Saravanan666/pypi-org huggingface dataset:
Package: 01d61084-d29e-11e9-96d1-7c5cf84ffe8e
docstestspackageAn example package. Generated with cookiecutter-pylibrary.Free software: BSD 2-Clause LicenseInstallationpip install 01d61084-d29e-11e9-96d1-7c5cf84ffe8eDocumentationhttps://01d61084-d29e-11e9-96d1-7c5cf84ffe8e.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append tox
Package: 0.618
📦 setup.py (for humans)This repo exists to providean example setup.pyfile, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices forsetup.py, as well as some
commented–out nice–to–haves.For example, thissetup.pyprovides a$ python setup.py uploadcommand, which creates auniversal wheel(andsdist) and uploads
your package toPyPiusingTwine, without the need for an annoyingsetup.cfgfile. It also creates/uploads a new git tag, automatically.In short,setup.pyfiles can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)Check out the example!Installationcdyour_project# Download the setup.py file:#  download with wgetwgethttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py-Osetup.py#  download with curlcurl-Ohttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.pyTo DoTests via$ setup.py test(if it's concise).Pull requests are encouraged!More ResourcesWhat is setup.py?on Stack OverflowOfficial Python Packaging User GuideThe Hitchhiker's Guide to PackagingCookiecutter template for a Python packageLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: 0-core-client
# Zero-OS Python Client## Install```bashpip3 install 0-core-client```## How to use```pythonfrom zeroos.core0.client import Clientcl = Client(host='<0-core-host-address>', password='<JWT>')#validate that core0 is reachableprint(cl.ping())#then u can do stuff likeprint(cl.system('ps -eF').get())print(cl.system('ip a').get())#client exposes more tools for disk, bridges, and container mgmtprint(cl.disk.list())```
Package: 0lever-so
This is a SSH login toolInstallationpip install --upgrade 0lever-so
or
pip install --upgrade 0lever-so -i https://pypi.org/simple/Usage# 初始化配置文件,升级无需初始化,chmod 400 ~/.so/keys/*
➜  ~ so_install
➜  ~ cd .so
➜  .so tree
.
├── keys
│   └── demo.pem
└── password.yaml

1 directory, 2 files
➜  .so# 配置文件
ssh:
  - id: 1
    name: demo1
    user: fqiyou
    password: xxx
    host: 1.1.1.1
    port: 20755
  - id: 2
    name: demo2
    user: fqiyou
    password: xxx
    host: 1.1.1.1
    port: 39986
  - id: 3
    name: demo3
    user: root
    password: demo.pem
    host: 1.1.1.1
    port: 22Other-shell#!/usr/bin/expect
set USER "xxx"
set PASSWD "xxx"
set timeout 10

trap {
    set rows [stty rows]
    set cols [stty columns]
    stty rows $rows columns $cols < $spawn_out(slave,name)
} WINCH
spawn su - $USER
expect "Password: "
send "$PASSWD\n"
interact#!/usr/bin/expect -f
set HOST [lindex $argv 0]
set USER [lindex $argv 1]
set PASSWD [lindex $argv 2]
set PORT [lindex $argv 3]
set timeout 10

trap {
    set rows [stty rows]
    set cols [stty columns]
    stty rows $rows columns $cols < $spawn_out(slave,name)
} WINCH

spawn ssh $USER@HOST -p $PORT
expect {
    "*yes/no" {send "yes\r"; exp_continue}
    "*password:" {send "$PASSWD\r"}
}
interact
```
Package: 0-orchestrator
# Python ClientO-orchestrator is the Python client used to talk to [Zero-OS 0 Rest API](../README.md)## Install```bashpip install 0-orchestrator```## How to use```pythonIn [9]: from zeroos.orchestrator import  clientIn [10]: c = client.Client('http://192.168.193.212:8080')In [11]: c.api.nodes.ListNodes().json()Out[11]:[{'hostname': '', 'id': '2c600cbc2545', 'status': 'running'},{'hostname': '', 'id': '2c600ccd2ae9', 'status': 'running'},{'hostname': '', 'id': '0cc47a3b3d6a', 'status': 'running'},{'hostname': '', 'id': '2c600ccd2ad1', 'status': 'running'},{'hostname': '', 'id': '2c600cbc23bc', 'status': 'running'}]```## To update the client from the RAML file```shellgo-raml client -l python --ramlfile raml/api.raml --dir pyclient/zeroos/orchestrator/client```
Package: 0proto
Usagepip install 0protoThen, simply use the command periodically:0proto https://example.com/something/etcThis will save data to:settings.BASE_DIR/data/0proto-DOMAIN:default/ItemN-SpacingIf you want to seprate different sessions and sources, just use name param:0proto URI –name NameThis will save to:settings.BASE_DIR/data/0proto-DOMAIN:Name/TypeThe–namevalue can be arbitray filesystem-compatible filename sub-string, so, you can use it to separate data by accounts, languages, or other features.NOTE: Corresponding auth and session data will be stored insettings.BASE_DIR/sessionsfolder.Saving to specific DIRSaving to custom folder simply pass–pathparameter, like:0proto URI –name Name –path /home/mindey/Desktop/mydata
Package: 0rss
使い方pip install 0rssThen, simply use the command periodically:0rss https://0oo.li/feed/enThis will save data periodically, to:~/.metadrive/data/0rss-0oo.li:default/Post多源用法If you want to seprate different sessions and sources, just use name param:0rss https://0oo.li/feed/en –name mindey@example.comThis will save to:~/.metadrive/data/0rss-0oo.li:mindey@example.com/PostThe–namevalue can be arbitray filesystem-compatible filename sub-string, so, you can use it to separate data by accounts, languages, or other features.NOTE: Corresponding auth and session data will be stored in~/.metadrive/sessionsfolder.指定したフォルダーへ保存Saving to custom folder simply pass–pathparameter, like:0rss https://hub.baai.ac.cn/rss –name Mindey –path /home/mindey/Desktop/mydata
Package: 0x0-python
Здесь есть функции:upload_file_url(url, expires, secret): Загрузка файла через ссылку, url=ссылка, expires=время хранения файла в часах(можно оставить пустым), secret=удлинняет ссылку(можно оставить пустым).upload_file_path(path, expires, secret): Тоже самое что и upload_file_url, только нужно указывать путь к файлу.delete_file(token, url): Удаляет файл, token=токен, url=ссылкана файл в 0x0.change_expires(url, expires, token): Изменяет время хранения файла, token=токен, url=ссылка на файл в 0x0, expires=новое время хранение файла в часах.
Package: 0x10c-asm
Install from PyPI:pip install 0x10c-asmUsage:$ 0x10c-asm.py -husage:0x10-asm.py[-h]IN [OUT]A simple Python-based DCPU assembly compilerpositional arguments:INfile path of the file containing the assembly codeOUTfile path where to store the binary codeoptional arguments:-h,--helpshow this help message and exit
Package: 0x-contract-addresses
0x-contract-addressesAddresses at which the 0x smart contracts have been deployed.Read thedocumentationInstallingpipinstall0x-contract-addressesContributingWe welcome improvements and fixes from the wider community! To report bugs within this package, please create an issue in this repository.Please read ourcontribution guidelinesbefore getting started.Install Code and Dependenciespipinstall-e.[dev]Clean./setup.py clean --allLint./setup.py lintBuild Documentation./setup.py build_sphinxMoreSee./setup.py --help-commandsfor more info.
Package: 0x-contract-artifacts
0x-contract-artifacts0x smart contract compilation artifactsRead thedocumentationInstallingpipinstall0x-contract-artifactsContributingWe welcome improvements and fixes from the wider community! To report bugs within this package, please create an issue in this repository.Please read ourcontribution guidelinesbefore getting started.Pull in artifacts from TypeScript build environment./setup.pypre_installInstall Code and Dependenciespipinstall-e.[dev]Clean./setup.py clean --allLint./setup.py lintBuild Documentation./setup.py build_sphinxMoreSee./setup.py --help-commandsfor more info.
Package: 0x-contract-wrappers
0x-contract-wrappers0x contract wrappers for those developing on top of 0x protocol.Read thedocumentationInstallingpipinstall0x-contract-wrappersContributingWe welcome improvements and fixes from the wider community! To report bugs within this package, please create an issue in this repository.Please read ourcontribution guidelinesbefore getting started.Install Code and DependenciesThis package contains code generated via npm package @0x/abi-gen. Preparing this package for development or installation requires running./setup.py pre_install, which will invoke abi-gen to write the files to the src hierarchy. It expects to find abi-gen and the contract artifacts at the relative directory locations in the monorepo.After code generation, ensure that you have installed Python >=3.6 and Docker, and then:pipinstall-e.[dev]TestTests depend on a running ganache instance and with the 0x contracts deployed in it. For convenience, a docker container is provided that has ganache-cli and a snapshot containing the necessary contracts. A shortcut is provided to run that docker container:./setup.py ganache. With that running, the tests can be run with./setup.py test.Clean./setup.py clean --allLint./setup.py lintBuild Documentation./setup.py build_sphinxMoreSee./setup.py --help-commandsfor more info.
Package: 0x-json-schemas
0x-json-schemas0x JSON schemas for those developing on top of 0x protocol.Read thedocumentationInstallingpipinstall0x-json-schemasContributingWe welcome improvements and fixes from the wider community! To report bugs within this package, please create an issue in this repository.Please read ourcontribution guidelinesbefore getting started.Install Code and Dependenciespipinstall-e.[dev]Test./setup.py test.Clean./setup.py clean --allLint./setup.py lintBuild Documentation./setup.py build_sphinxMoreSee./setup.py --help-commandsfor more info.
Package: 0x-middlewares
0x-middlewaresWeb3 middlewares for 0x applications.Read thedocumentationInstallingpipinstall0x-middlewaresContributingWe welcome improvements and fixes from the wider community! To report bugs within this package, please create an issue in this repository.Please read ourcontribution guidelinesbefore getting started.Install Code and DependenciesEnsure that you have installed Python >=3.6 and Docker. Then:pipinstall-e.[dev]TestTests depend on running a local ethereum JSON-RPC server. For convenience, a docker container is provided that has ganache-cli.
A shortcut is provided to run that docker container:./setup.py ganache. With that running, the tests can be run with./setup.py test.Clean./setup.py clean --allLint./setup.py lintBuild Documentation./setup.py build_sphinxMoreSee./setup.py --help-commandsfor more info.
Package: 0x-order-utils
0x-order-utils0x order-related utilities for those developing on top of 0x protocol.Read thedocumentationInstallingpipinstall0x-order-utilsContributingWe welcome improvements and fixes from the wider community! To report bugs within this package, please create an issue in this repository.Please read ourcontribution guidelinesbefore getting started.Install Code and DependenciesEnsure that you have installed Python >=3.6 and Docker. Then:pipinstall-e.[dev]TestTests depend on a running ganache instance with the 0x contracts deployed in it. For convenience, a docker container is provided that has ganache-cli and a snapshot containing the necessary contracts. A shortcut is provided to run that docker container:./setup.py ganache. With that running, the tests can be run with./setup.py test.Clean./setup.py clean --allLint./setup.py lintBuild Documentation./setup.py build_sphinxMoreSee./setup.py --help-commandsfor more info.
Package: 100bot
# 100BotThis is a son-of-[IoBot](https://github.com/adahn6/Io) project, taking the bestthings about Io and turning them into a monster.A picture is worth 1000 words:![Screenshot of example conversation](example.png)100Bot is powered by the Watson Tone Analyzer service. Each message is carefullyparsed for emotional significance and response, so that the perfectlyappropriate reaction emoji can be chosen.## Running 100BotThe bot requires three very specific parameters:- A Watson Tone Analyzer `username` credential- A Watson Tone Analyzer `password` credential- A Slack Integration Token for a bot### Running with docker-composeThe easiest way to get 100bot up and running with dependencies is by using the docker service file included: `docker-compose.yml`. Modify the supplied `.env-sample` to provide credentials for the Watson Tone Analyzer and a Slack bot. Then build and start the service with:```shelldocker-compose up -d```### Running natively with pythonPip modules `slackclient` and `requests` must be installed. Use virtualenv to make your life easier. Passing of credentials can be done with argparse:```shellpython3 100bot.py \--bx-username "user" \--bx-password "verysecret" \--slack-token "xoob-137138657231-2ReKEpxlvWwe6vDBripOs7sR"```but can also be done with environment variables:```shellexport BLUEMIX_USERNAME=userexport BLUEMIX_PASSWORD=verysecretexport SLACK_TOKEN="xoob-137138657231-2ReKEpxlvWwe6vDBripOs7sR"python3 100bot.py```(yes, these are all fake credentials don't try them...)
Package: 101703301-Project1-TOPSIS
TOPSIS ImplementationProject1 UCS633Submitted by Kushagra Thakral 101703301Implementation of TOPSIS for a simple dataset having 4 columns.run in CMD:python3 project1.py file_name.csv 0.25 0.25 0.25 0.25 a-+++bwhere a and b can be replaced by any character except shell script keywords.
Package: 101703301-project3
#Handling NULL values in a dataset.
##Project3 UCS633
##Submitted by Kushagra Thakral 101703301To run in cmd line:python3 missing.py input_file_name.csvCount of null values present in each columns will be displayed.Then count of null values present after the use of package will be displayed(null values will be zero).Then the dataset will be displayed(first 5 rows).An output fill will be created with the name input_file_name_not_null.csvThe package only works for csv files.This package uses backfill method followed by forward fill method to handle all the null values
Package: 101703311-OUTLIERS
Outlier Removal Using InterQuartile RangeProject 2 : UCS633Submitted By:Lokesh Arora 101703311pypi:https://pypi.org/project/101703311_OUTLIERS/InterQuartile Range (IQR) DescriptionAny set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:The minimum or lowest value of the dataset.The first quartile Q1, which represents a quarter of the way through the list of all data.The median of the data set, which represents the midpoint of the whole list of data.The third quartile Q3, which represents three-quarters of the way through the list of all data.The maximum or highest value of the data set.These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.Calculation of IQRIQR = Q3 – Q1MIN = Q1 - (1.5IQR)MAX = Q3 + (1.5IQR)InstallationUse the package managerpipto install 101703311_OUTLIERS.pipinstall101703311_OUTLIERSHow to use this package:101703311_OUTLIERS can be run as shown below:In Command Prompt>> outlierRemoval dataset.csvSample datasetMarksStudents3Student157Student265Student398Student443Student544Student654Student799Student81Student9Output Dataset after RemovalMarksStudents57Student265Student398Student443Student544Student654Student7It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.LicenseMIT
Package: 101703312-outlierRemoval
Outlier Removal Using InterQuartile RangeProject 2 : UCS633Submitted By:Lovish Jindal 101703312pypi:https://pypi.org/project/101703312_outlierRemoval/InterQuartile Range (IQR) DescriptionAny set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:The minimum or lowest value of the dataset.The first quartile Q1, which represents a quarter of the way through the list of all data.The median of the data set, which represents the midpoint of the whole list of data.The third quartile Q3, which represents three-quarters of the way through the list of all data.The maximum or highest value of the data set.These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.Calculation of IQRIQR = Q3 – Q1MIN = Q1 - (1.5IQR)MAX = Q3 + (1.5IQR)InstallationUse the package managerpipto install 101703312_outlierRemoval.pipinstall101703312_outlierRemovalHow to use this package:101703312_outlierRemoval can be run as shown below:In Command Prompt>> outlierRemoval dataset.csvSample datasetMarksStudents3Student157Student265Student398Student443Student544Student654Student799Student81Student9Output Dataset after RemovalMarksStudents57Student265Student398Student443Student544Student654Student7It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.LicenseMIT
Package: 101703373-topsis
TOPSIS PackageTOPSIS stands for Technique for Oder Preference by Similarity to Ideal Solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.This package has been created based on Project 1 of course UCS633. Nikhil Vyas COE-17 101703373In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+"
Package: 101703573-Missing-pkg-suruchipundir
Filling Missing ValuesMissing Data can occur when no information is provided for one or more items or for a whole unit. Missing Data is a very big problem in real life scenario. Missing Data can also refer to asNA(Not Available) values in pandas. In DataFrame sometimes many datasets simply arrive with missing data, either because it exists and was not collected or it never existed.
In this package, the missing values in a csv file are filled using the fillna function in pandas. For this the statistical model of mean is used.Usage$ python3 missing.py filename
Package: 101703573-Topsis-pkg-suruchipundir
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).To run on command line
python3 topsis.py diabetes.csv "1,2,5,10,1,2,1,1,2" "-,+,+, -, +, +,+,-, +"
Package: 102003037-topsis
102003037 TOPSIS PACKAGE HIMANGI SHARMARoll Number : 102003037Subgroup : 3COE18The program takes csv file containing our data to be ranked, weights and impacts in the form of "+" or "-", seperated by commas as inputs and then outputs a resultant csv file with two additional columns of performance score and Ranks.What is TOPSISTOPSIS, Technique of Order Preference Similarity to the Ideal Solution, is a multi-criteria decision analysis method (MCDA).It chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.InstallationHow to install the TOPSIS packageusing pip install:-pip install 102003037-topsis-HimangiFor Calculating the TOPSIS ScoreOpen terminal and type102003037 102003037-data.csv "1,1,1,1" "+,+,-,+" 102003037-output.csvThe output will then be saved in a newly created CSV file whose name will be provided in the command line by the user.Input File [102003037-data.csv]:Topsis mathematical operations to be performed on the input file which contains a dataset having different fields.Weights ["1,1,1,1"]The weights to assigned to the different parameters in the dataset should be passed in the argument, seperated by commas.Impacts ["+,+,-,+"]:The impacts are passed to consider which parameters have a positive impact on the decision and which one have the negative impact. Only '+' and '-' values should be passed and should be seperated with ',' only.Output File [102003037-output.csv]:This argument is used to pass the path of the result file where we want the rank and performance score to be stored.
Package: 102003105
# Topsis Value Calculator
Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) helps solve MADM problems.This is a Python package implementing TOPSIS method for multi-criteria decision analysis.## Installation$ pip install TOPSIS-102003105In the commandline, you can write as -$ python <package_name> <path to input_data_file_name>
<weights as strings> <impacts as strings> <result_file_name>E.g for input data file as data.csv, command will be like$ python 102003105.py 102003105-data.csv “0,1,1,1,2,1” “+,-,-,+,-,+” 102003105-Result1.csvThis will give the output in 102003105-Result1.csv fileLicense -> MIT
Package: 102003766-topsis
TopsisTOPSIS is based on the fundamental premise that the best solution has the shortest distance from the positive-ideal solution, and the longest distance from the negative-ideal one. Alternatives are ranked with the use of an overall index calculated based on the distances from the ideal solutions.It takes 4 arguments :
1.Data.csv file
2.Weights
3.Impacts
4.Result fileReturns file with Ranks and Topsis ScoreHow to use it?Open terminal and type topsis data file name weights impacts result file nameex: topsis data.csv 1,1,1,1 +,+,-,+ resullt.csv
Package: 102017059-Aakanksha-Topsis
102017059_Aakanksha_TopsisThis package is implementation of multi-criteria decision analysis using topsis. This package will accept three arguments during file execution:dataset.csv //file which contains the models and parameters
string of weights separated by commas(,)
string of requirements (+/-) separated by commas(,) // important install pandas,sys,operator and math libraries before installing this // You can install this package using following command pip install 102017059_Aakanksha_Topsis
Package: 102017067-topsis
TOPSIS PackageTOPSIS stands for Technique for Oder Preference by Similarity to Ideal Solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.This package has been created based on Project 1 of course UCS633. Tarandeep Singh 102017067In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+"
Package: 102017119-topsis
TOPSIS PackageTOPSIS stands for Technique for Oder Preference by Similarity to Ideal Solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.This package has been created based on Assignment 1 of course UCS654. Prince Sharma 102017119In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+"