Saravanan666/pypi-org huggingface dataset:
Package: bendu
BenduTable of ContentsIntroductionGetting StartedPrerequisitesUsageTestingHelpTechnologiesIntroductionThis python tool automates operations with docker containers by utilizing the Docker Remote API. This python tool implements the following:Building a Docker Image from a given Dockerfile and an application (a ‘Hello world’ web application)Starting a few instances of the Docker Image in different containersValidating that the container instances are runningValidating the status of the service running inside the container (health check)Monitoring the resource usage of each container (CPU, I/O, etc)Consolidating the log output of all the container instances into one centralized log file.Getting StartedPrerequisitesPlease make sure you've alreadyPython3. The tool is utilizing theDocker Remote API, thePython SDK for Dockerand the python packageClick. TheRemote APIis set up in tcp://0.0.0.0:2376. For the purpose of using the tool, you should set up the Remote API likewise. The following instructions apply to Ubuntu 16.04 and Debian 9.9 (stretch).Install the Python SDK and Click:apt-get install python3-pip
pip3 install docker
pip3 install clickEdit the file:sudo nano /lib/systemd/system/docker.serviceUpdate with:ExecStart=/usr/bin/dockerd -H fd:// -H tcp://0.0.0.0:2376Then, restart the docker service running below commands:sudo systemctl daemon-reload
sudo systemctl restart docker
sudo systemctl status dockerFor older versions than Ubuntu 16.04 you can use the instructions in the following source,Enabling and accessing Docker Engine API on a remote docker host on UbuntuUsageThe tool is equipped with five commands:build,deploy,validate,statsandloggingas presented below:python3 bendu.py [build, deploy, validate, stats, logging] --helpTestingTo test the python tool follow the instructions below.First install virtual environment:apt-get install virtualenvThen, in the same depth as the project make a folder namedvenv:mkdir venvExecute the following command:virtualenv -p python3 venv/Get up and running a virtual environment by running executing:. venv/bin/activate
pip3 install --editable .Finally, execute thebuildcommand to build the image according the instructions that are defined in a Dockerfile:bendu --help
bendu build -d ./data -t "alpine_hello_world:1"Exit virtual environment by invoking:deactivateHelpEach one of these commands has each one functionality which can be over-viewed by invoking each command followed by--hep.TechnologiesDockerClickPython SDK for Docker
Package: beneath
Beneath Python Client LibraryThis folder contains the source code for theBeneathPython library. Here are some useful links:Beneath DocsPython Client API ReferenceReading data tutorialWriting data tutorialInstallationTo install the Python library (requires Python 3.7 or higher), run:pip3 install --upgrade beneathProviding feedbackBeneath is just entering public beta, so there's bound to be some rough edges. Bugs, feature requests, suggestions – we'd love to hear about them.Click hereto file an issue.
Package: benepar-nx
benepar_nxNetworkX graph fromBerkeley neural parserconstituency parseGetting started , or see demo.ipynbFIXME: get beneparpipinstallbenepar&&\python-c"import benepar; benepar.download('benepar_en3')"and maybe spaCy modelpython-mspacydownloaden_core_web_mdAdd benepar to the pipelineimportspacymd_benepar=spacy.load('en_core_web_md')importbenepar# for add_pipe('benepar'...md_benepar.add_pipe('benepar',config={'model':'benepar_en3'})parse sentences:doc=md_benepar('In the fourth quarter, we closed on the transformative acquisitions of Larry H. Miller and Total Care Auto, powered by Landcar, Kahlo Chrysler Jeep Dodge, Arapahoe Hyundai-Genesis and the Stevinson Automotive Group, representing approximately $6.6 billion in annualized revenue.')and create graph for the sentence:frombenepar_nx.constituency_parseimportcreate_constituency_parse_graphs=list(doc.sents)[0]G=create_constituency_parse_graph(s)Draw constituency parse graphGet graphviz , GNU sed and pydotbrewinstallgraphvizgnu-sed&&\pipinstallpydotthenfromnetworkx.drawing.nx_pydotimportwrite_dotwrite_dot(G,'G.dot')that will write graph like thisG.dotandgsed-e's/label=,/label=","/g'G.dot|dot-Tpng>output.pngHackingpipinstall--editable.
Package: benerator-cumberpy
Benerator Cumberpy is a name generator written in Python that generates word pairs similar to “Benedict Cumberbatch”. There already exist generators like this. However, they create new words, such as “Boobiedook Cumberfinkle”.Benerator Cumberpy will create pairs of existing English words that fullfil certain criteria. Examples could include “Benefits Coordination” or “Benevolent Cucumber”. There are over 480 million possible combinations.The script works both in Python 2 and 3.Install and run Benerator CumberpyMake sure you have Python installed, and (optionally) create a virtualenv.To install Benerator Cumberpy, runpip installbenerator-cumberpyTo run Benedict Cumberpy in terminal, run:Available commandsCommandDescriptionbeneratePrint only a name-surname pair.hello-benPrint a sentence like “Hello there, my name is Bronzelike Certificate.”.For added hilarity, pipe the output ofhello-benthroughcowsay(Win:cowsay for Windows, Linux:apt install cowsayor whatever else your distro uses, OSX: if you have HomeBrew installed –brew install cowsay).Example:$ hello-ben | cowsay -f stegosaurus
 ____________________________________
/ Hello there, my name is Beetleweed \
\ Correctioner.                      /
 ------------------------------------
\                             .       .
 \                           / `.   .' "
  \                  .---.  <    > <    >  .---.
   \                 |    \  \ - ~ ~ - /  /    |
         _____          ..-~             ~-..-~
        |     |   \~~~\.'                    `./~~~/
       ---------   \__/                        \__/
      .'  O    \     /               /       \  "
     (_____,    `._.'               |         }  \/~~~/
      `----.          /       }     |        /    \__/
            `-.      |       /      |       /      `. ,~~|
                ~-.__|      /_ - ~ ^|      /- _      `..-'
                     |     /        |     /     ~-.     `-. _  _  _
                     |_____|        |_____|         ~ - . _ _ _ _ _>You can also use Benerator Cumberpy in your Python script.For example:# your_script.pyfrombenerator_cumberpyimportbenerate_namefrombenerator_cumberpyimporthellobenerate_name()hello()$ python your_script.py

Bestselling Corridors
Hello there, my name is Belial Cobalt.Where do the words come from?I found a list of English words to use –this one.
Then I made two lists –first_names.txtandlast_names.txtbased on the following criteria:First name (end result: 16692 words):starts with “B”is at least 6 letters longis not BenedictLast name (end result: 28786 words):starts with “C” or “K”is at least 6 letters longdoes not start with “ch”is not Cumberbatch, though it wasn’t in the dataset anyway
Package: benford-law
A Python package to apply the Benford´s Law analysisProject MotivationAs part of the Udacity Data Science nanodegree this is a project to create and deploy a Python package, based on this I decided to use the Benford´s Law to create a package using OOP than can apply the analyzis to a dataset, an imgae or a numpy array of numbers.Installation:pip intall benford_lawThis package uses these librariespandasnumpymatplotlibThe python version used:3.8Files in the repositoryREADME.md    : This filesetup.py     : Setup file for the package.gitignore   : File to ignore files and not load to the GitHub repositoryLICENSE      : The MIT Licensebenford_lawbendord_law.py     :   The Benford Classinit.py    :   Inititalization file for the packageteststests.py       :   Script for unit tests of the Benford ClassBenford_plot_test.ipynb : Jupyter Notebook to test the graphs plotting ot the Benford Classflower.jpg     :   JPG image to test the Benford Classpopulation.csv :   CSV dataset with the Wordl Population to test the Benford ClassHow To use the packageCreate an instace with the Benford Classbenford = Benford()Load a dataset or an image ,bendford.load_dataset(dataset_name)benford.load_image(image_name)Do the benford analysis using the benford_analysis methodbenford.benford_analysis()Graph or plot the results using plot() methodbenford.plot()Save the results using expor_to_csv() method:benford.export_to_csv(filename)Example using the pakage to analyze an image:#Import librariesimportpandasaspdimportnumpyasnpfrombenford_lawimportBenfordimportmatplotlib.pyplotaspltfromPILimportImageimage_file='flower.jpg'#Read imageimage=Benford()#Create Benford instanceimage.load_image(image_file)#Load imageimage.benford_analysis()#Do Analysisimage.plot()#Plot Benford´s analysisTerms of use:This is a tool to do  an initial analyzis using Benford´s Law  and plot how the digit´s probability are distributed on different datasets, it is not a formal analysis or study using the Benford´s LawLicense:The code follows this license:https://opensource.org/licenses/MIT
Package: benfordslaw
benfordslawbenfordslawis Python package to test if an empirical (observed) distribution differs significantly from a theoretical (expected, Benfords) distribution. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small. This method can be used if you want to test whether your set of numbers may be artificial (or manipulated). If a certain set of values follows Benford's Law then model's for the corresponding predicted values should also follow Benford's Law. Normal data (Unmanipulated) does trend with Benford's Law, whereas Manipulated or fraudulent data does not.Assumptions of the data:The numbers need to be random and not assigned, with no imposed minimums or maximums.The numbers should cover several orders of magnitudeDataset should preferably cover at least 1000 samples. Though Benford's law has been shown to hold true for datasets containing as few as 50 numbers.⭐️ Star this repo if you like it ⭐️Install benfordslaw from PyPIpipinstallbenfordslawImport benfordslaw packagefrombenfordslawimportbenfordslawDocumentation pagesOn thedocumentation pagesyou can find detailed information about the working of thebenfordslawwith many examples.ExamplesExample: Analyze first digit-distributionExample: Analyze second digit-distributionExample: Analyze last digit-distributionExample: Analyze second last digit-distributionReferenceshttps://en.wikipedia.org/wiki/Benford%27s_lawhttps://towardsdatascience.com/frawd-detection-using-benfords-law-python-code-9db8db474cf8CitationPlease cite in your publications if this is useful for your research (see citation).MaintainersErdogan Taskesen, github:erdogantContributeAll kinds of contributions are welcome!If you wish to buy me aCoffeefor this work, it is very appreciated :)LicenceSeeLICENSEfor details.
Package: benfords-law
Benford's LawA python package for testing if a dataset of numbers passes Benford's law; also known as the law of analogous numbers.Installationpip install -U benfords-lawUsages>> import numpy as np
>>
>> from benfords_law import BenfordsLaw
>> 
>> # initialize array with random numbers that will fail Benford's Law
>> data = np.random.randint(low=100, high=1000000, size=1000)
>> benfords = BenfordsLaw(data)
>> benfords.apply_benfords_law()
Chi-squared test failed with statistic: 998.0013682427352 and p-value: 4.032015415461028e-210
>> # Benford's Test Image Below:Dependenciesnumpy==1.17.1pandas==0.25.1scipy==1.6.0matplotlib==3.3.3Introduction and DescriptionNewcomb-Benford's Law(The Law of Analogous Numbers) states that in many naturally occurring sets of numbers, the first significant digit is likely to be small.
This means that in a set of numbers; eg. populations of countries in the world, the first digit of the number is most likely to be 1. And following that, the probability that the first digit is 2, is less that that of one, but greater than all the rest and so on and so forth.In fact, this expectation from Benford's Law follows a very specific distribution that is shown below:As such, using the example of country populations in the world, the distribution of first significant digits against the expected distribution from Benford's Law can be seen as follows:This phenomenon is pervasive in many extensive sets of numbers. Examples are:Earthquake MagnitudesDow Jones Industrial Average from 1990– 19933,141 county populations from the 1990 U.S. CensusDistance of stars from earth in light yearsMost common iphone passcodesThe explanations for this law are many and debated. However there are some key (non-exhaustive) characteristics of these sets of numbers. These sets generally:Occur 'naturally' without human manipulationOccur in many orders of magnitude.Have some exponentation going onWhy should we care? Glad you asked. If these numbers are naturally occurring, then if a set of naturally occuring set of numbers does not follow Benford's law , then there is cause to believe that there's something unscrupulous going on. For example, in cases where elections have been rigged, you'll find that numbers tallied do not follow Benford's Law.As such it's a fair practice to detect inconsistencies in sets of numbers using Benford's Law. Examples of real work applications of Benford's Law in manipulation/fraud/misleading-data detection include:National COVID-19 Tracking Errors/InconsitenciesElectoral FraudKey TerminologyFirst Leading Digit (fsd):The first non-zero digit of a number. eg:52390234 has fsd=50.0004562 has fsd=42943.6 has fsd=2#ReferencesWikipediahttps://en.wikipedia.org/wiki/Benford's_lawNetflixConnected: Season 1; Episode (Digits)https://www.netflix.com/watch/81084953YoutubeKhan Academy: Benford's Law Explainedhttps://youtu.be/SZUDoEdjTzg
Package: benfordslaw-analysis
Benford's law analysisBenford's law is a digit-law, which states that digits from numbers follow a specific frequency.
This specific frequency has been observed in many numerical datasets,
as discovered by Simon Newcomb and Frank Benford.
You can find onwikipediamore information about this mysterious law.Benford's law is a helpful tool to detect fraud, do science, or just investigate the quality of data.
You can also read myblogon Towards Data Science with a summary of Benford's law andthis paper,
where I used Benford's law to study digit patterns of the distances between stars in our Milky Way.InstallationWithpip install benfordslaw-analysisyou can install the package.UsageAfter installing, you can run in Pythonfrom benfordslaw_analysis.analysis import Analysis.
This imports the classAnalysis.
With this class you can verify if Benford's law is hidden in your own data.For example, make a plot with Benford's law versus random data with:from benfordslaw_analysis.analysis import Analysis
from random import uniform
random_data = [uniform(-10, 10) for i in range(0,1000)]
bl = Analysis(random_data)
bl.plot_first_digit('Random stuff')Note that we use theEuclidean distancebetween the digit frequency from Benford's law and your own data as a measure
and that we use Poisson error bars (based on the number of data points).Euclidean distanceThe normalized Euclidean distance is a quick way to test whether your data follows Benford law.
This value is situated between 0 and 1, the closer to 0 the better.
However, it is not a formal statistic because it is sample size independent.
In the literature there are several other measures (Chi-square, Kolmogorov-Smirnov, ..) that are used. However, I noticed in my own research that
size dependency is a limitation in bigger datasets and classifies all bigger datasets as non-Benford, even though they are Benford by eye.CitingIf you findbenfordslaw_analysisa useful tool for your own research, please cite in the following way:@misc{benford_py,
      author = {Jurjen, de Jong},
      title = {{benfordslaw_analysis: a Python Implementation of Benford's Law analysis}},
      year = {2021},
      howpublished = {\url{https://github.com/jurjen93/Benfords_law}},
}
Package: benfordviz
benfordvizInteractive plotting to benford_py.This is a lib to make interactive plotting of Benford's Law Tests easier.CitingIf you findbenfordvizuseful in your research, please consider adding the following citation:@misc{benfordviz,author={Marcel, Milcent},title={{Benfordviz: a Python Implementation of interactive plotting for Benford's Law Tests}},year={2021},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/milcent/benfordviz}},}current version = 0.2.0Seerelease notesfor details of features and bugs.Python versions >= 3.6Installationpip install benfordvizDependenciesbenford_py >= 0.4.2;bokeh >= 2.3.2Implemented so far:BokehMore in the furure- let me know which one you prefer implemented next in theIssues. Plotly? Altair?UsageHave your data ingested in abenford_pyBenford obect like so:importnumpyasnpimportbenfordasbf# Benford's sets are combinations of random variablesa=np.random.rand(3000)b=np.random.randint(0,55,3000)c=np.random.normal(3000)abd=a*b*cbo=bf.Benford(abc)########## Benford Object Instantiated ########### 

Initial sample size: 3000.

Test performed on 2941 registries.

Number of discarded entries for each test:
{'F1D': 0, 'F2D': 0, 'F3D': 0, 'SD': 0, 'L2D': 1}Now that you have a Benford object with the main tests already computed, you can give them tobenfordvizand let it do the rest. The main function for plotting the respective test withbokehis thebokeh_chart. It receives a digit test instance, which in turn is an attribute of the Benford object:F1D: First Digit Test;SD: Second Digit Test;F2D: First Two Digits Test;F3D: First Three Digits Test; andL2D: Last Two Digits Test.Thebokeh_chartfunction then retuns abokehfigure, which you can then pass to the output of your choice (browser, file, jupyter...)frombokeh.plottingimportoutput_notebook,showfrombenfordvizimportbokeh_digits_chartoutput_notebook()# plotting the First Two Digits Test ('F2D')benf_bokeh_f2d_fig=bokeh_digits_chart(bo.F2D)show(benf_bokeh_f2d_fig)Or you can directly instantiate aBokehDigitsChartobject, and use itsfigure, like so:frombenfordviz.bokeh_plottingimportBokehDigitsChartbdc_f2d=BokehDigitsChart(bo.F2D)show(bdc_f2d.figure)As of version 0.2.0, we also have the Manitssas Tests chart, which is a union of the Ordered Mantissas and the Mantissas Arc Plots, side-by-side. Since the mantissas assessment is made on the whole set (not segregated by digits), you can pass the Benford instance directly to it.frombenfordvizimportbokeh_mantissas_chartbenf_bokeh_mant_fig=bokeh_mantissas_chart(bo)show(benf_bokeh_mant_fig)
Package: bengali
bengaliA Python package for Bengali language processing related tasks.BackgroundBengalialso known asBanglais one of the widely used language in the world. This python package is an effort to combine some usefull python tools to work conveniently with Bengali language and computation.InstallationInstall using the following command -pipinstallbengaliUninstall using the following command -pipuninstallbengaliGetting StartedTo usebengaliinstall it usingpip. And make sure you have the required python version in your environment, i.e.python >= 3.5.How to ContributeInspiration
Package: bengaliwordcorrector
Bengali Word CorrectorInstallationpip install bengaliwordcorrector==1.1.1Quick StartAfter installation, using bengaliwordcorrector should be fairly straight forward:Word Correctionfrom bengaliwordcorrector import correction
l = ['মেইন্টনেন্স','পরমর্শ','অতক্রম্য','কমিশনরের']
for i in l:
    print('incorrect word:',i,"correct word:",correction(i))Related Word Suggestionsfrom bengaliwordcorrector import suggestions
l = ['মেইন্টনেন্স','পরমর্শ','অতক্রম্য','কমিশনরের']
for i in l:
    print("incorrect word:",i,"suggestions:",suggestions(i))
Package: bengrn
bengrnAwesome Benchmark of Gene Regulatory Networks created by @jkobjectThe package is supposed to work withGRnnDataIt can run Genie3 & pyscenic on your data as a comparisonIt has 4 different types of key ground truth data to compare your GRN to:sushmita roy's ChIP+Perturb ground truthcollectri's literature curated ground truthdorothea's literature curated ground truthtf2gene's chip curated ground truthYou can find the documentationhereInstall it from PyPIpipinstallbengrnUsagefrombengrnimportBenGRNfrombengrnimportsome_test_function# a GRN in grnndata formartgrndataBenGRN(grndata).do_tests()#orsome_test_function(grndata)see more in the notebooks in the docs folder or in thedocumentationDevelopmentRead theCONTRIBUTING.mdfile.
Package: ben-mackenzie-features
feature store utilsA light-weight package that allows you express ML features in simple yaml, build a training data set and then write them to a feature store.some general thoughts on building a training datasethttps://docs.google.com/presentation/d/1tVkrwCLVwFp8cZC7CmAHSNFhsJrcTdC20MlZfptkSBE/edit?usp=sharingoptions for useclone this repo.  create features.yaml.  follow demo notebook. do not check back in.create you own repo and install as package (currently in testpypi).  Seehttps://github.com/BenMacKenzie/churn_model_demoas an example.  Note that you must create a .env file in folder which contains the features.yaml fileNotesCurrent version is experimental.  Not clear that Jinja is the right way to write parameterized SQL. Might be better to do in Python.Current version is not optimized. Each feature is calculated individually, whereas if table, filters and time windows are identical, multiple aggregation features can be calculated simultaneously.I believe there are around a dozen standard feature types.  The most common have been implemented.  Note that views can fill in a lot of gaps if encountered.  missing:type 1 lookup.1st order aggregations over time series (e.g., just treat it like a fact table)2nd order aggregations over time series e.g., max monthly job dbu over 6 month window.time in state,  e.g., how long was a ticket open.  based on a type 2 table.time to event in fact table, e.g., time since last call to customer supportscalar functions of two or more features, e.g, time in days between two datenum state changes over interval (rare)functions of features (e.g., ratio of growth in job dbu to interactive dbu).  Arguably this is not needed for boosted trees. Might be useful for neural nets...but why use a nueral net on heterogeneous data? (actually this kind of thing can be good for model explainability)Need to illustrate adding features from a related dimension table (using a foreign key...machinery is in place to do so.)Current version illustrates creating a pipeline which uses the api.  But it would be nice just to generate the code and write it to a notebook so that the package is invisible in production (like bamboolib)The demo repo (https://github.com/BenMacKenzie/churn_model_demo) illustrates 'hyper-features' which are features with variable parameters.Connecting 'hyper-features' to feature store needs to be worked out.  Currently the option is to add all of them or specify individual version by their (generated) nameFix feature store feature gen observation dates.  Align with grain of feature, e.g., if grain is monthly make sure feature store contains an observation on first of month.Buildingnote that you need a token to do this...probably I am the only one who can push to testpypi at the moment.python3 -m build  
python3 -m twine upload --repository testpypi dist/*Running unit tests on databricksinstall the databricks extension for vscodeuse this repo as a template.  Note the following:remote_test_harness/pytest_databricks.py.vscode/launch.jsonwrite tests as usual (see tests/time_series/time_series_test.py as an example)