Saravanan666/pypi-org huggingface dataset:
Package: jupyter-docx-bundler
Jupyter docx bundler extensionJupyter bundler extension to export notebook as a docx fileInstallationUsing condaconda install -c conda-forge jupyter-docx-bundlerUsing pipMake sure you havePandocinstalled, seeinstalling-pandocfor instructions.pip install jupyter-docx-bundlerUsageAdding MetadataThe bundle extension uses metadata of the notebook, if you you provide it."title": "Notebook title""authors": [{"name": "author1"}, {"name": "author2"}]"subtitle": "Notebook subtitle""date": "Notebook date"The notebook metadata can be edited underEdit->Edit Notebook Metadata.Hiding inputs or complete code cellsYou can hide individual code cells or just their inputs by defining cell tags:nbconvert-remove-cell: Remove the entire cellnbconvert-remove-input: Remove the input code of the cell(Currently there are no default values configured for these tags, the ones listed above are defined in my code and not innbconvert. This may will change in the future.)Cell tags can be shown by activating the cell toolbar underView->Cell Toolbar->Tags.Hiding all inputsIt is also possible to hide all inputs. To achive this you need to add the following lines to your notebook metadata:{"jupyter-docx-bundler":{"exclude_input":"True"}}The notebook metadata can be edited underEdit->Edit Notebook Metadata.Direct call from console (nbconvert)To use the bundler direct from console the nbconvert utility can be used with target format docx:jupyter nbconvert --execute --to=docx <source notebook>.ipynb --output <target document>.docxThe--executeoption should be used to ensure that the notebook is run before generation.DevelopmentSeeCONTRIBUTING
Package: jupyter-doexport
jupyter-doexportThis packages provides a context menu and template for exporting a jupyter notebook with Stata kernel, to a do-file with all markdown and code ready to be executed at the Stata GUI or console.To install the exporter, write:pip install jupyter-doexportFeaturesThis package features two types of export, a regular script export to that you can then run in Stata,dofile, and one which also includes output from the notebook making it a sort of hybrid do-file/log, calleddofile_output.Your jupyter notebook should be available for export from theFile ->  Download asmenu.Alternatively, you can export your notebook from the commandline like so:jupyter nbconvert --to [dofile/dofile_output] <name of file>Currently, translation of stata_kernel magics are not supported.Thanks also toKyle Barron for an amazing Stata kernel implementation.
You can see how to install ithere.
Package: jupyter-dojo
A JupyterLab and Jupyter Notebook extension for rendering unittest
results.This project consists in a nbextension for Jupyter Notebooks and a
labextension for Jupyter Lab. It adds the following features to Jupyter:
* Test status in toolbar (result ofIPython
Unittest) *
Argument –previous, to move to previous cells after execution * Timer,
pair programming log, function generation for coding dojo sessions *
Syntax highlight for%%write {mode}magicPrerequisitesJupyterLab ^0.30.0 and/or Notebook >=4.3.0How to InstallIf you want to install theJupyter Notebookextension:pipinstalljupyter_dojojupyternbextensionenable--py--sys-prefixjupyter_dojoIf you want to install theJupyter Labextension:jupyterlabextensioninstall@jupyter_dojo/labextensionNote that Jupyter Lab extension API is not stable yet. Thus, it is very
likely that future version will break this extension.Developmentpipinstall-e.# For JupyterLabjupyterlabextensionlinklabextensionjupyterlabbuild# For Notebookjupyternbextensioninstall--symlink--py--sys-prefixjupyter_dojojupyternbextensionenable--py--sys-prefixjupyter_dojoContactDo not hesitate to contact me:João Felipe Pimenteljoaofelipenp@gmail.comLicense TermsPermission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: jupyter-drawio-export
jupyter-drawio-exportPDF export ofdrawiodiagrams for Jupyter.See theproject repofor more information.Server Extensionjupyter-drawio-exportruns as aserver extension. When installed,
itshouldbe automatically configured. If problems arise, it can be manuallyenabled:jupyterserverextensionlist# If you don't see it here...jupyterserverextensionenable--sys-prefix--pyjupyter_drawio_export# run this...jupyterserverextensionlist# ... then check it againCommand-Line UsageExporting PDFPDF can also be generated from the command line:pipinstalljupyter-drawio-export
jupyterdrawio-exportsome_file.dioIf needed, thenodejsapplication will be installed into the Jupyter data path.jupyterconfig--paths# it uses the one in the environmentIn the future, it's hoped this can be packaged more conveniently.ProvisioningTo ensure thenodejsapplication is installedwithoutexporting a PDF (e.g
when building a docker container).jupyterdrawio-configprovisionOpen SourceThis work is licensed under theApache 2.0.The vendored code from@jgraph/draw-image-export2is also licensed under theApache-2.0License.
Package: jupyter-earth
Jupyter Earthpipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnjupyterlab
Package: jupyter-echarts-pypkg
The project packagesjupyter-echartsand distributes it via pypi.InstallationYou can install jupyter-echarts-pypkg via pip:$pipinstalljupyter-echarts-pypkgor clone it and install it:$gitclonehttps://github.com/pyecharts/jupyter-echarts-pypkg.git$cdjupyter-echarts-pypkg$pythonsetup.pyinstallChange log0.1.5 - 12/01/2019UpdatedUpdate echarts-liquid to 2.0.4 and echarts-wordcloud to 1.1.30.1.4 - 09/11/2018Updateduse lml >= 0.0.20.1.2 - 28/07/2018Updateduse jupyter-echarts version 1.4.10.1.1 - 24/04/2018Updatedpyecharts#492: Pip 10
compatibility issues0.1.0 - 03/03/2018Updatedupdated jupyter-echarts to 1.4.00.0.11 - 22/02/2018AddedFirst usable pypkg
Package: jupyter-education
🪐 👩‍🎓 Jupyter EducationJupyer Education.pipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist
yarnjupyterlabResourceshttps://github.com/jmshea/jupytereducationhttps://twitter.com/kolibril13/status/1639262409175990272
Package: jupyter-environments
🪐 🌳 Jupyter EnvironmentsEnvironmnents for Jupyter.Jupyter Environments allow Users to define the packages and libraries (Python...) that are used in their Kernels.An Environment is also the place where Users define their Secrets and Environment Variables.Developpipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist
yarnjupyterlab
Package: jupyter-events
Jupyter EventsAn event system for Jupyter Applications and extensions.Jupyter Events enables Jupyter Python Applications (e.g. Jupyter Server, JupyterLab Server, JupyterHub, etc.) to emitevents—structured data describing things happening inside the application. Other software (e.g. client applications like JupyterLab) canlistenand respond to these events.InstallInstall Jupyter Events directly from PyPI:pip install jupyter_eventsor conda-forge:conda install -c conda-forge jupyter_eventsDocumentationDocumentation is available atjupyter-events.readthedocs.io.About the Jupyter Development TeamThe Jupyter Development Team is the set of all contributors to the Jupyter project.
This includes all of the Jupyter subprojects.The core team that coordinates development on GitHub can be found here:https://github.com/jupyter/.Our Copyright PolicyJupyter uses a shared copyright model. Each contributor maintains copyright
over their contributions to Jupyter. But, it is important to note that these
contributions are typically only changes to the repositories. Thus, the Jupyter
source code, in its entirety is not the copyright of any single person or
institution. Instead, it is the collective copyright of the entire Jupyter
Development Team. If individual contributors want to maintain a record of what
changes/contributions they have specific copyright on, they should indicate
their copyright in the commit message of the change, when they commit the
change to one of the Jupyter repositories.With this in mind, the following banner should be used in any source code file
to indicate the copyright and license terms:# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
Package: jupyterexcel
jupyterexcel PackageThis is a python package to make Jupyter.ipynb file a web api with json result. You can call Jupyter from Excel Formula or Ribbon CallBack Functions
SourceCode inJupyterExcelBefore install, please downloadJupyterExcelTesting.xlsmto try its formula function and excel ribbon.  If you think it works, you can modify the url in the sheet to your jupyter page and save it as "Addin" file.This Jupyter Excel web api can be connected with Excel addin which call this web api. Excel formula will generate a web api url and through winhttp to get json result. It now works Mac Excel by usingVBA-Web.Installationpip install jupyterexcelthen runjupyter serverextension enable --py jupyterexcelServer settingPlease follow configjupyter serveror use commandjupyter notebook --generate-config 
	    if the file alraady there, please do not override.  Remember the file pathand change following values:c.NotebookApp.token = 'ABCD'   #now, it has to use token mode. In the url in Excel, if it is not token, it will cause page forward to ask password, the excel will not work
c.NotebookApp.allow_origin = '*'  #allow any origin to access your server.  You can ignore following 3 connections,if you connect only from local computer
c.NotebookApp.allow_remote_access = True  #if you like to set to access from other computer
c.NotebookApp.ip = '0.0.0.0'   #allow all ip address to connect to this instance 
c.NotebookApp.iopub_data_rate_limit = 32000000  #it might be good to change to a high number, if you will pass out large amount of data. (bytes/sec) Maximum rate at which stream output can be sent on iopub beforeExampleThe following screenshot shows the sample notebook file with a function sum. You can download TestingJupyter.ipynb or create your own.  The following is an instance I hosted in google cloud platform, you can open and add a function of yours.http://www.jupyterexcel.com:8888/Excel/TestingJupyter.ipynb?token=ABCD&functionname=sum&1=11&2=8&3=6http://www.jupyterexcel.com:8888/notebooks/TestingJupyter.ipynb?token=ABCDplease change 34.67.24.96 to your computer name or localhostThe following screenshot shows how excel Formula works.The following screenshot Shows how Ribbon Call Back function worksFuture Development PlanMake Excel client side more easier to use, such as generate Excel formula proxyAble to support R, Julia ....It might only support one notebook pageReferenceread some code fromappmode
Package: jupyter-existing-provisioner
jupyter_existing_provisionerThis package register existing_provisioner endpoint for attach to the running jupyter kernel.pip install jupyter_existing_provisioner
# check endpoint
jupyter pyxll installBased onPyXLL-Jupyter.RequirementsJupyter-client >= 7.0.0Optionaljupyterlab >= 4.0.0InstallationTo install this package use:pip install jupyter_existing_provisionerOnce installed registerexisting_provisionerendpoint forjupyter_client.# check endpoints
jupyter kernelspec provisionersJupyter Lab# prepare working environment
pip install jupyterlab
pip install qtconsole
python embed_qtconsole.py

# connect to the last kernel
jupyter lab --KernelProvisionerFactory.default_provisioner_name=existing-provisioner

# linux: connect to the kernel-xxxx.json file 
EXISTING_CONNECTION_FILE=kernel-xxxx.json jupyter lab --KernelProvisionerFactory.default_provisioner_name=existing-provisioner

# windows: connect to the kernel-xxxx.json file 
set EXISTING_CONNECTION_FILE=kernel-xxxx.json
jupyter lab --KernelProvisionerFactory.default_provisioner_name=existing-provisionerIn the working kernel show connection info magic%connect_infoGoogle ColabConnect instruction# prepare for connect colab
pip install jupyter_http_over_ws
jupyter serverextension enable --py jupyter_http_over_ws

jupyter lab --KernelProvisionerFactory.default_provisioner_name=existing-provisioner \
    --ServerApp.allow_origin='https://colab.research.google.com'StartGoogle Colaband
connect to the local environment - url from terminal.
Package: jupyterfair
jupyterfaira tool for seamless integration of Jupyter-based research environments and research data repositories.This extension is composed of a Python package namedjupyterfairfor the server extension and a NPM package namedjupyterfairfor the frontend extension.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstalljupyterfairUninstallTo remove the extension, execute:pipuninstalljupyterfairTroubleshootIf you are seeing the frontend extension, but it is not working, check
that the server extension is enabled:jupyterserverextensionlistIf the server extension is installed and enabled, but you are not seeing
the frontend extension, check the frontend extension is installed:jupyterlabextensionlistContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the jupyterfair directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Server extension must be manually installed in develop modejupyterserverextensionenablejupyterfair# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstall# Server extension must be manually disabled in develop modejupyterserverextensiondisablejupyterfair
pipuninstalljupyterfairIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedjupyterfairwithin that folder.Packaging the extensionSeeRELEASE
Package: jupyter-fdc3-widgets
jupyter_fdc3_widgetsFDC3 enabled Jupyter WidgetsInstallationYou can install usingpip:pipinstalljupyter_fdc3_widgetsIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]jupyter_fdc3_widgetsAboutThis project was bootstrapped using the FINOS Software Project Blueprint.Project blueprint is a GitHub repository template for allFintech Open Source Foundation (FINOS)hosted GitHub repositories, contributed and maintained by FINOS as part of theOpen Developer Platform (ODP)initiative.LicenseCopyright 2023 Fintech Open Source FoundationDistributed under theApache License, Version 2.0.SPDX-License-Identifier:Apache-2.0
Package: jupyter-flex
Documentation·InstallationBuild dashboards using Jupyter NotebooksUse Markdown headers and Jupyter Notebook cell tags to define the dashboard layout and its componentsFlexible and easy way to specify row and column based layoutsUsenbconvertto create static reportsUseVoilato start a live Jupyter Kernel for fully dynamic applicationsSupport forJupyter widgetsInstallationpipinstalljupyter-flexDocumentationLearn more in thejupyter-flex documentation.ContributingSeeCONTRIBUTING.md.
Package: jupyterflow
JupyterFlowRun your workflow on JupyterHub!What is JupyterFlow?RunArgo WorkflowonJupyterHubwith single command.No Kubernetes knowledge (YAML) needed to run.No container image build & push or deploy.Just simply run your workflow with single commandjupyterflow.jupyterflowis a single command that helps user utilize Argo workflow engine without making any YAML files or building containers on JupyterHub. The followingjupyterflowcommand will make sequence workflow for you. That's it!jupyterflowrun-c"python hello.py >> python world.py"To make parallel workflow, write your ownworkflow.yamlfile.Problem to solveI wanted to train multiple ML models efficiently.Using Kubernetes was a good idea, sinceit is easy to make distributed jobs.it is easy to schedule ML jobs on multiple training server.it has native resource management mechanism.it has good monitoring system.But there were some drawbacks.I needed to re-build & re-push image everytime I updated my model. This was painful.People who were not familiar with k8s had a hard time using this method.JupyterFlow aims to solve this problem. Run your workflow  on JupyterHub with single command without Kubernetes & container troublesome task.LimitationJupyterFlow only works on JupyterHub deployed on Kubernetes.JupyterHub for KubernetesKubeflowTherefore, although using JupyterFlow does not require Kubernetes knowledge, setting up JupyterFlow requires Kubernetes understandings(YAML,helm,Service). If you're familiar with Kubernetes, it will not be too hard.Getting StartedTo set upjupyterflowand start running your first workflow, follow theGetting Startedguide.How does it workTo learn how it works, go toHow it worksguide.ExamplesFor examples how to use, please seeExamplespage.ConfigurationTo find out more configuration, take a look atConfigurationpage.CLI ReferenceFor more detail usage ofjupyterflowcommand line interface, find out more atCLI Referencepage.
Package: jupyter-formily
jupyter-formilyUseformilyin Jupyter.Installationpipinstalljupyter-formilyUsagefromjupyter_formilyimportFormilyexample=Formily(schema,options,default_value)example.display()schemacan be constructed withFormily Designeror crafted manually.optionsallows you to tune the form to fit you need.default_valueallows you to set initial value which follow schema.optionsis dict, and key as follow:keyTypeDefault valueshow_modalboolTrueok_labelstring"OK"cancel_labelstring"Cancel"ok_propsISubmitProps{}cancel_propsButtonProps{}form_propsIFormLayoutProps{}modal_propsModalProps{}Checkdemo.ipynbfor more information.Custom ComponentsCurrently custom can not be used via Formily Designer, you have to add them manually.
A trick to use custom components is to add a similar component in desinger as placeholder.
And then replace it to the component you want by patching the generated schema.File PickerSelect file path interactively.{"type":"string","name":"input_file","title":"Input File","x-decorator":"FormItem","x-component":"FilePicker","x-validator":[],"x-component-props":{"init_path":"./"},"x-decorator-props":{},"x-designable-id":"8j01zeibhn3","x-index":1}DevelopmentSetupYou need to install dependencies both python and javascript. Please make sure you have installedpoetryandyarn.# pythonpoetryinstall# javascriptcdvendor/formily&&yarnStart Servercdvendor/formily&&npmrundevFormilyThen you can check widget indemo.ipynb.Build./build.sh
Package: jupyter-forward
jupyter-forwardCIDocsPackageLicenseOverviewJupyter-forward simplifies the process of runningjupyter labon a remote machine by performing the following tasks on behalf of the users:Logging into a remote cluster/resource via the SSH protocol.Launching Jupyter Lab on the remote cluster.Port forwarding Jupyter Lab session back to your local machine.Opening the port forwarded Jupyter Lab session in your local browser.InstallationJupyter-forward can be installed from PyPI with pip:python-mpipinstalljupyter-forwardJupyter-forward is also available from conda-forge for conda installations:condainstall-cconda-forgejupyter-forwardSeedocumentationfor more information.
Package: jupyter-functions
🪐 ⏏️ Jupyter Functionsyarn
yarnbuild# open http://localhost:3063# open http://localhost:8888/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnstartpipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist# open http://localhost:8888/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnjupyterlab
Package: jupyter-geppetto
Geppetto Jupyter Notebook ExtensionExperimental Jupyter notebook extension. This
extension extends Jupyter Python server based on tornado that allows the
client to establish a websocket connection and server static resources
but there is no real functionality beyond that.Installationpipinstalljupyter_geppettojupyternbextensionenable--py--sys-prefixjupyter_geppettoFor further information please visit:https://github.com/openworm/org.geppetto.frontend.jupyter
Package: jupyter-git
Follow steps onhttps://packaging.python.org/en/latest/distributing/#requirements-for-packaging-and-distributingOne time things:Make account on PyPIpip install twineTo Update Package:Update version number in setup.pyrm dist/*python setup.py sdistUse the form on the PyPI website, to upload your PKG-INFO info located in your local project tree at myproject.egg-info   /PKG-INFO. If you don’t have that file or directory, then run python setup.py egg_info to have it generated.twine upload dist/*
Package: jupyter-git-haitham
Follow steps onhttps://packaging.python.org/en/latest/distributing/#requirements-for-packaging-and-distributingOne time things:Make account on PyPIpip install twineTo Update Package:Update version number in setup.pyrm dist/*python setup.py sdistUse the form on the PyPI website, to upload your
PKG-INFO info located in your local
project tree at myproject.egg-info   /PKG-INFO.
If you don’t have that file or directory, then run
python setup.py egg_info to have it generated.twine upload dist/*
Package: jupyter-hdfscontents
===========================================HDFS Contents Manager for Jupyter Notebooks===========================================A contents manager for Jupyter that stores files in Hadoop File System (HDFS)Install-------1) Install HDFS3_.2) Clone this repository then pip install .Run----You can use command line arguments to set HDFS related configurations::jupyter-notebook --NotebookApp.contents_manager_class='hdfscontents.hdfsmanager.HDFSContentsManager' \--HDFSContentsManager.hdfs_namenode_host='localhost' \--HDFSContentsManager.hdfs_namenode_port=9000 \--HDFSContentsManager.root_dir='/user/centos/'.. code: bashAlternatively, first run:::jupyter-notebook --generate-configto generate a default config file. Edit and add the HDFS related configurations in the generated file. Then start the notebook server.::jupyter-notebook.. _HDFS3: https://hdfs3.readthedocs.io/en/latest/install.html