped.jpg
>>> eindje.summary
Eindhoven is a city and municipality in the Netherlands, located in the southern province of North Brabant of which it is its largest. With a population of 238,326 on 1 January 2022, it is the fifth-largest city of the Netherlands and the largest outside the Randstad conurbation.
Package: wikipit
wikipitA Command Line Tool to Search Wikipedia in the terminal.InstallationUsageExamplesFlagsOutputSupport DevelopmentInstallation$ pip install wikipitUsageSyntax:$ wikipit <query> [-flags]Quotes are required for multi-word queries.Examples$ wikipit wikipedia$ wikipit "Miguel Cabrera"$ wikipit linux -b$ wikipit "Miguel Cabrera" -l 8FlagsFlagDescription--l numSet line wrap length tonum-bOpen full Wikipedia article in default browser--all-aPrint all sections of the article (the full page). Recommended to pipe into a reader e.g.less--lang langCode-e langCodeSpecify language;langCodeis anHTML ISO language codeOutputThe output will be the paragraphs of the wikipedia article before the table of contents.
Line length is neatly wrapped based on your terminal's window size, For example:$ wikipit "Miguel Cabrera"
 José Miguel Cabrera Torres (born April 18, 1983), commonly known as Miguel 
 Cabrera and nicknamed "Miggy", is a Venezuelan professional baseball player. 
 He is the first baseman for the Detroit Tigers of Major League Baseball (MLB).
 Since his debut in 2003 he has been a two-time American League (AL) Most 
 Valuable Player (MVP) award winner, a four-time AL batting champion, and an 
 11-time MLB All-Star. He has played at first and third base for most of his 
 major league career, but primarily played left and right field before 2006. He 
 claimed the 17th MLB Triple Crown in 2012, the first to do so in 45 seasons. 
 In Venezuelan Winter League, Liga de Beisbol Profesional de Venezuela, Cabrera was
 signed by Tigres de Aragua at 16 years old. He batted his first hit inSupport developmentIf you liked this, donate to the cause.