Saravanan666/pypi-org huggingface dataset:
Package: numerize
[![Build Status](https://travis-ci.org/davidsa03/numerize.svg?branch=master)](https://travis-ci.org/davidsa03/numerize)![Python 2.7](https://img.shields.io/badge/python-2.7-green.svg)![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg)[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/davidsa03/numerize/blob/master/LICENSE)# NumerizeNumerize is a Python (2 and 3) library for converting large numbers into readable strings.Similar to what Twitter and Instagram use for large follower counts.| Number | Numerized ||------|:---------:|| 1  | **1**  || 1000  | **1K**  || 1500  | **1.5K**  || 1000000  | **1M**  || 1500000  | **1.5M**  || 1000000000  | **1B**  || 1500000000  | **1.5B**  || 1000000000000  | **1T**  || 1500000000000  | **1.5T**  || 21324314       | **21.32M**|| -21324314       | **-21.32M**|## Installation```$ pip install numerize```## Usagenumerize(number_to_numerize, decimal_places_to_round[optional])```>>> from numerize import numerize>>> numerize(1234567.12)'1.23M'>>> numerize(12134.123, 3)'12.134K'```This will convert large numbers like 1234567.12 into 1.23M## Requirements- Python >= 2.7 or >= 3.4## Contributing[How to contribute](https://github.com/davidsa03/numerize/blob/master/CONTRIBUTING.md)## Testing```$ python numerize/test.py```## LicenseMIT licensed.
Package: numerize-denumerize
numerize_denumerizeA python package to numerize and denumerize numbers.Installationpip3 install numerize_denumerizeUsagefrom numerize_denumerize import numerize, denumerize
print(numerize.numerize(10000000, decimal_points=2))  # '10.00M'
print(denumerize.denumerize('10.00M'))  # 10000000Methodsnumerize(number, decimal_points=2)This method takes a number and returns a string in the format of x.xxM or x.xxK or x.xxB based on the number.denumerize(number_string)This method takes a string in the format of x.xxM or x.xxK or x.xxB and returns the actual number.NotePlease make sure you provide correct format while calling denumerize method.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: numerizer
numerizerA Python module to convert natural language numerics into ints and floats.
This is a port of the Ruby gemnumerizerInstallationThe numerizer library can be installed from PyPI as follows:$pipinstallnumerizeror from source as follows:$gitclonehttps://github.com/jaidevd/numerizer.git$cdnumerizer$pipinstall-e.Usage>>>fromnumerizerimportnumerize>>>numerize('forty two')'42'>>>numerize('forty-two')'42'>>>numerize('four hundred and sixty two')'462'>>>numerize('one fifty')'150'>>>numerize('twelve hundred')'1200'>>>numerize('twenty one thousand four hundred and seventy three')'21473'>>>numerize('one million two hundred and fifty thousand and seven')'1250007'>>>numerize('one billion and one')'1000000001'>>>numerize('nine and three quarters')'9.75'>>>numerize('platform nine and three quarters')'platform 9.75'Using the SpaCy extensionSince version 0.2, numerizer is available as aSpaCy extension.Any named entities of a quantitative nature within a SpaCy document can be numerized as follows:>>>fromspacyimportload>>>nlp=load('en_core_web_sm')# or load any other spaCy model>>>doc=nlp('The projected revenue for the next quarter is over two million dollars.')>>>doc._.numerize(){thenextquarter:'the next 1/4',overtwomilliondollars:'over 2000000 dollars'}Users can specify which entity types are to be numerized, by using thelabelsargument in the extension function, as follows:>>>doc._.numerize(labels=['MONEY'])# only numerize entities of type 'MONEY'{overtwomilliondollars:'over 2000000 dollars'}The extension is available for tokens and spans as well.>>>two_million=doc[-4:-2]# span corresponding to "two million">>>two_million._.numerize()'2000000'>>>quarter=doc[6]# token corresponding to "quarter">>>quarter._.numerized'1/4'ExtrasFor R users, a wrapper library has been developed by@amrrs. Try it outhere.
Package: numero-a-letras
Número a Letras LiteralDescripciónPrograma que convierte un número a su equivalente en letras.Instalaciónpipinstallnumero_a_letrasUsofromnumero_a_letrasimportnumero_a_monedanumero_a_letras(123456789.12)# Salida: 'ciento veintitres millones cuatrocientos cincuenta y seis mil setecientos ochenta y nueve con doce'También puede generarse el número en letras con su equivalente en moneda.fromnumero_a_letrasimportnumero_a_monedanumero_a_moneda(123456789.12)# Salida: 'ciento veintitres millones cuatrocientos cincuenta y seis mil setecientos ochenta y nueve pesos con doce centavos'
Package: numero-nome
numero_nomeDescriptionO pacote numero_nome Ã© usado:Somar os valores das letras do nome informadoVerificar significado do resultado da soma em relaÃ§Ã£o a numerologiaInstallationUse the package managerpipto install numerologia_nomepipinstallnumero_nomeUsagefromnumero_nomeimportprocessamentoprocessamento.significa_num(processamento.vetor_numero())AuthorAlan Silva VieiraLicenseMIT
Package: numeronymizer
NumeronymizerA simple library to generate numeronyms.ExampleinputThe utilization of this library can significantly enhance the accessibility of written content by abbreviating lengthy words. By reducing the number of characters in words such as internationalization, the overall readability and understandability of the text improves. Furthermore, it can also aid in reducing the amount of space required to display the text, which can be beneficial in digital contexts such as on websites or mobile devices.outputThe u9n of this library can s11y enhance the a11y of written content by a10g lengthy words. By reducing the number of c8s in words such as i18n, the overall r9y and u15y of the text improves. F9e, it can also aid in reducing the amount of space required to display the text, which can be b8l in digital contexts such as on websites or mobile devices.InstallationpipinstallnumeronymizerUsagefromnumeronymizerimportnumeronymizetext="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua."numeronymized_text=numeronymize(text)print(numeronymized_text)# "Lorem ipsum dolor sit amet, c9r a8g elit, sed do eiusmod tempor i8t ut labore et dolore magna aliqua."numeronymized_text=numeronymize(text,n=5)print(numeronymized_text)# "L3m i3m d3r sit amet, c9r a8g elit, sed do e5d t4r i8t ut l4e et d3re m3a a4a."For example, you can use the following code to read a text file namedmy_text.txt, and replace its text. Additionally, you can also save the shortened text to another text file of your choice.fromnumeronymizerimportnumeronymizewithopen("my_text.txt","r",encoding='utf-8')asfile:text=file.read()numeronymized_text=numeronymize(text,n=10)print(numeronymized_text)withopen("numeronymized.txt","w",encoding='utf-8')asfile:file.write(numeronymized_text)Functionnumeronymize(text:str,n:int=10)->str:"""Numeronymize the words in text.:param text: input text.:param n: the number of characters (default value is 10).:return: replaced text."""The function takes two arguments, the text you want to shorten and the number of characters, and returns the replaced text. The default number of characters is 10.LicenseThis project is licensed under the 0BSD License.
Package: numerospremiers
Este es un ejemplo simple de un paquete Python. Este paquete contiene una funciÃ³n para obtener los numeros primos que hay desde el 1 hasta el nÃºmero introducido por teclado.
This program is free software: You can redistribute it and/or modify it under the terms of license by the Free Software Foundation, either version 3 of the License(at your option) any other version.
Package: numerosprimoscelx2
NUMEROS PRIMOSEste es un ejemplo simple de un paquete Python. Este paquete contiene un conjunto de funciones para realizar operaciones matemÃ¡ticas sobre nÃºmeros enteros.
This program is free software: You can redistribute it and/or modify it under the terms of license by the Free Software Foundation, either version 3 of the License (at your option) or any other version.
Package: numerosprimosDavid
Actividad relacionada con la lecciÃ³n 4:
Haciendo uso de la librerÃ­a rpy2, implemente una funciÃ³n en R para calcular los
nÃºmeros primos entre 1 y n, donde n es un parÃ¡metro de la funciÃ³n.
A continuaciÃ³n, haciendo uso de las herramientas de distribuciÃ³n de cÃ³digo
estudiadas en esta lecciÃ³n, empaquete el cÃ³digo realizado de modo que pueda
ser utilizado por otro usuario (por medio del gestor de paquetes pip)
This program is free software:
you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation,
either version 3 of the License, or (at your option) any later version
Package: numerosPrimos-javivi
Esto es un ejemplo de paquete Python. Este paquete contiene contiene la funcion para Python donde se puede calcular los nÃºmeros primos entre 1 y n, donde nes un parÃ¡metro de la funciÃ³n.
This program is free software: You can redistribute it and/or modify it under the terms of license by the Free Software Foundation, either version 3 of the License(at your option) any other version.
Package: numerosprimosjm
Este es un ejemplo simple del paquete Python. Este paquete contiene una funciÃ³n que es capaz de mostarte, dado un nÃºmero n, sus nÃºmeros primos.
This program is free software: You can redistribute it and/or modify it undre the terms of license y the Free Software Foundation, either version3 of the License (at your option) any other version.
Package: numerous-engine
Numerous — an object-oriented modelling and simulation engineReasons for developing a python-based object-oriented simulation engineThe arguments for a python simulation engine are numerous:In order to take advantage of cloud based computing power a run-anywhere solution is preferredOpen-source to eliminate the need of complex licenses for deploying thousands of simulationsDirect connection with machine-learning and artificial intelligence libraries through the most popular programming language for data scienceLeveraging packages from the vast community seamlesslyPhilosophy and Motivation for Object-Oriented ModellingAs systems becomes complex the number of equations and variables grow fast and the overview is lost for the model developer. The idea behind this engine is to allow the model developer focus on one familiar object at a time and setup simulations for validation – and then combine these objects together to form complex interacting systems in a simple way where all the general tedious work is handled by the engine.Quick startTo install using pip usepip install numerous-engineYou can get started quickly here with a simple example:fromnumerous.engineimportmodel,simulationfromnumerous.examples.dampened_oscillator.dampened_oscillatorimportOscillatorSystem#Define simulations=simulation.Simulation(model.Model(OscillatorSystem('system')),t_start=0,t_stop=10,num=100,num_inner=100,max_step=0.1)#Solves.solve()simulation_result=s.model.historian_dfOr follow one of our comprehensive tutorials:Bouncing_Balla model of a ball dropping from a certain height and bouncing off the ground, until it finally comes to restTwo_Tanks_Systemtwo tanks are placed on top of each other and connected by a valve.Documentation can be found onreadthedocs.Or you can get familiar with the concepts we have used to abstract away building complex interacting systems right inside python.
Package: numerous-html-report-generator
numerous Report GeneratorA python package focused on enabling you to create beautiful and powerful html reports with great ease. You can use the reports to document your work and showcase your data, even generating these automitically with your analysis code already implemented in python.This package exposes a simple library to create a report, adding your sections, figures and tables or even just directly html divs with your own custom html code.When you have added your content to the report you can save it to a stand-alone html file you can share with anyone.We have provided you with a first template that you can modify to your needs. Soon we will publish a guide on how to change basic things in it - for instance the logo file.InstallationThe package numerous report generator can be installed with:pip install numerous-html-report-generatorQuick startHere is a simple example to get you started once you have installed the package:frompathlibimportPathimportosfromnumerous.html_report_generatorimportReport,Section,GoFigure,DataFrameTableimportpandasaspd# Define output folder and html filefolder=Path('./output')filename='my_first_report'file=folder.joinpath(filename+'.html')#Remove previous report file if existsifos.path.exists(file):os.remove(file)# Create reportreport=Report(target_folder=folder,filename=filename)# Add info for the header and title pagereport.add_header_info(header='My Organization',title='My First Report',sub_title='but not the last',sub_sub_title='at all',footer_title='My first footer',footer_content='This is the end!')#Add a sectionsection=Section(section_title="Section 1")#create a figurefigure=GoFigure({"data":[{"type":"bar","x":[1,2,3],"y":[1,3,2]}],"layout":{"title":{"text":"A Figure Specified By Python Dictionary"}}},caption="Test figure",notes=["Please notice x","Please notice y"])#create a tabletable=DataFrameTable(pd.DataFrame({'a':[1,2,3],'b':[4,5,6]}),caption="This is a test table")#Add the figure and table to the sectionsection.add_content({'figure':figure,'table':table})#Add the section to the reportreport.add_blocks({'section':section})# Save the report - creates the html output filereport.save()Contributing:Please see CONTRIBUTING.md for details on how to contribute.
Package: numerous.sdk
numerous SDKWelcome to the repository fornumerous.sdk!Thenumerous.sdkwhich includes all the things you need to build tools and
applications that can run on thenumerous platform.RequirementsThis project requires python version 3.9 or later.InstallationTo installnumerous.sdkyou can use the following command:pip install numerous.sdkDocumentationDocumentation for the numerous SDK can be found atreadthedocs.io. This contains the latest
documentation. We are continuously adding more, as the project matures.ContributingWe welcome contributions, but please readCONTRIBUTING.mdbefore you start. It lays down the processes and expectations of contributions
to the numerous SDK.Setup development environmentIn order to start developing on the numerous SDK, we recommend you create a virtual
environment, and install the package (includinglintandtestdependencies) in an
editable state:pip install -e .[lint,test]This will also installpre-commitwhich can then be used to setup the pre-commit
checks, like below:pre-commit installGenerate documentationIn order to generate the documentation, install the requirements, and run the build.cd docs
pip install -r requirements.txt
make htmlThe built documentation is then stored indocs/build/html.ChangesSee theCHANGELOG.mdto get an overview latest features, fixes
and other changes tonumerous.sdk, and also to find the general history of
changes.Licensenumerous.sdkis licensed under the MIT License, and it can be read inLICENSE.md.
Package: numerous-solver
A jitable object-oriented ODE solver for numerical simulationsOverview:Numerous solver is a fast, jitable, highly customizable, and object-oriented ODE (ordinary differential equations)
solver.How to get startedInstall the package viapip install numerous-solver, or clone thegit repositoryand install using thesetup.pyfile.DocumentationPlease see thegitlab pagessite for documentation.Design philosophyThenumerous solveris the choice of ODE (ordinary differential equations) solver fornumerous engine(https://github.com/fossilfree/numerous). It is developed following a set of principles:Object-oriented: A python-based solver should take advantage of the class structure of python. Numerous solver uses
"models" and "interfaces" to manage the state-flow between the numerical model and the numerical solver.Customizable: Using "solver events", the solver can be customized to break the solver loop in order to read external
data, save outputs to database, print status messages etc. This allows a great degree of customization.Support time- and state-events: Built-in support for time- and state-events to support advanced models, controllers
and physics discontinuities.Jitable:Numerous solvermay be compiled with numba (https://numba.pydata.org/) to, in some cases, increase speed.
The build-in toolchain can assist the user to write (njit) compilable models.Extendable: Numerous solver currently supports 3 methods (RK45, BDF, Euler) and can be extended with new solve
methods.Open-source: The code base is open for contributions.
Package: numerov
Compute vibrational levels, wavefunctions, and expectation values using the
Numerov-Cooley algorithm.Copyright and licenseCopyright 2017 Radovan Bast.
Use of this source code is governed by a the Mozilla Public License v2.0 that
can be found in theLICENSE file.CitationIf you use this tool in a program or publication, please acknowledge its
author(s) by adding the following reference (please replace vX.Y.Z
by the appropriate version):Radovan Bast, Numerov vX.Y.Z, 2017, GitHub repository,https://github.com/bast/numerov.As soon as the API stabilizes a bit, I will tag a release and attach a DOI to it.InstallationpipinstallnumerovExamplePlease have a lookhere.BackgroundThis script will calculate the vibrational levels (and wavefunctions)
corresponding to a normal mode numerically using the Numerov-Cooley algorithm.The script will increase the energy and count the nodes of the wave function.
If the number of nodes changes and stepsize is belowenergy_precision_hartree, it will
accept the solution, integrate the property along q and move on to the next
solution untilnum_solutionsis reached. It will also calculate the transition
frequencies 0 -> n, this is useful to check against the harmonic frequencies.Adviceenergy_precision_hartreeis often more important than number of grid points.Be careful with the displacement range.If the script enters an endless loop probably the reduced mass or the displacement range is wrong.Practice first with the harmonic oscillator.It is a good idea to play with parameters to check convergence and numerical
stability.Potential and property are approximated by polynomials that contain
coefficients FROM ZEROTH to nth order (that’s what polyfit gives) and you
might not want that (for instance you might insist that the gradient of the
potential is zero at equilibrium) in this case you can provide your own
expansion coefficients or program an alternative interpolation scheme.
Package: numerox
Numerox is a Numerai tournament toolbox written in Python.All you have to do is create a model. Take a look atmodelfor examples.Once you have a model numerox will do the rest. First download the Numerai
dataset and then load it:>>> import numerox as nx
>>> data = nx.download('numerai_dataset.zip')Let’s use the logistic regression model in numerox to run 5-fold cross
validation on the training data:>>> model = nx.logistic()
>>> prediction = nx.backtest(model, data, tournament='bernie', verbosity=1)
logistic(inverse_l2=0.0001)
       logloss     auc     acc    ystd   stats
mean  0.692885  0.5165  0.5116  0.0056   tourn  bernie
std   0.000536  0.0281  0.0215  0.0003  region   train
min   0.691360  0.4478  0.4540  0.0050    eras     120
max   0.694202  0.5944  0.5636  0.0061  consis   0.625OK, results are good enough for a demo so let’s make a submission file for the
tournament. We will fit the model on the train data and make our predictions
for the tournament data:>>> prediction = nx.production(model, data, 'bernie', verbosity=1)
logistic(inverse_l2=0.0001)
       logloss     auc     acc    ystd   stats
mean  0.692808  0.5194  0.5142  0.0063   tourn      bernie
std   0.000375  0.0168  0.0137  0.0001  region  validation
min   0.691961  0.4903  0.4925  0.0062    eras          12
max   0.693460  0.5553  0.5342  0.0064  consis        0.75Let’s upload our predictions to enter the tournament:>>> prediction.to_csv('logistic.csv')
>>> upload_id, status = nx.upload('logistic.csv', 'bernie',
                                  public_id, secret_key, model_id)
metric                  value   minutes
concordance              True   0.0898
consistency              0.75   0.0898
originality             False   0.1783
validation_logloss     0.6928   0.1783
stakeable                True   0.1783ExamplesHave a look at theexamples.InstallInstall with pip:$ pip install numeroxAfter you have installed numerox, run the unit tests (please report any
failures):>>> import numerox as nx
>>> nx.test()Requirements: numpy, scipy, pandas, sklearn, pytables, numerapi,
setuptools, requests, nose.ResourcesLet’schatSeeexamplesCheckwhat’s newReportbugsSponsorThank youNumeraifor funding the development of Numerox.LicenseNumerox is distributed under the the GPL v3+. See LICENSE file for details.
Where indicated by code comments parts of NumPy are included in numerox. The
NumPy license appears in the licenses directory.
Package: numertweak
numertweakSome utility function for thenumer.aicompetition.This for my personal usage so far.
If you have any questions then send me an Email.Table of ContentsInstallationCommandsSupportContributingInstallationThenumertweakgit repois available asPyPi packagepip install numertweakIn notebooks use this%%capture
!pip install "numertweak>=0.2.0"
from numertweak import *CommandsCheck syntax:flake8 --ignore=F401,W503,E731Remove.pycfiles:find . -type f -name "*.pyc" | xargs rmRemove__pycache__folders:find . -type d -name "__pycache__" | xargs rm -rfUpload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*SupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: numetrics
NumetricsNumetrics is a small package that has some functions regarding checking the performance of a model's predictions.VersionGithub Releasev0.2.1PyPi Versionv0.2.1Why did I create Numetrics?The first reason is: I want to contribute to the Numerai community. I have learned a lot from Arbitrage, JRB, Wigglemuse, Michael Oliver, the team, and everybody in the community.The second reason is: I was tired of copying and pasting my code throughout my notebooks, so I decided to create a package to make it easier for me to just install it through pipHow to install Numetrics?You can install it with pippipinstallnumetricsimportnumetricsHow to use Numetrics?You have one function calleddiagnosethis is the main function that does all the magic.
It takes a dataframe of your prediction. Dataframe format should be same as the one you submit to Numerai. Prediction dataframe must have the followingThe idpredicton columnyou can just pass only the predicton dataframenumetrics.diagnose(example_prediction)or you can spicify the target name and the predicton namenumetrics.diagnose(preds_df,PREDICTION_NAME='prediction')See the example file to learn how to use this package.This is the first version/draft/attempt of the package. The code does not look pretty and it still missing lots of functionalities. However, I promise I will do my best to make it better ASAP.Feel free to contribute to this package.Change Log0.0.1 (Nov/09/2020)First Release0.0.2 (Nov/11/2020)Edited setup.py0.0.3 (Nov/11/2020)Added Readme0.0.4 (Nov/11/2020)Edited Readme0.0.5 (Nov/13/2020)Added val data fileAdded beta_dignose function0.1.0 (Nov/14/2020)Added data file and fixed the file path errorAdded beta_dignose function0.2.0 (Nov/18/2020)Upgraded beta_dignose to diagnose and then deleted the old diagnoseChanged data fileChanged example prediction0.2.1 (Nov/28/2020)Added the feature of calcualting diffrences between Vals
Package: numex
This Python software implements a quick and easy explore for numerical data.InstallationThe recommended way of installing the software is throughPyPI:$pipinstallnumexAlternatively, you can clone the source repository fromGitHub:$mkdirnumex$cdnumex$gitclonegit@github.com:norok2/numex.git$pythonsetup.pyinstall(some steps may require additional permissions depending on your configuration)The software does not have additional dependencies beyond Python and its
standard library.It was tested with Python 2.7 and 3.6.
Other version were not tested.