Saravanan666/pypi-org huggingface dataset:
Package: examplelspserver
exampleDSL for referenceOptional: check python packagesupdate package manager toolspython -m pip install --upgrade pip setuptools wheelcheck if python packagevirtualenvis installed if Python < 3.3 is used else a subset of it has been integrated into the standard library undervenvpython -m pip show virtualenvif not install packagevirtualenvinto global system environmentpython -m pip install virtualenvInstall Server Dependenciesgenerate virtual environment in directory.venvusing the standard libraryvenvpython -m venv .venvalternative usevirtualenv, see optional python packagesactivate virtual environment.venvsource .venv/bin/activateinstallantlr4-python3-runtime,Jinja2,mock,pyglsandpytestpackage into virtual environment.venvpython -m pip install antlr4-python3-runtime Jinja2 mock pygls pytestOptional: update npm and nodechecknpmversionnpm --versioninstall latestnpmversion globally (-gor--global)sudo npm install -g npm@latestinstall latestnodeversionsudo n latestInstall the dependencies to the global mode (-gor--global)sudo npm update -gInstall Client DependenciesGoto extension directorypython-oceandsls/example-dslInstall the dependencies to the local node_modulesnpm installRun Example using VSCodeOpenpython-oceandsls/example-dslin VSCodeTrust author files inpython-oceandsls/example-dslGoto Run and DebugCtrl/Cmd+Shift+DSelectServer + ClientStart Debugging viaF5or GUIOpen a*.odslfileIf server is working correctly notification showsText Document Did OpenTroubleshootingIf VS Code doesn`t automatically locate the interpreter of the virtual environment.Set it manuallyOpen Command promptCtrl/Cmd+Shift+PRun select interpreter commandPython: Select InterpreterAlternative create.vscode/settings.jsonfile inpython-oceandsls/example-dsldirectory and setpython.defaultInterpreterPathto point to the virtual environmentsettings.json{
    // set Python Interpreter relative to workspaceFolder to virtual environment '.venv'
    "python.defaultInterpreterPath": "${workspaceFolder}/../.venv/bin/python",
    // alternative
    //"python.defaultInterpreterPath": "../.venv",
    // deprecated
    // "python.pythonPath": "${workspaceFolder}/../.venv/bin/python",

    // Pylance VSCode code analysis and auto-completion using Python 3.10
    "python.analysis.extraPaths": [
        "{workspaceFolder}/../.venv/lib/python3.10/site-packages/:${workspaceFolder}/../antlrLib/"
    ],


    // Object with environment variables that will be added to the VS Code process to be used by the terminal on OS X
    "terminal.integrated.env.osx": {"PYTHONPATH": "${workspaceFolder}/.."},

    // Object with environment variables that will be added to the VS Code process to be used by the terminal on Linux
    "terminal.integrated.env.linux": {"PYTHONPATH": "${workspaceFolder}/.."},

    // Object with environment variables that will be added to the VS Code process to be used by the terminal on Windows
    "terminal.integrated.env.windows": {"PYTHONPATH": "${workspaceFolder}/.."},
}If npm version conflicts exists change npm to the required versionE.g. reset npm version to 9.2.0sudo npm install -g npm@9.2.0
Package: example-magic
example IPython magicExtension for add two example magics in IPython shell or IPy kernel.The code for the magics was taken from IPython docs:https://ipython.readthedocs.io/en/stable/config/custommagics.html#defining-custom-magicsInstallationpip install example_magicUsageYou can use it from IPython shell or in Jupyter lab/notebook with Python kernel:In [1]: %load_ext example_magic                                       

In [2]: %abra                                                         
Out[2]: ''

In [3]: %%cadabra 
   ...: world 
   ...:                                                               
Out[3]: ('', 'world\n')
Package: example_maths
# OverviewThis repo is for creating best practice python packages. Marco Bonzanini’s [definition](https://marcobonzanini.com/2015/07/01/how-to-develop-and-distribute-python-packages/) of a package is as follows:> In Python, a simple source file containing the definitions of functions, classes and variables, is a module… On the other side, a package is a folder containing an __init__.py file, as well as other different Python source files. Typically a package contains several modules and sub-packages.This repo assumes you have understanding how to set up a Python Module and have done so before going any further.# Unit TestingFor unit testing, Dive Into Python 3’s example of [unit testing](http://www.diveintopython3.net/unit-testing.html) is a great resource. It’s a simple Roman Numeral conversion example that walks you through the bulk of what you’ll need to know.# LoggingShould an error occur when the package is live, logs are a great method to provide information to stdout. Jorgen Schaefer’s [guide to logging](https://www.loggly.com/ultimate-guide/python-logging-basics/) establishes logging convention, and the developer’s role.As logging doens’t function part of the example ‘maths’ module, I’m currently of the opinion that it can be kept in a seperate utils module# Error HandlingNaturally this moves us on to custom error handling where Aaron Maxwell’s [exceptional logging of exceptions](https://www.loggly.com/blog/exceptional-logging-of-exceptions-in-python/) article takes centre stage. It’s a moderate advancement on the error handling you were used to in the unit testing example, but does allow us to capture and relay traceback information in defined patterns.It additionally defines anti-patterns for which neither the Unit Testing or Loggging links provide, i.e. what is considered unacceptable logging.### NotesThis tutorial assumes you know about Python classes. If not, look no further than Python’s [classes tutorial](https://docs.python.org/3/tutorial/classes.html)‘exc_info’ returns ‘NoneType: None’ should ‘if else raise’ statements be performed.# Creating a PackagePython’s own [packaging tutorial](https://packaging.python.org/tutorials/distributing-packages/) covers the basic scaffolding.Peter Downs’ article on [getting setup with PyPI](http://peterdowns.com/posts/first-time-with-pypi.html). It solves some of the problems around uploading packages to PyPI not covered in the packaging tutorial, and shows you how to avoid repetitive authentication with (admittedly) a plain text file.
Package: example-nim-pkg-stever
Example Package with NimThis is a simple example package with Nim module.Build stepsNOTE: These build steps are using Linux/macOS or similar. For Windows, the Nim
module will be compiled tomymodule.pydinstead ofmymodule.so.cdpy_module_nim/example_pkg/
nimbleinstallnimpy
nimcompile--threads:on--app:lib--out:mymodule.somymodulecd..
virtualenvvenvsourcevenv/bin/activate
pythonsetup.pysdistbdist_wheelPublish stepscdpy_module_nim/
pipinstalltwine
twineuploaddist/*Example using packageWrite the following Python script to file namedtry.py:fromexample_nim_pkgimportmymoduleprint(mymodule.greet("World"))Install theexample-nim-pkg-steverpackage and run the script:pipinstallexample-nim-pkg-stever
pythontry.py
Package: example-package
Welcome to GitHub PagesYou can use theeditor on GitHubto maintain and preview the content for your website in Markdown files.Whenever you commit to this repository, GitHub Pages will runJekyllto rebuild the pages in your site, from the content in your Markdown files.MarkdownMarkdown is a lightweight and easy-to-use syntax for styling your writing. It includes conventions forSyntax highlighted code block# Header 1## Header 2### Header 3-Bulleted-List1.Numbered2.List**Bold**and_Italic_and`Code`text

[Link](url) and ![Image](src)For more details seeGitHub Flavored Markdown.Jekyll ThemesYour Pages site will use the layout and styles from the Jekyll theme you have selected in yourrepository settings. The name of this theme is saved in the Jekyll_config.ymlconfiguration file.Support or ContactHaving trouble with Pages? Check out ourdocumentationorcontact supportand we’ll help you sort it out.
Package: example-package-Akalya
<<<<<<< HEADmle-training=======mle-trainingMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >
Package: example-package-akyepitsme
https://www.youtube.com/watch?v=v4bkJef4W94<< video >>Make sure you have upgraded version of pipWindowspy -m pip install --upgrade pipLinux/MAC OSpython3 -m pip install --upgrade pipCreate a project with the following structurepackaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir testspyproject.tomlThis file tells tools like pip and build how to create your project[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.Setup.cfg setupUsing setup.cfg is a best practice, but you could have a dynamic setup file using setup.py[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = srcRunning the buildMake sure your build tool is up to dateWindowspy -m pip install --upgrade buildLinux/MAC OSpython3 -m pip install --upgrade buildCreate the buildpy -m buildReferenceshttps://packaging.python.org/tutorials/packaging-projects/
Package: example-package-aliciane-pinault
M1-2022-gitIntroductionThis repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.Getting startedYou should be working fromyour own repositorythat is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.Clone this projectmove into the project foldercdM1-2022-git-workflowSet up a virtual environmentCreate the virtual environmentpython-mvenvmyvenvActivate venv.venv/bin/activateInstall pytestpipinstall-UpytestExecute main scriptpythonmain.pyInstructionsMerge feature branch into devNow that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Selectbaseandcompare. Remember : we want to mergedecimal-binary-converterintodev.
Then,Create the pull request. Add some description of the work you have done and publish the Pull Request.Note: We could have just mergedecimal-binary-converterintodevfrom the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.Publish your workWe now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.First, you have to create an account onPypi.orgThen, in your repository, you will find a.github directoryInside this directory, you will find .yml files that defines workflows (using GitHub actions)Open the yml files and take time to understand them. Do not hesitate to ask the teacher about themHands on !We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPIIn GitHub, click on theActionstab, then click onnew workflowChoose among the suggestions the template that best suits your use-case and clickConfigureYou may find some useful informations in thePython documentationWhen creating a release on GitHub, do not forget to create a tag corresponding to that releasePull requestOnce the workflow is setup and tested (you have published your package using it) you can open a pull request to theoriginal repository
Package: example-package-analytic-lib
To use this Average and percentage library,input the following parameters.#for "Average"  of set of numbers,
"sum_of_numbers": means the total sum of all numbers that you are about getting the average
"count_of_numbers":means the count of the total numbers you are summing#for "percentage of set nof numbers""total_of_particular_entity": this is the particular entity you are about getting the percentage out of the whole entitiestotal_of_whole_entity": this is the value of the whole entities.you are to use the instance this way:ifname== 'main':
ap = AnalyticProperties()
sum_of_numbers=678
count_of_numbers=67
total_of_particular_entity=499
total_of_whole_entity=50000print('\n the average of the count of {} entries  to the total sum of all entries {}  is   : {}'.format(count_of_numbers,sum_of_numbers, ap.average(count_of_numbers,sum_of_numbers)))
print('The percentage of {}  to {} is : {}%'.format(total_of_particular_entity,total_of_whole_entity, ap.percentage(total_of_particular_entity,total_of_whole_entity)))
Package: example-package-andrewlayer
Make sure you have upgraded version of pipWindowspy -m pip install --upgrade pipLinux/MAC OSpython3 -m pip install --upgrade pipCreate a project with the following structurepackaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir testspyproject.tomlThis file tells tools like pip and build how to create your project[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.Setup.cfg setupUsing setup.cfg is a best practice, but you could have a dynamic setup file using setup.py[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = srcRunning the buildMake sure your build tool is up to dateWindowspy -m pip install --upgrade buildLinux/MAC OSpython3 -m pip install --upgrade buildCreate the buildpy -m buildReferenceshttps://packaging.python.org/tutorials/packaging-projects/
Package: example-package-bisan
IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core
Package: example-package-calculator
Simple CalculatorIt's a simple calculator wich do either add, subtract, multiply, divide or take a root of a number.Example how to use:# Instalation from PyPI:

pip install example-package-calculatorCode:from example_package_calculator import Calculator

calculator = Calculator()

calculator.add(5, 5, 6)
16
calculator.root()
4
calculator.multiply(5)
20
calculator.reset()
0
Package: example-package-colleenDesruisseaux
M1-2022-gitIntroductionThis repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.Getting startedYou should be working fromyour own repositorythat is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.Clone this projectmove into the project foldercdM1-2022-git-workflowSet up a virtual environmentCreate the virtual environmentpython-mvenvmyvenvActivate venv.venv/bin/activateInstall pytestpipinstall-UpytestExecute main scriptpythonmain.pyInstructionsMerge feature branch into devNow that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Selectbaseandcompare. Remember : we want to mergedecimal-binary-converterintodev.
Then,Create the pull request. Add some description of the work you have done and publish the Pull Request.Note: We could have just mergedecimal-binary-converterintodevfrom the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.Publish your workWe now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.First, you have to create an account onPypi.orgThen, in your repository, you will find a.github directoryInside this directory, you will find .yml files that defines workflows (using GitHub actions)Open the yml files and take time to understand them. Do not hesitate to ask the teacher about themHands on !We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPIIn GitHub, click on theActionstab, then click onnew workflowChoose among the suggestions the template that best suits your use-case and clickConfigureYou may find some useful informations in thePython documentationWhen creating a release on GitHub, do not forget to create a tag corresponding to that releasePull requestOnce the workflow is setup and tested (you have published your package using it) you can open a pull request to theoriginal repository
Package: example-package-HA-TUONG-NGUYEN
🐼nguyenpanda🐼nguyenpandaOverviewWelcome to the world ofnguyenpanda, a Python library that brings you the essence of utility packages,
each named after animals, perfectly representing its unique domain.🦋 butterflyEmbark on a journey through randomness and chaos with the enigmaticbutterflypackage.
Delve into the realms of uncertainty, probability, and statistics.
Embrace the unpredictable nature of nguyenpanda'sbutterfly.
Remember, even the flutter of abutterfly'swings can cause significant changes in a complex system,
so seize the opportunity to explore its mysteries.🦉 owlIlluminate the path to mathematical enlightenment with the wisdom of theowl.
Ascend to mastery in calculus, conquer the realms of linear algebra,
and traverse the diverse landscapes of mathematics.
Allow nguyenpanda'sowlto be your steadfast companion on your intellectual odyssey.🐦‍⬛ ravenEnter the realm of the raven, where intelligence and innovation
converge in a comprehensive library of data structures
Inspired by the ingenuity of the raven, this package offers elegant data structures classes.🦢 swanIndulge in the beauty of aesthetics and user interfaces with the elegantswanpackage.
Dive into the world of colors, front-end development, GUI, and more.
Transform your applications into visual masterpieces withswan.InstallationTo embark on your nguyenpanda adventure, install the library with the following command:pipinstallnguyenpandaFor a specific version, gracefully flutter to your desired release with:pipinstallnguyenpanda==<version>Getting StartedUnleash the power of nguyenpanda by delving into the documentation and perusing code examples within each package directory. Witness the harmonious convergence of butterfly, owl, and swan as they elevate your projects in distinct domains.🤝 ContributionsBecome a part of the nguyenpanda community! Share your ideas, report bugs, or request features through GitHub issues. We welcome your contributions and enhancements.🪪 Licensenguyenpanda is released under the MIT License. Feel the freedom to explore, utilize, and contribute, respecting the terms outlined in the license.☎️ ContactHà Tường Nguyên (Mr.)Computer ScienceOffice for International Study Programs | HCMC University of TechnologyContact via:Gmail:nguyen.hatuong0107@hcmut.edu.vnGmail:hatuongnguyen0107@gmail.comHappy coding with nguyenpanda! 🚀