Saravanan666/pypi-org huggingface dataset:
Package: genderdetectfrance
Detect sex from first name. Supports French, German, Italian.from   genderdetectfrance import *
print('1st letter is sex, then name freq in our DB')
print('--------')
print(getPren('Lisa'))
print(getPren('Adrian'))
print(getPren('Hans'))
print(getPren('Angelo'))
print(getPren('Ancuta'))
print(getPren('Unknown'))
print(getPren('Léa'))built by Marcel Mateescu 2022
Package: gender-detector
Gender DetectorGender detector is a Python library for guessing a person’s gender by his/her first name. This library is based on [beauvoir](https://github.com/jeremybmerrill/beauvoir) with support for United States, United Kingdom,  Argentina and Uruguay.This is still in beta stages, use with precaution© 2014 Marcos Vanetta. Available under GPL2 License. See LICENSE.DatasetsUK, fromOpenGenderTracking projectUS, fromOpenGenderTracking projectAR, fromNames query sitefrom the goverment of the City of Buenos Aires.this is a small sample, use with precaution!.UY, fromCivil registry) in Montevideo. Same as in AR,Use with precaution!How to use itNothing too fancy, just install:pipinstallgender-detectorAnd then:fromgender_detectorimportGenderDetectordetector=GenderDetector('us')# It can also be ar, uk, uy.detector.guess('Marcos')# => 'male'TODO:Add information about statisticsBetter datasets
Package: gender-determinator
Gender DeterminatorDetermines the gender of german nouns and provides some convenient methods.UsagefromgenderdeterminatorimportGenderDeterminator,Casegd=GenderDeterminator()print(gd.get_gender("Garten"))# prints "m"print(gd.get("Garten",Case.NOMINATIVE))# prints "der Garten"Gender determinator uses the eng-deu dictionary byFreeDictto determine the genders.
Package: gender-extractor
Gender extractorGender extractor is a tool that tries to guess the gender of a person given a name.I agree - there are already many projects like that, and most of them are written better (especially considering the fact that I've spent whooping 30 minutes on this one, most of which was focused on fighting the paths), but somehow I couldn't find anything on MIT license. However, if the licensing is not your concern, I invite you to use anything else (for example the greatly donehttps://github.com/tue-mdse/genderComputer).So, here it is - a name-to-gender extractor on an MIT license.Usage>>>fromgender_extractorimportGenderExtractor>>>ext=GenderExtractor()>>>print(ext.extract_gender("Andrea"))female>>>print(ext.extract_gender("Andrea","Italy"))mostlymaleDatabaseThe database of the names was taken from thehttps://github.com/tue-mdse/genderComputerproject, in which it was published under theOpen Database License.
Package: gender-guesser
This package uses the underlying data from the program “gender” by Jorg Michael (describedhere).  Its use is pretty straightforward:>>> import gender_guesser.detector as gender
>>> d = gender.Detector()
>>> print(d.get_gender(u"Bob"))
male
>>> print(d.get_gender(u"Sally"))
female
>>> print(d.get_gender(u"Pauley")) # should be androgynous
andyThe result will be one ofunknown(name not found),andy(androgynous),male,female,mostly_male, ormostly_female. The difference betweenandyandunknownis that the former is found to have the same probability to be male than to be female, while the later means that the name wasn’t found in the database.I18N is fully supported:>>> print(d.get_gender(u"\xc1lfr\xfan"))  # u"Álfrún"
femaleAdditionally, you can give preference to specific countries:>>> print(d.get_gender(u"Jamie"))
mostly_female
>>> print(d.get_gender(u"Jamie", u'great_britain'))
mostly_maleAdditionally, you can create a detector that is not case sensitive (defaultisto be case sensitive):>>> d = gender.Detector(case_sensitive=False)
>>> print(d.get_gender(u"sally"))
female
>>> print(d.get_gender(u"Sally"))
femaleTry to avoid creating many Detectors, as each creation means reading the data file.LicensesThe generator code is distributed under the GPLv3.  The data file nam_dict.txt is released under the GNU Free Documentation License.Changelog0.3.0 (2016-07-02)Removeunknown_valueinit option, since it can be implemented very easily with a wrapper if needed.Returnunknownwhen name is not found andandywhen it is valid equally for both male and female.Test README examples as doctests.Fix incorrect country-wise gender detection for non-iso886-15 names coming from line length change after data file conversion to UTF-8. See #gh2. Thanks @miquelcamprodon.0.2.0 (2015-12-06)Wire intoxto test in both Python 2 and Python 3.Python 2 and 3 compatiblity.Remove obsolete character mapper code.For previous versions, seesexmachine.CreditsThis is a fork of theSexMachinepackage byFerhat Elmas. It was created to be able to publish a Python 3 compatible version to PyPI and to be able add some more improvements without bugging the original author.
Package: gender-guesser-br
Gender Guesser BrasilPowered by DadosAbertosBrasilVersão brasileira do pacote Python para adivinhar o gênero de um nome próprio.Este pacote utiliza o DadosAbertosBrasil para capturar informações do Censo Demográfico das APIs oficiais do IBGE e calcula a probabilidade de que determinado nome próprio seja feminino ou masculino.Instalaçãopip install gender-guesser-brFazendo previsõesApós importar o pacote, crie uma instância do objetoGenerousando o nome próprio como argumento. Por fim, utilize o método__call__para conferir a previsão.>>>fromgender_guesser_brimportGenero>>>nome=Genero("gustavo")>>>nome()'masculino'É possível utilizar o argumentoufpara fazer uma previsão por unidade federativa, o que pode aumentar a precisão. Veja que "Darci" é um nome que pode receber qualquer classificação, dependendo da UF.>>>rs=Genero(nome="darci",uf="rs")>>>rs()'masculino'>>>sc=Genero(nome="darci",uf="sc")>>>sc()'provavelmente_masculino'>>>sp=Genero(nome="darci",uf="sp")>>>sp()'ambos'>>>ac=Genero(nome="darci",uf="ac")>>>ac()'feminino'>>>rr=Genero(nome="darci",uf="rr")>>>rr()'desconhecido'Os argumentosnomeeufsão case insensitive, então você pode usar letras maiúsculas e minúsculas como quiser, desde que uf seja a sigla de duas letras da UF ou o código IBGE de dois dígitos. Utilize a funçãolocalidadedo DadosAbertosBrasil para obter uma lista completa dos códigos das UFs.>>>fromDadosAbertosBrasilimportibge>>>ibge.localidades(nivel="estados")Refinando resultadosAo fazer a previsão, utilize os argumentoscorte_ambosecorte_maioriapara definir qual é a proporção mínima em que o objeto para a ter certeza de que o nome é de determinado gênero ou que é considerado de ambos os gêneros.>>>ariel=Genero("Ariel",uf="RJ")>>>ariel(corte_ambos=0.8,corte_maioria=0.9)'ambos'>>>ariel(corte_ambos=0.6,corte_maioria=0.8)'provavelmente_masculino'>>>ariel(corte_ambos=0.6,corte_maioria=0.7)'masculino'Para ter ainda mais controle, utilize as propriedadesfempara obter acesso aos números brutos.Essas propriedades são dicionários contendo o número total de habitantes do gênero correspondente que possuem aquele nome e a proporção do gênero.>>>camila=Genero("Camila")>>>camila.f{'absoluto':469851,'percentual':0.9964012147225733}>>>camila.m{'absoluto':1697,'percentual':0.003598785277426688}
Package: gender-guesser-first-names
gender-guesser with first names and full countriesChangesgender_guesserby:Works for all countriesConvert mostly_(fe)male to (fe)maleWorks for composed names by finding the first female or male and ignoring the other onesInstall$pipinstallgender-guesser-first-namesUSAGE>>>fromgender_guesser_first_namesimportgender>>>d=gender.Detector(case_sensitive=False)>>>d.get_gender('Andrea Maria',country='italy')'male'
Package: genderperformr
GenderPerformrIntroGenderPerformr is the model release from the paperIt’s going to be okay: Measuring Access to Support in Online Communitiesby Zijian Wang and David Jurgens (in proceedings of EMNLP 2018).It is the current state-of-the-art method that predicts gender from usernames based on a LSTM model built in PyTorch (as of Sept. 2018).See theproject websitefor full details, including contact information.InstallUse pipIfpipis installed, genderperformr could be installed directly from it:pip install genderperformrFrom rawgit clonethe project and do:python setup.py installDependenciespython>=3.6.0
torch>=0.4.1
numpy
unidecodeUsage and Examplepredictpredictis the core method of this package,
which takes a single username of a list of usernames, and returns a tuple of raw probabilities in[0,1](0 - Male, 1 - Female), and labels (M - Male, N - Neutral, F - Female, empty string - others).Simplest usageYou may directly importgenderperformrand use the default predict method, e.g.:>>> import genderperformr
>>> genderperformr.predict("AdamMcAdamson")
(0.019139649, 'M')Construct from classAlternatively, you may also construct the object from class, where you could customize the model path and device:>>> from genderperformr import GenderPerformr
>>> gp = GenderPerformr()

# Predict a single username
>>> gp.predict("John")
(0.087956183, 'M')

# Predict a list of names
>>> probs, labels = gp.predict(["BarryCA67", "pizzamagic", "KatieZ22"])
>>> f"Raw probabilities are {probs}"
Raw probabilities are [0.03398224 0.5439474 0.93964571]
>>> f"Labels are {labels}"
Labels are ['M', 'N', 'F']More detail on how to construct the object is available in docstrings.Model using new data partitionIf you want to use the model described in Supplemental Material using the new data partition, you may construct the object via>>> gp = GenderPerformr(is_new_model=True)All other usages remain the same.Citation@inproceedings{wang2018its,
       title={It's going to be okay: Measuring Access to Support in Online Communities},
       author={Wang, Zijian and Jurgens, David},
       booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
       year={2018}
}ContactZijian Wang (zij<last_name>@stanford.edu)David Jurgens (<last_name>@umich.edu)
Package: gendiscalpy
gendiscalpyPython bindings toGenDisCaland addition of phylogenetic tree functionGenDisCal repo:https://github.com/LM-UGent/GenDisCalGenDisCal paper:https://academic.oup.com/bioinformatics/article/36/8/2337/5695704InstallationThis package was created usingPython 3.9.pipinstallgit+https://github.com/MrTomRod/gendiscalpyThis will add two scripts to your PATH:install_gendiscalgendiscal_treeUsage in terminalinstall_gendiscalThis script downloads the GenDisCal binary and unpacks it to a path of your choosing.$ install_gendiscal 
Options:
  1: /home/user/PycharmProjects/venvs/gendiscalpy10/bin
  2: /home/user/miniconda3/condabin
  3: /home/user/bin
  4: /usr/bin
  5: /usr/sbin
Please select a path to install GenDisCal to!
1
You chose: /home/user/PycharmProjects/venvs/gendiscalpy10/bin
Installed GenDiscal here: /home/user/PycharmProjects/venvs/gendiscalpy10/bin/GenDisCalNow, you should be able to run GenDisCal in your terminal. Test withGenDisCal --version.gendiscal_treeCreate a phylogenetic tree based on GenDisCal a distance matrix. ReturnsNewick format.This script uses theupmgaalgorithm of thebiotitepackage. Seewikipediaandbiotite docs.Requires GenDisCal to be in PATH, see alsoinstall_gendiscal.Examples:gendiscal_treefrom_filesassembly1.fnaassembly2.fna
gendiscal_treefrom_filesassemblies/*.fna
gendiscal_treefrom_filesassemblies/*.fna--presetPaSiT6--methodeuclidianUsage as Python classfromgendiscalpyimportGenDisCal,GenDisCalTreefromgendiscalpy.install_gendiscalimportinstall# install GenDisCal to /home/user/bin/GenDisCalinstall(path='/home/user/bin')gdc=GenDisCal()# bin='/home/user/bin/GenDisCal'# these commands return pandas.DataFrame objectstable1=gdc.run('test-data/*.fna')table2=gdc.run('test-data/*.fna',preset='PaSiT6',method='euclidian')distance_matrix=gdc.run('test-data/*.fna',distance_matrix=True)histogram=gdc.run('test-data/*.fna',histogram=True)# this command returns a floatval=gdc.compare_two(assembly_1='test-data/FAM3257-i1-1.fna',assembly_2='test-data/FAM13496-i1-1.fna')# this command returns a string in Newick formatnewick=GenDisCalTree.from_files('test-data/*.fna')
Package: gen-doc
gen_doc- Library to generate documentationInstallationpip install gen_docWhat it does?aggregates all.pyfiles into one or multiple.mdfiles, keeping the same hierarchycollects all classes and methods with their informationparsesgoogle&sphinxpy docstrings.How to use:install the libraryin your project directory open the terminalrun the commandgen_doc initadjust config file accordinglyrun the commandgen_doc build -cDetailsGeneralUsage: gen_doc [OPTIONS] COMMAND [ARGS]...

  Utility for generating project documentation from docstrings

Options:
  -v, --version  Get library version
  -i, --init     Init gen_doc config with default parameters
  -b, --build    Build documentation by config
  --help         Show this message and exit.

Commands:
  build  Build documentation
  init   To init config file in order to generate documentation.InitUsage: gen_doc init [OPTIONS]
  To init config file in order to generate documentation.
Options:
  -f, --file-config TEXT  Config file name  [default: gen_doc.yaml]
  -o, --overwrite         To overwrite, in case file already exists
  --help                  Show this message and exit.BuildUsage: gen_doc build [OPTIONS] [[py]]
  Build documentation
Options:
  -sm, --save-mode [md]      Save mode
  -hi, --hierarchically      Extract with the same hierarchy
  -o, --overwrite            To overwrite, in case file already exists
  -p2r, --path-to-root TEXT  Path to the directory for which documentation
                             should be compiled
  -p2s, --path-to-save TEXT  Path to the directory where the documentation
                             should be saved
  -f2s, --file-to-save TEXT  Path to the directory where the documentation
                             should be saved
  -c, --config               Use config to build documentation.
  -f, --file-config TEXT     Config file name  [default: gen_doc.yaml]
  --help                     Show this message and exit.
Package: gendocs
This is a Python package for automatically building the documentation pages to
document a given Python package using Sphinx.gendocsallows users to keep all of their documentation directly within their
packages as pages are generated directly from the docstrings in the code!ConnectionsWant to see examples? Check out one of the following projects which usegendocs:PVGeo: A Python package for visualizing geophysical data in VTK and ParaViewESPA Tools: A Python package for simple loading of Landsat imagery as NumPy arraysGet StartedInstallgendocsfrom PyPI:$pipinstallgendocsCookiecutterWant to easily create a new project that will build its own documentation?
Try thegendocsCookiecutter which will prompt you for your new project’s
details and create all the needed file structures for your new, automatically
documented Python package.To create a new project, make sure you haveCookiecutterinstalled in your
virtual environment:$pipinstallcookiecutterNow you can useour Cookiecutter templateto create a new project ready
forgendocsby executing the following command and following the prompts:$cookiecutterhttps://github.com/banesullivan/cookiecutter-gendocs.gitThat’s it! Now you have a new Python project ready for automatic documentation
and deployment.UsageAlready have a Python package in need of automatic documentation? Follow these steps.This generator is built for Sphinx (RST) documentation.
To document your package, setup sphinx and aconf.pythen add the following
somewhere near the top of yourconf.py:# Import the package to document:importwonderfulpackage# Automatically generate documentation pagesfromgendocsimportGeneratorGenerator().DocumentPackages(wonderfulpackage)That’s it! That code block above is all you need to do to document your package(s) thoroughly. Now you can build the Sphinx documentation, and all docs pages will be automatically generated.Remove theEdit on GitHubButtonBe sure to remove theEdit on GitHublink from your project by followingthese steps.Make Your Own HomepageNote that you can also set up a base index file in your project for theGeneratorto append if you’d like to include a brief overview of the package.
We simply pass the filename of our README to theDocumentPackagesmethod.Let People KnowIf your project generates documentation automatically, please add a badge to your project to let people know! We hope that these badges will stir curiosity, involvement, and community contributions to thegendocspackage.Markdown:[![Documentation Built by gendocs](https://img.shields.io/badge/docs%20by-gendocs-blue.svg)](https://gendocs.readthedocs.io/en/latest/)RST / Sphinx:.. image:: https://img.shields.io/badge/docs%20by-gendocs-blue.svg
   :target: https://gendocs.readthedocs.io/en/latest/
   :alt: Documentation Built by gendocsContributeCheck outgendocsofGitHubto Contribute and make automatic documentation even better!
Package: genDocsGPT
Generate Docs using ChatGPTCLI to generate API documentation in MarkDown based on the models and api handlers for given project using ChatGPT.genDocsGPT badgeChatGPT API KeyThe genDosGPT demand a ChatGPT API Key that you can generate after create your free account inOpenAI. Tutorialhere. After this, create a.envfile in the following format:API_KEY="chatgpt_api_Key"Usageusage: genDocsGPT [-h] [-v, --verbose] -m / --model <model_paths> -a / --api <api_paths> 
[-o / --output <filename>] [-t / --token <token>] [--env <filename>] [--gpt_model <model>]

CLI to generate API documentation in MarkDown based on the models and api handlers for 
given project using ChatGPT.

options:
  -h, --help            show this help message and exit
  -v, --verbose         Show all the messages to debug
  -m / --model <model_paths>
                        Model paths. If more than one, separate with a comma
  -a / --api <api_paths>
                        API paths. If more than one, separate with a comma
  -o / --output <filename>
                        Filename to the documentation generated. Default: ./api.md
  -t / --token <token>  ChatGPT API KEY
  --env <filename>      Env file that MUST and ONLY have API_KEY="chatgpt_api_key". 
                        Default: ./.env
  --gpt_model <model>   Model that will be used by ChatGPT. Default: gpt-3.5-turbo

We do not recommend using it in non-open-source projects.StandaloneThegenDocsGPT.sh, the bash script above, should be used in projects where the documentation should be generated not frequently. You only need to copy this file,change the configuration(models, API handlers…), paste it into your project, create the .env file with the ChatGPT API Key, and run this bash script.I am using this standalone version in theDadosJusBr’s GraphQLproject which is a Node/ExpressJS using Apollo Server. So, I created a script in thepackage.jsonto run it only using:npm run genDocsGPToryarn genDocsGPTGitHub ActionThis GitHub Action will run after every commit in the main/master branch and create a pull request that you only need to approve (or not) and the documentation will be updated.ForgenDocsGPT.ymlworks fine in your project, you should set the model, API handlers paths, and other options (line 30), create, in the GitHub, the secretChatGPT_API_KEY(tutorial here), and change the workflow permissions (tutorial here) to “Read and Write permissions” and use the “Allow GitHub Action to create and approve pull requests”.For more detail, I integrated this GitHub Action in theLightweight Go Serverproject, aGo servers benchmark between Echo, Gin, and Fiber.
Package: genea
GeneaPronounced "genie". Scrape parent-child relationships from Wikipedia infoboxes.GeneaWhy Infoboxes?InstallationGetting StartedPositional ArgumentsNamed ArgumentsText OutputMotivating ExamplesLicenseWhy Infoboxes?Infoboxes give us a digest of a particular Wikipedia page, in addition to the
relational information that we'll need to build a tree.Modified infobox as seen on theWikipedia page for George WashingtonIn the image above, we can see rows of data under the "Personal Details"
section; each of these rows contain a header (bolded text) and (typically)
associated links.We'll use regular expression patterns to match with these headers, some of
which provide ancestral relationships ("Parents", in this case), some provide
descendant relationships ("Children"), and others that could provide extra
links that we can walk out from ("Relatives").Let's try out the above example.InstallationClone this repository to your local machine with git, then install with Python.gitclonehttps://github.com/shanedrabing/genea.gitcdgenea
pythonsetup.pyinstallGetting StartedRun the program with Python.pythongenea.py"George Washington""^Parent""^Child"Positional Argumentsterm: Search term. Redirects to initial Wikipedia page.pre: (optional, regex) If matched, will add ancestor.post: (optional, regex) If matched, will add descendant.Named Arguments-n [STEPS]: How many steps to walk from initial page?-e [EXTRA]: (regex) If matched, will add additional links (no relation).Text OutputANCESTORS of George Washington
â”œâ”€â”€ Augustine Washington Sr.  
â”‚   â”œâ”€â”€ Mildred Gale
â”‚   â”‚   â””â”€â”€ Augustine Warner Jr.
â”‚   â”‚       â””â”€â”€ Augustine Warner
â”‚   â””â”€â”€ Lawrence Washington
â”‚       â””â”€â”€ John Washington
â”‚           â””â”€â”€ Lawrence Washington
â””â”€â”€ Mary Washington

DESCENDANTS of George Washington
â””â”€â”€ John Parke Custis
    â”œâ”€â”€ George Washington Parke Custis
    â”‚   â”œâ”€â”€ Mary Anna Custis Lee
    â”‚   â”‚   â”œâ”€â”€ Eleanor Agnes Lee
    â”‚   â”‚   â”œâ”€â”€ George Washington Custis Lee
    â”‚   â”‚   â”œâ”€â”€ William Henry Fitzhugh Lee
    â”‚   â”‚   â”œâ”€â”€ Robert E. Lee Jr.
    â”‚   â”‚   â”œâ”€â”€ Mildred Childe Lee
    â”‚   â”‚   â”œâ”€â”€ Anne Carter Lee
    â”‚   â”‚   â””â”€â”€ Mary Custis Lee
    â”‚   â””â”€â”€ Maria Carter Syphax
    â”œâ”€â”€ Martha Parke Custis Peter
    â”œâ”€â”€ Elizabeth Custis Law
    â””â”€â”€ Eleanor Parke Custis LewisMotivating ExamplesTry out these other searches! Genea is intended to be general, meaning that any
infobox labels you find can define the relationships between pages.# how many cars succeeded the Ford Quadricycle?pythongenea.py"Ford Quadricycle""^Predecessor""^Successor"# what is the pedigree of Secretariat? (goes back to the 1700s!)pythongenea.py"Secretariat (horse)""^(Sire|Dam)$"--extra"sire"# where did Windows XP come from, where did it go?pythongenea.py"Windows XP""^(Preceded by)$""^(Succeeded by)$"# how many child companies does Disney have?pythongenea.py"Disney""Parent""(Divisions|Subsidiaries)"LicenseMIT
Package: geneabacus
GeneAbacus PythonThis repository provides Python code forGeneAbacus. Using theprofileiomodule, you can import profiles exported from GeneAbacus using thebinaryformat. Profiles are loaded intoNumpyarrays, usable for analysis.From high-throughput sequencingmapped reads(SAM/BAM),GeneAbacus:Createsprofilesrepresenting coverage depth per nucleotide,Countsreads mapped within user selected features such as chromosomes or genes.DownloadSeetagspage.Installpip3installgeneabacusReading profiles from Pythonimportgeneabacus.profileioprofiles=geneabacus.profileio.pfopen('profiles.bin.lz4','danrer_cdna_protein_coding_ensembl104.fon1.json')To get a transcript profile:profiles['ENSDART00000000486']# will returnarray([0.,0.,21.,...,0.,3.,0.],dtype=float32)LicenseGeneAbacusis distributed under the Mozilla Public License Version 2.0 (see /LICENSE).Copyright © 2015-2023 Charles E. Vejnar