Saravanan666/pypi-org huggingface dataset:
Package: anonymizedf
Anonymize df: a convenient way to anonymize your data for analyticsWhat is it?Anonymize dfis a package that helps you quickly and easily generate realistic
fake data from a Pandas DataFrame.What are the expected use cases / why was this made?You're hiring consultants to work on your data but need to anonymize it firstYou're a consultant and created something great that you want to make into a templateInstallationYou can install anonymizedf using pip:pipinstallanonymizedfThis will also try downloading the tableau hyper api and pandas packages
if you don't have them already.If you don't want to use pip you can also download this repository and execute:pythonsetup.pyinstallExample usageimportpandasaspdfromanonymizedf.anonymizedfimportanonymize# Import the datadf=pd.read_csv("https://query.data.world/s/shcktxndtu3ojonm46tb5udlz7sp3e")# Prepare the data to be anonymizedan=anonymize(df)# Select what data you want to anonymize and your preferred style# Example 1 - just updates dfan.fake_names("Customer Name")an.fake_ids("Customer ID")an.fake_whole_numbers("Loyalty Reward Points")an.fake_categories("Segment")an.fake_dates("Date")an.fake_decimal_numbers("Fraction")# Example 2 - method chainingfake_df=(an.fake_names("Customer Name",chaining=True).fake_ids("Customer ID",chaining=True).fake_whole_numbers("Loyalty Reward Points",chaining=True).fake_categories("Segment",chaining=True).fake_dates("Date",chaining=True).fake_decimal_numbers("Fraction",chaining=True).show_data_frame())# Example 3 - multiple assignmentsfake_df=an.fake_names("Customer Name")fake_df=an.fake_ids("Customer ID")fake_df=an.fake_whole_numbers("Loyalty Reward Points")fake_df=an.fake_categories("Segment")fake_df=an.fake_dates("Date")fake_df=an.fake_decimal_numbers("Fraction")fake_df.to_csv("fake_customers.csv",index=False)# One thing to note is that you can't directly pass in a list of columns.# If you want to apply the same function to multiple columns there are many ways to do that.# Example 4 - for multiple columnsforcolumnincolumn_list:an.fake_categories(column)Example outputCustomer IDCustomer NameLoyalty Reward PointsSegmentDateFractionFake_Customer NameFake_Customer IDFake_Loyalty Reward PointsFake_SegmentFake_DateFake_Fraction0AA-10315Alex Avila76Consumer01/01/20007.6Christian Metcalfe-ReidYEJP71011502726136558Segment 11978-11-0929.961AA-10375Allen Armold369Consumer02/01/200036.9Helen TaylorXWOB83170110594048286Segment 11989-12-2972.502AA-10480Andrew Allen162Consumer03/01/200016.2Joanne PriceVVCJ28547588747677742Segment 11982-09-2379.773AA-10645Anna Andreadi803Consumer04/01/200080.3Rhys JonesOXCI12190813836802206Segment 12000-10-147.154AB-10015Aaron Bergman935Consumer05/01/200093.5Nigel Baldwin-CookJOXS05799252235987914Segment 12018-01-3040.66DependenciesPandasFaker
Package: anonymizeip
anonymize_ipThis is a simple Python library for anonymizing IP addresses. Both IPv4 and IPv6 addresses are supported.Examples:IPv4:95.239.169.11→95.239.169.0IPv6:5219:3a94:fdc5:19e1:70a3:b2c4:40ef:ae03→5219:3a94:fdc5:19e1::Usagepip install anonymizeipfromanonymizeipimportanonymize_ipaddress="fe80::0202:b3ff:fe1e:8329"anonymized=anonymize_ip(address)print(anonymized)# Prints "fe80::"SettingsThe number of address blocks that are set to 0 can be customized.Besides the IP address, the functionanonymize_iptakes two optional parameters:anonymize_ip(address,ipv4_mask="...",ipv6_mask="...")ipv4_mask: Defaults to255.255.255.0, i.e. the last octet will be anonymized (set to 0)ipv6_mask: Defaults toffff:ffff:ffff:ffff::(same asffff:ffff:ffff:ffff:0:0:0:0), i.e. the last four blocks will be anonymized (set to 0)Developmentgit clonepipenv install --devMake your code modificationspipenv run testpipenv run lintContributions are always welcome. Please first discuss changes via issue before submitting a pull request.CreditsThe implementation of this library was strongly inspired byphp-ip-anonymizerby Geert Wirken.
Package: anonymizer
# anonymisationLe répertoire anonymisation fournit une méthode, des outils et des références sur l’anonymisation des données à caractère personnel.## Objectifs et usagesCe projet  a pour objectif :
+ D’introduire l’utilisateur et le producteur de données aux enjeux de l’anonymisation, d’un point de vue juridique, scientifique et technique.
+ De construire un espace collaboratif autour de ce thème.
+ De proposer une méthode robuste et testée de k-anonymisation de données.## ContenuPlus précisément, cet espace est constitué d’un wiki permettent de détailler et de comprendre l’anonymisation.
Le code formalise le traitement de k-anonymisation et, en guise de cas pratique, est notamment appliqué à la base Transparence Santé. Le détail des différentes étapes est disponible ici : [Transparence Santé](https://github.com/SGMAP-AGD/anonymisation/wiki/Transparence-Sant%C3%A9).
Un autre exemple est appliqué sur la base du fichier des équidés.## DonnéesLes données exploitées pour tester notre algorithme peuvent être téléchargées :
* [Transparence Santé](https://www.data.gouv.fr/fr/datasets/transparence-sante-1/) (data.gouv.fr)
* [Enrichissement des données](http://www.insee.fr/fr/themes/detail.asp?reg_id=99&ref_id=equip-serv-medical-para) (INSEE)
* [Fichier des équidés](https://www.data.gouv.fr/fr/datasets/fichier-des-equides/) (data.gouv.fr)## Installationpip install anonymizer## Qui sommes-nous ?
Ce projet est développé par [l’équipe de l’Administrateur Général des Données (AGD)](http://agd.data.gouv.fr/), en collaboration avec toutes les bonnes volontés et les producteurs de données à caractère personnel. [Le forum d’Etalab](https://forum.etalab.gouv.fr/) est aussi ouvert à toute question, remarque ou suggestion. [Une consultation](https://forum.etalab.gouv.fr/search?q=anonymisation) sur les logiciels d’anonymisation y a notamment été initiée.
Package: anonypy
AnonyPyAnonymization library for python.
AnonyPy provides following privacy preserving techniques for the anonymization.K AnonymityL DiversityT ClosenessThe Anonymization methodAnonymization method aims at making the individual record be indistinguishable among a group record by using techniques of generalization and suppression.Turning a dataset into a k-anonymous (and possibly l-diverse or t-close) dataset is a complex problem, and finding the optimal partition into k-anonymous groups is an NP-hard problem.AnonyPy uses "Mondrian" algorithm to partition the original data into smaller and smaller groupsThe algorithm assumes that we have converted all attributes into numerical or categorical values and that we are able to measure the “span” of a given attribute Xi.Install$ pip install anonypyUsageimportanonypyimportpandasaspddata=[[6,"1","test1","x",20],[6,"1","test1","x",30],[8,"2","test2","x",50],[8,"2","test3","w",45],[8,"1","test2","y",35],[4,"2","test3","y",20],[4,"1","test3","y",20],[2,"1","test3","z",22],[2,"2","test3","y",32],]columns=["col1","col2","col3","col4","col5"]categorical=set(("col2","col3","col4"))defmain():df=pd.DataFrame(data=data,columns=columns)fornameincategorical:df[name]=df[name].astype("category")feature_columns=["col1","col2","col3"]sensitive_column="col4"p=anonypy.Preserver(df,feature_columns,sensitive_column)rows=p.anonymize_k_anonymity(k=2)dfn=pd.DataFrame(rows)print(dfn)Original datacol1col2col3col4col5061test1x20161test1x30282test2x50382test3w45481test2y35542test3y20641test3y20721test3z22822test3y32The created anonymized data is below(Guarantee 2-anonymity).col1col2col3col4count02-42test3y212-41test3y122-41test3z136-81test1,test2x246-81test1,test2y1582test3,test2w1682test3,test2x1
Package: anora
Anora==========================Anora is a Django template filter that determines whether or not a word should have an "a" or "an" in front of it. Adds either one of these depending on the phoenetic value of the given text. It will also automatically add one space before the text.Install-------1. ``pip install anora``2. Add ``anora`` to your ``INSTALLED_APPS``3. Add ``{% load anora %}`` to the top of templates you wish to use these tags in.Usage-------``{% load anora %}````I was taking a walk in the woods today. I came across {{ animal_type|anora  }}.``Possible outcomes from the above:-------I was taking a walk in the woods today. I came across a raccoon.I was taking a walk in the woods today. I came across an owl.Credits-------Original template filter can he found [here](http://djangosnippets.org/snippets/1519/).
Package: anospp-analysis
anospp-analysisPython package for ANOSPP data analysisANOSPP is the multiplexed amplicon sequencing assay for Anopheles mosquito species identification and Plasmodium detection. This repository contains the code for analysis of the sequencing results pre-processed withnf-core ampliseqpipeline.InstallationFor released versioncondainstall-cbiocondaanospp-analysisFor development setup, see instructions belowUsageKey analysis steps are implemented as standalone scripts:anospp-preptakes DADA2 output files and targets primer sequences, demultiplexes the amplicons and yields haplotypes tableanospp-qctakes haplotypes table, DADA2 stats table and samples manifest and produces QC plotsanospp-plasmblasts Plasmodium sequences against reference dataset to determine species and infer sample infection statusanospp-nncompares k-mer profiles of mosquito targets against a reference dataset and provides probabilistic species callsanospp-vaeprovides finer scale species prediction for An. gambiae complex with VAE projectionDevelopmentSetupInstallation is hybrid with conda + poetry:gitclonegit@github.com:malariagen/anospp-analysis.gitcdanospp-analysis
mambaenvcreate-fenvironment.yml
condaactivateanospp_analysis_dev
poetryinstallUsage & testingThe code in this repository can be accessed via wrapper scripts:anospp-qc\--haplotypestest_data/haplotypes.tsv\--samplestest_data/samples.csv\--statstest_data/stats.tsv\--outdirtest_data/qcBesides, individual components are available as a python API:$python
>>>fromanospp_analysis.utilimport*
>>>PLASM_TARGETS['P1','P2']TODO Automated testing & CIAdding Python depsIntroducing python dependencies should be done via poetry:poetryaddpackage_nameThis should update bothpyproject.tomlandpoetry.lockfilesIf the package should be used in development environment only, usepoetryaddpackage_name--devTo update environment after changes made topyproject.tomland/orpoetry.lockpoetryinstallAdding non-Python depsIntroducing non-python dependencies should be done via conda: editenvironment.yml,
then re-create the conda environment and poetry deps:mambaenvcreate-fenvironment.yml
condaactivateanospp_analysis
poetryinstallChanges in conda environment might also introduce changes to the python installation,
in which case one should update poetry lock filepoetrylock
Package: another
Another"Another" Python backend framework, for fun.InstallationpipinstallanotherYou also need an ASGI server, such asUvicornorGranian.pipinstalluvicorn# orpipinstallgranianUsageCreate amain.pyfile and copy-paste the following snippet into it.fromanotherimportAnother,Status,Request,Responseapp=Another()@app.get("/hello")defhellow_another(req:Request):returnResponse({"message":"Hello!","extra":req.query},status=Status.HTTP_200_OK)And then run the server:uvicornmain:appNow open this linklocalhost:8000/hello?first_name=Mads&last_name=Mikkelsenin your browser.
Package: another-bigquery-magic
another-bigquery-magicUnofficial bigquery magic Command for IPython notebookInstallation# from pypi$pipinstallanother-bigquery-magic# alternatively, from github$gitclonehttps://github.com/kota7/another-bigquery-magic.git--depth1$pipinstall-U./another-bigquery-magicUsage# Set the project IDproject_id="<google-cloud-project-id>"!gcloudconfigsetproject{project_id}# If you are authenticated to the google cloud already, skip this cell.# Otherwise, authenticate with your choice of method.# Example 1. Authentication on colabfromgoogle.colabimportauthauth.authenticate_user()# Example 2. Authentication by user log-in# Note: to access external table with google drive,#       we also need "https://www.googleapis.com/auth/drive" in the scope!gcloudauthapplication-defaultlogin--scopes="https://www.googleapis.com/auth/bigquery"# Example 3. Authentication with a local json filejsonfile="<json-file-path>"%configBigqueryMagic.localjson=jsonfile# Load the bq magic command%load_extbq# %bq magic command runs the query and returns the pandas data frame%bqSELECT1AStestStart query at 2024-01-12 15:31:07.286991
End query at 2024-01-12 15:31:10.047083 (Execution time: 0:00:02.760092, Processed: 0.0 GB)test01Seeexample.ipynbfor more examples.
Package: another-crazy-thursday
crazy_thursday: your python REFUSE to work on Crazy Thursdaycrazy_thursday is a library that allows you to go to KFC in time on Crazy Thursday by raisingCrazyThursdayException.Usagefrom crazy_thursday import CrazyThursday

@CrazyThursday
def hello():
    print('hello')

if __name__ == '__main__':
    hello() # crazy_thursday.kfc.CrazyThursdayException: KFC Crazy Thursday WhoEver Gives me 50 CNY, I Will Thank Him.
Package: another-demo
python example PackageThis is a simple example package.https://github.com/pypa/sampleproject.githttps://www.geeksforgeeks.org/what-is-setup-py-in-python/#https://packaging.python.org/en/latest/guides/installing-using-linux-tools/https://packaging.python.org/en/latest/tutorials/packaging-projects/pip install setuptools 
python setup.py sdist bdist_wheel
python setup.py sdist
pip install sdk
Package: another-fastapi-jwt-auth
Another FastAPI JWT AuthSource Code:https://github.com/delrey1/another-fastapi-jwt-auth/BackgroundThis was forked fromhttps://github.com/IndominusByte/fastapi-jwt-authas it is no longer maintained.This release contains changes related to Pydantic >2 and PyJWT > 2. I used this on my own projects and will be updating
it
as required. PRs invited.FeaturesFastAPI extension that provides JWT Auth support (secure, easy to use and lightweight), if you were familiar with flask-jwt-extended this extension suitable for you, cause this extension inspired by flask-jwt-extended 😀Access tokens and refresh tokensFreshness TokensRevoking TokensSupport for WebSocket authorizationSupport for adding custom claims to JSON Web TokensStoring tokens in cookies and CSRF protectionInstallationThe easiest way to start working with this extension with pippipinstallanother-fastapi-jwt-authLicenseThis project is licensed under the terms of the MIT license.
Package: another-linked-list
Another Linked ListTable of ContentsWhat is it?Why create it?Simple usageApiclass LinkedList([a list of elements])AttributesMethodsnodeTestWhat is it?An incomprehensive implementation of a doubly-linked listWhy create it?I didn't like the api or documentation of other linked lists.  I'm also new
to python so this was a good way to learn.Simple usagefromanother_linked_listimportLinkedList# create a list with three nodesll=LinkedList(["a","b","d"])# get the node with element "b"bNode=ll.findFirstNode("b")# insert "c" after bNodell.insertAfter(bNode,"c")print(list(ll))# prints ['a', 'b', 'c', 'd']Apiclass LinkedList([a list of elements])the linked list holds a list ofnodes.  Each node holds an element
(the value) along with pointers to the previous and next nodes.  For the most
part the methods are intended to allow you to work with the elements moreso
than the nodes because that was my use-case.  This design decision may change
in the future to be more focused around the nodes.all methods returnselfunless specified otherwiseall methods which take a list argument also accept an instance of LinkedListin all code examples below, assumellstarts withLinkedList(["a", "c"])the internal methods implemented are__copy____iter__ (iterates over the elements,notthe nodes)__len____reversed__AttributesfirstNode:nodeprint(ll.firstNode.element)# alastNode:nodeprint(ll.lastNode.element)# cMethodsappend(element)ll.append('d')print(list(ll))# ['a', 'c', 'd']copy() => LinkedListappendAll(list)ll.appendAll(['d','e'])print(list(ll))# ['a', 'c', 'd', 'e']findFirstNode(element) =>nodecNode=ll.findFirstNode(['c'])print(cNode.element)# cinsertAfter(node, element)ll.insertAfter(ll.firstNode,'b')print(list(ll))# ['a', 'b', 'c']insertAllAfter(node, list)ll.insertAllAfter(ll.firstNode,['b','d'])print(list(ll))# ['a', 'b', 'd', 'c']insertAllBefore(node, list)ll.insertAllBefore(ll.lastNode,['b','d'])print(list(ll))# ['a', 'b', 'd', 'c']insertBefore(node, element)ll.insertBefore(ll.lastNode,'b')print(list(ll))# ['a', 'b', 'c']prepend(element)ll.prepend('z')print(list(ll))# ['z', 'a', 'c']prependAll(list)ll.prependAll(['y','z'])print(list(ll))# ['y', 'z', 'a', 'c']removeFirstElement(element)ll.removeFirstElement('c')print(list(ll))# ['a']removeNode(node)ll.removeNode(ll.firstNode)print(list(ll))# ['c']nodea node is just an instance of SimpleNamespace with three attributeselement: <can be anything>next_: nodeprevious: nodeprint(ll.firstNode.element)# aprint(ll.firstNode.next_.element)# cprint(ll.lastNode.previous.element)# aprint(ll.firstNode.previousisNone)# TrueTest## you must have poetry installed#$poetryshell
$poetryinstall
$pythonrunTests.py
Package: anotherpdfmerger
anotherpdfmergerThis is a small program to concatenate PDFs and add bookmarks where the
start of each PDF begins. The bookmarks have the same filename as the
PDF, minus the.pdfextension.There are a few of these programs around, but none that had the exact
formatting I wanted. So here's another one.Install this withsudo pip3 install anotherpdfmergeror just run therun_anotherpdfmerger.pyscript directly.Either way, if you want to merge1.pdf,2.pdf, and3.pdfintocombined.pdf, runanotherpdfmerger 1.pdf 2.pdf 3.pdf combined.pdf
Package: another-setuptools-git-version
another-setuptools-git-versionAutomatically set package version from Git. This is a re-release of
[bad-setuptools-git-version][] with fixes and improvements, which is itself a re-release ofsetuptools-git-versionIntroductionInstead of hard-coding the package version insetup.pylike:setup(name='foobar',version='1.0.0',...)this package allows to extract it from tags in the underlying Git repository:setup(name='foobar',version_config={"version_format":"{tag}.{cc}","starting_version":"0.1.0"},setup_requires=['bad-setuptools-git-version'],...)The tool uses the semantically-latest tag as the base version. If there are no annotated tags, the version specified bystarting_versionwill be used. IfHEADis at the tag, the version will be the tag itself. If there are commits ahead of the tag, the first 8 characters of the sha of theHEADcommit will be included.
Package: another-sudoku-library
Another Sudoku LibraryLibrary for generating and solving Sudoku puzzles.Free software: MIT licenseDocumentation:https://another-sudoku-library.readthedocs.io.FeaturesGenerate new, random puzzlesSolve puzzlesCheck correctness of puzzlesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-01-10)First release on PyPI.
Package: anothertimer
Repository:https://github.com/dangcpham/anothertimerJust another timer for code timing. anothertimer enables easy timing for code runs, saving timing data, and provides basic plotting capabilities. Example usage:from anothertimer import Timer
timer = Timer()
timer.tic()
#some code here
timer.toc()Saving and loading data (modes ‘a’ for append and ‘w’ for write):timer.dump('example.csv', mode='a')
timer.load('example2.csv')Plotting can be done via:timer.plot()Check out example.py on GitHub for some more features (stopwatch/sections) and plots!Installationpip install anothertimerDependeciesStandard Python libraries (typing, time, os, csv).matplotlib (optional) - for plotting.OverheadMeasured by pstats on example.py, core functionalities causes minimal (<0.001s)
overhead. Plotting causes some overhead (due to matplotlib plotting, on the
order of a few seconds), so plot carefully.PrecisionCurrently, anothertimer uses the time.time() function to keep track of time. The precision is in the sub-second range (read more about Python timing athttps://www.python.org/dev/peps/pep-0564/,https://stackoverflow.com/questions/1938048/high-precision-clock-in-python). Currently, it is not a good idea to use this package for high precision applications, consider the timeit module.
Package: anova-wifi
Anova WifiA package to get read only data from Anova precision cookers with wifiInstallationInstall this via pip (or your favourite package manager):pip install anova-wifiContributors ✨Thanks goes to these wonderful people (emoji key):Luke💻🤔📖This project follows theall-contributorsspecification.
Contributions of any kind welcome!CreditsThis package was created withCopierand thebrowniebroke/pypackage-templateproject template.This is heavily based off of information
fromthis reddit postby/u/InitializedVariable
Package: anovos
AnovosAnovosis an open source library for feature engineering at scale.
Built by data scientists & ML Engineers for the data science community,
it provides all the capabilities required for data ingestion, data analysis,
data drift & data stability analysis, feature recommendation and feature composition.
In addition, it automatically produces easily interpretable professional data reports
that help users understand the nature of data at first sight and further enable data
scientists to identify and engineer features.Leveraging the power ofApache Sparkbehind the scenes,Anovosimproves data scientists' productivity and helps them build more resilient
and better performing models.Quick StartThe easiest way to try outAnovosand explore its capabilities is through the providedexamplesthat you can run via Docker without the need to install anything
on your local machine.# Launch an anovos-examples Docker containersudodockerrun-p8888:8888anovos/anovos-examples-3.2.2:latestTo reach the Jupyter environment, open the link tohttp://127.0.0.1:8888/?token...generated by the Jupyter NotebookApp.If you're not familiar withAnovosor feature engineering, theGetting Started with
Anovosguide is a good place to begin your journey.
You can find it in the/guidesfolder within the Jupyter environment.For more detailed instructions on how to install Docker and how to troubleshoot potential
issues, see theexamples README.Using AnovosRequirementsTo useAnovos, you need compatible versions ofApache Spark,
Java and Python.Currently, we officially support the following combinations:Apache Spark 2.4.x on Java 8 with Python 3.7.xApache Spark 3.1.x on Java 11 with Python 3.9.xApache Spark 3.2.x on Java 11 with Python 3.10.xTo see what we're currently testing, seethis configuration.InstallationYou can install the latest release ofAnovosdirectly throughPyPI:pipinstallanovosDocumentationWe provide a comprehensive documentation atdocs.anovos.aithat includesuser guidesas well as a detailedAPI documentation.For usage examples, see the providedinteractive guides and Jupyter notebooksas well as
theSpark demo.OverviewRoadmapAnovoshas designed for to support any feature engineering tasks in a scalable form.
To see what's planned for the upcoming releases, see ourroadmap.Development VersionTo try out the latest additions toAnovos, you can install it directly fromGitHub:pipinstallgit+https://github.com/anovos/anovos.gitPlease note that this version is frequently updated and might not be fully compatible with the documentation available
atdocs.anovos.ai.ContributeWe're always happy to discuss and accept improvements toAnovos. To get started, please refer to
ourContributing to Anovospage in the documentation.To start coding, clone this repository, install both the regular and development requirements,
and set up the pre-commit hooks:gitclonehttps://github.com/anovos/anovos.gitcdanovos/
pipinstall-rrequirements.txt
pipinstall-rdev_requirements.txt
pre-commitinstall