Saravanan666/pypi-org huggingface dataset:
Package: jupyter-content
🪐 💼 Jupyter ContentJupyter Content.Jupyter Content allows Users to create, access and share code files (Notebooks, Markdown...) and datasets persisted in local drives, distributed file systems or cloud buckets.Developpipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist
yarnjupyterlab
Package: jupytercontrib
jupyter contribSome small utilities that are related to Jupyter.This is not endorsed by the Jupyter team, and not part of the official Jupyter project.Feel free to submit any patches, or utility modules for which a full packages
might be inappropriate or too much of an hassle.installationpip install jupytercontribExtensionsAny extension present here should not modify Jupyter/IPython behavior at install time.
You can provide a tool to simplify activation, but installation should likely be without side-effects.What can I find here:2to3 for notebooksAn extra nbconvert exporter that convert Legacy Python 2 notebook to New Awesome and useful Python 3
It requiresnbconvertmaster.It uses [entry points]  to directly expose a--to=2to3when usingjupyter nbconvert, thus you can do thing likejupyter nbconvert --to 2to3 <mynotebook.ipynb>This will create a file namemynotebook.nbconvert.ipynbwhich each cell converted to Python 3.Markdown exporter w/ outputExtension of the standard notebook-to-markdown exporter that tags output cells asoutput.
Can be used, for example, when building Sphinx docs to style output differently from input code blocks.jupyter nbconvert --to mdoutput --output=<output.md> <mynotebook.ipynb>
Package: jupyter-contrib-nbextensions
Contains a collection of extensions that add functionality to the Jupyter
notebook. These extensions are mostly written in Javascript, and are loaded
locally in the browser.Readthe documentationfor more information.Thejupyter-contrib repositoryis maintained independently by a group of users and developers, and is not
officially related to the Jupyter development team.The maturity of the provided extensions varies, so please checkthe repository issues pageif you encounter any problems, and create a new issue if needed!
Package: jupyter-conveyor
ConveyorCompute and use Jupyter notebook cell outputs in other notebooks and scripts with just a couple lines of code.Use CasesSplit ordered steps across multiple notebooks, picking up the most recent state information with each new notebookPrototype and organize workflows entirely in Jupyter notebooks without having to manage script exportsImprove performance by reducing overheads from notebook servers and unused code cellsRequirementsConveyor currently only supports Jupyter notebooks written in Python. It is important to ensure that you have the version of Python (and any dependencies) used in your notebooks also installed locally, or on the machine using this library.ExamplesFor quick and simple access to values in a prior notebook:conveyor.run_notebook("conveyor/examples/Sample Calculations I.ipynb",import_globals=True)# all notebook globals pushed to conveyor.nbglobals with import_globals flagfromconveyor.nbglobalsimportx,y,z,figprint(y)Conveyor can also provide you with any information available in a prior Jupyter workspace.results=conveyor.run_notebook("conveyor/examples/Sample Calculations I.ipynb")# Cells are zero-indexed, only code cells are countedcode_cell_idx=1# Get a cell's source codecode=results[code_cell_idx]['code']# Get a cell resultcell_result=results[code_cell_idx]['result']# Get cell stdoutcell_stdout=results[code_cell_idx]['stdout']# To get any variable available in notebookx=results.getvar('x')There are options for running notebooks that can be used to optimize notebook execution, or stitch notebooks together inpipelines.fromconveyor.multinbimportPipeline...data_processing=Pipeline()data_processing.add_notebook(filename="conveyor/examples/load_data.ipynb",carry_vars=['df'])data_processing.add_notebook(filename="conveyor/examples/process_data.ipynb",carry_vars=['magic_number'],start_cell_idx=3)# Add custom intermediary stepsdeftransform_magic(from_state):to_state=dict()to_state['transformed_magic_number']=-1*from_state['magic_number']returnto_statedata_processing.add_transform(transform_magic)# Get output of selected variables from each stageresults=data_processing.run()InstallationThis package is available on pypi. Install it using pip withpip install jupyter-conveyorConveyor is only compatible with Python 3.5 and above.DocumentationSee the docs folder, orread them here.
Package: jupytercor
JupytercorJupytercor est un package python qui permet de convertir les cellules markdown d'un notebook jupyter avec pandoc.Installation et utilisationPour installer jupytercor, vous devez avoir python3 et pandoc installés sur votre machine.Vous pouvez ensuite installer jupytercor avec pip:pipinstall--upgradejupytercorPour utiliser jupytercor, vous devez exécuter le script jupytercor.py avec la commande suivante:jupytercorinput.ipynb[-ooutput.ipynb][--clean][--toFORMAT][--images]Où:input.ipynbest le nom du fichier notebook d'entrée à convertir-o output.ipynbest une option qui permet de spécifier le nom du fichier notebook de sortie (par défaut c'est output.ipynb)--to FORMATest une option pour préciser le format de sortie.--to latexpour convertir en LaTeX.--to pdfpour convertir en PDF.--cleanest une option qui permet d'effectuer les conversions avec pandoc (par défaut c'est False)--imagesest une option qui permet de télécharger les images distantes dans un dossierimages(par défaut c'est False)Fonctionnalités et optionsJupytercor offre les fonctionnalités et options suivantes:Il lit un fichier notebook au format ipynb et en extrait les cellules markdownIl transforme chaque cellule markdown en html avec pandoc en utilisant l'option-f markdown -t htmlIl transforme chaque cellule html en markdown avec pandoc en utilisant l'option-f html -t gfm-raw_htmlIl remplace le contenu des cellules markdown par le texte transforméIl écrit un nouveau fichier notebook au format ipynb avec les cellules convertiesIl permet à l'utilisateur de choisir le nom du fichier notebook d'entrée et celui du fichier notebook de sortieIl permet à l'utilisateur d'activer ou non les conversions avec pandoc grâce au drapeau--cleanIl permet à l'utilisateur de télécharger les images distantes avec une url grâce au drapeau--imagesLicence et créditsJupytercor est distribué sous la licence MIT.Jupytercor utilise nbformat pour lire et écrire des fichiers notebooks.Jupytercor utilise subprocess pour exécuter des commandes pandoc.Jupytercor utilise argparse pour analyser les arguments passés au script.Jupytercor s'inspire du code trouvé sur cette page web:https://beautiful-soup-4.readthedocs.io/en/latest/#searching-the-tree
Package: jupyter-cpp-kernel
:warning:For C/C++ header add-on development,try this templateSecurity issue with Jupyter NotebooksC++ 14 kernel for JupyterInstallation:warning:If you want to use it on Windows, please installing theGNU Compiler Collection for WindowsNormally, your target machine must meet these requirement packages before installing and usingjupyter-cpp-kernel.g++python3,python3-pipjupyter(recommendjupyterlab)Install from PyPI:warning:For Windows User: Please followthis instructionto install GCC properly.pipinstalljupyter-cpp-kernelInstall from GitHub repo:warning:For Windows User: Please followthis instructionto install GCC properly.pipinstallgit+https://github.com/shiroinekotfs/jupyter-cpp-kernel.gitContributingYou can clone, create a fork or import this repo whenever you want.Please follow the GitHub standards and the licenseGuides (notebook)See more athere
Package: jupyter-cron
Simple clock/cron process that monitors a specific directory and run jobs based on its filename.Currently filename with this pattern is supported:(every X at time) whereXis [‘day’, ‘monday’, ‘tuesday’, ‘wednesday’,’thursday’, ‘friday’,’saturday’,’sunday’]timeis either in short form without minutes like 5PM or long form with minutes, 5:00PMFor example:notebooks/generate_model (every day at 5pm).ipynbwill triggerjupyter nbconvertto run every day at 5pm.scripts/gen (every monday at 12pm).pywill triggerpython scripts/gen….pyto run.FeaturesSimple to useIntegrates well with JupyterTested on Python 3.6Usageusage:jupyter-cron[-h][-d]globScansforfiletorunonaschedulebasedonitsnamepositionalarguments:globspecifyglobtosearcheg.test/**/*.ipynboptionalarguments:-h,--helpshowthishelpmessageandexit-d,--daemonizedaemonizetheprocessMetaQuoc Le -@realQuoc-quocble@gmail.comDistributed under the MIT license. SeeLICENSE.txtfor more information.https://github.com/quocble/jupyter-cronhttp://rst.ninjs.org/
Package: jupyter-ctrl
Daemon, which ensures, that the Jupyter notebooks server is started only once, no metter how often or where from you open a notebook.
No root privileges required.Besides of Jupyter, the package requires Python 2, Pyro and theargparsemodule.
It was successfully tested with version 3.14 of Pyro and is known not to work with Pyro 4.
On Ubuntu 14.04, the dependencies are satisfied by issuing the following command:sudo apt-get install pyroInstallationFetch the latest version:git clone https://github.com/kostrykin/jupyter-ctrl.gitInstall as a user:cd jupyter-ctrl
python2 setup.py install --userEnsure that~/.local/binis listed in your$PATHvariable:echo 'export PATH="$PATH:~/.local/bin"' >> ~/.bashrcUsageAdd the following command to the list of commands to be run on start-up by your desktop manager:jupyter-daemon.pyOn most Ubuntu-based systems, this is accomplished through thegnome-session-propertiesapplication.Run the following command to open-up a notebook (e.g.Unnamed.ipynbin your working directory) in your favorite web browser:xdg-open `jupyter-view.py Unnamed.ipynb`It’s convenient to set this to be the default “application” for opening up.ipynbfiles.Alternatively, you can also use thejupyter-openbash script for convenience, which does the same:jupyter-open Unnamed.ipynbNote that file managers, which support.desktopfiles, should offerJupyter Notebookas a method of opening files after installing this package.
It was tested with Pantheon files 0.2.4.Status Bar IndicatorAlongside ofjupyter-daemon.py, you may also want to putjupyter-indicator.pyon your list of automatically started desktop applications, which is a highly rudimentary user interface for the daemon:Use the--iconparameter to adjust the appearance e.g. for bright or dark themes.
The two iconsjupyter-brightandjupyter-darkare installed by default, but you may also use any icon you like.
Package: jupyter-cv-utils
Jupyter CV UtilsThis package contains utility functions to make life easier when using Jupyter notebooks.InstallationWith pip:pip install jupyter_cv_utilsEditable mode to develop and run tests, run the following in your virtualenv:# Clone the repository.
git clone https://github.com/saratrajput/jupyter_cv_utils.git

cd jupyter_cv_utils/
pip install -e .[dev]UsageImport package.import jupyter_cv_utils as jutilsInstall package.jutils.install("numpy")Show image in matplotlib fashion.jutils.imshow("numpy")Show image in OpenCV window.jutils.imshow_opencv("numpy")Reload a module.jutils.reload(<module_name>)Segment image based on color.lower_hsv = (Hue, Saturation, Value)
upper_hsv = (Hue, Saturation, Value)
jutils.segment_color(img, lower_hsv, upper_hsv)Hue is in the range 0 ~ 180.
Saturation and Value are in the range 0 ~ 255.
Package: jupyterd3
### JupyterD3
Attempt to bring some nice d3 plots and libraries to Jupyter notebook.Simple wrapper on the d3 code and input is pandas Dataframes.D3 plots supported:
- [Parallel Coordinates](https://syntagmatic.github.io/parallel-coordinates/)
- [Time Series](http://mcaule.github.io/d3-timeseries/)
- [Time Data Availability Chart](https://github.com/flrs/visavail)
Package: jupyter-dash
Jupyter DashThis library makes it easy to develop Plotly Dash apps interactively from within Jupyter environments (e.g. classic Notebook, JupyterLab, Visual Studio Code notebooks, nteract, PyCharm notebooks, etc.).See thenotebooks/getting_started.ipynbfor more information and example usage.InstallationYou can install the JupyterDash Python package using pip...$ pip install jupyter-dashor conda$ conda install -c conda-forge -c plotly jupyter-dashJupyterLab supportWhen used in JupyterLab, JupyterDash depends on thejupyterlab-dashJupyterLab extension, which requires JupyterLab version 2.0 or above.This extension is included with the Python package, but in order to activate it JupyterLab must be rebuilt. JupyterLab should automatically produce a popup dialog asking for permission to rebuild, but the rebuild can also be performed manually from the command line using:$ jupyter lab buildTo check that the extension is installed properly, calljupyter labextension list.Colab supportAs of version 0.3.0,JupyterDashworks in Colab with no additional configuration. Just installjupyter-dashusing pip in a Colab notebook cell!pip install jupyter-dashFeaturesTo learn more about the features of JupyterDash, check out theannouncement post.DevelopmentTo develop JupyterDash, first create and activate a virtual environment using virtualenv or conda.Then clone the repository and change directory to the repository root:$ git clone https://github.com/plotly/jupyter-dash.git
$ cd jupyter-dashThen install the dependencies:$ pip install -r requirements.txt -r requirements-dev.txtThen install the Python package in editable mode. Note: this will requirenodejsto be installed.$ pip install -e .Then install the classic notebook extension in development mode:$ jupyter nbextension install --sys-prefix --symlink --py jupyter_dash
$ jupyter nbextension enable --py jupyter_dashThen install the JupyterLab extension in development mode:$ jupyter labextension link extensions/jupyterlabFor release, build the JupyterLab extension to bundle with the Python package (seeRELEASE.mdfor the full process):$ python setup.py build_js
Package: jupyter-dashboard
🪐 🏄 Jupyter DashboardCreate and publish Dashboard from Jupyter.yarn#  "installConfig": {#    "hoistingLimits": "workspaces"#  },yarnbuild# open http://localhost:3063# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnstartpipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnjupyterlab
Package: jupyter-dash-mp
Jupyter Dash - MultiprocessingThis is a fork of Jupyter Dash that enables servers to start and stop easier.This library makes it easy to develop Plotly Dash apps interactively from within Jupyter environments (e.g. classic Notebook, JupyterLab, Visual Studio Code notebooks, nteract, PyCharm notebooks, etc.).See thenotebooks/getting_started.ipynbfor more information and example usage.InstallationYou can install the JupyterDash Python package using pip...$ pip install jupyter-dashor conda$ conda install -c conda-forge -c plotly jupyter-dashJupyterLab supportWhen used in JupyterLab, JupyterDash depends on thejupyterlab-dashJupyterLab extension, which requires JupyterLab version 2.0 or above.This extension is included with the Python package, but in order to activate it JupyterLab must be rebuilt. JupyterLab should automatically produce a popup dialog asking for permission to rebuild, but the rebuild can also be performed manually from the command line using:$ jupyter lab buildTo check that the extension is installed properly, calljupyter labextension list.Colab supportAs of version 0.3.0,JupyterDashworks in Colab with no additional configuration. Just installjupyter-dashusing pip in a Colab notebook cell!pip install jupyter-dashFeaturesTo learn more about the features of JupyterDash, check out theannouncement post.DevelopmentTo develop JupyterDash, first create and activate a virtual environment using virtualenv or conda.Then clone the repository and change directory to the repository root:$ git clone https://github.com/plotly/jupyter-dash.git
$ cd jupyter-dashThen install the dependencies:$ pip install -r requirements.txt -r requirements-dev.txtThen install the Python package in editable mode. Note: this will requirenodejsto be installed.$ pip install -e .Then install the classic notebook extension in development mode:$ jupyter nbextension install --sys-prefix --symlink --py jupyter_dash
$ jupyter nbextension enable --py jupyter_dashThen install the JupyterLab extension in development mode:$ jupyter labextension link extensions/jupyterlabFor release, build the JupyterLab extension to bundle with the Python package:$ python setup.py build_js
Package: jupyter-datasette-proxy
jupyter-datasette-proxyUsingdatasettein JupyterHub or Binder withjupyter-server-proxyLaunch into notebook interface (rundatasettefrom the notebookNewmenu):Launch directly into datasette:Thedatasetteport is assigned dynamically, although a known port could be specified if datasette is used to provide an API.InstallationYou can install from PyPI:pipinstalljupyter-datasette-proxyThis will also bring in datasette.
Package: jupyter-datatables
Jupyter DataTablesJupyter Notebook extension to levarage pandas DataFrames by integrating DataTables JS.AboutData scientists and in fact many developers work withpd.DataFrameon daily basis to interpret data to process them. In my typical workflow. The common workflow is to display the dataframe, take a look at the data schema and then produce multiple plots to check the distribution of the data to have a clearer picture, perhaps search some data in the table, etc...What if those distribution plots were part of the standard DataFrame and we had the ability to quickly search through the table with minimal effort? What if it was the default representation?The jupyter-datatables usesjupyter-requireto draw the table.Installationpipinstalljupyter-datatablesUsageimportnumpyasnpimportpandasaspdfromjupyter_datatablesimportinit_datatables_modeinit_datatables_mode()That's it, your default pandas representation will now use Jupyter DataTables!df=pd.DataFrame(np.abs(np.random.randn(50,5)),columns=list(string.ascii_uppercase[:5]))In most cases, you don't need to worry too much about the size of your data. Jupyter DataTablescalculates required sample sizebased on a confidence interval (by default this would be0.95) and margin of error and ceils it to the highest 'smart' value.For example, for a data containing100,000samples, given0.975confidence interval and0.02margin of error, the Jupyter DataTables would calculate that3044samples are required and it would round it up to4000.With additional note:Sample size: 4,000 out of 100,000We can also handle wide tables with ease.df=pd.DataFrame(np.abs(np.random.randn(50,20)),columns=list(string.ascii_uppercase[:20]))As per 0.3.0, there is a support forinteractive tooltips:And also support for custom indices includingDatetype:dft=pd.DataFrame({'A':np.random.rand(5),'B':[1,1,3,2,1],'C':'This is a very long sentence that should automatically be trimmed','D':[pd.Timestamp('20010101'),pd.Timestamp('20010102'),pd.Timestamp('20010103'),pd.Timestamp('20010104'),pd.Timestamp('20010105')],'E':pd.Series([1.0]*5).astype('float32'),'F':[False,True,False,False,True],})dft.D=dft.D.apply(pd.to_datetime)dft.set_index('D',inplace=True)Current status and future plans:Check out theProject Boardwhere we track issues and TODOs for our Jupyter tooling!Author: Marek Cermakmacermak@redhat.com, @AICoE
Package: jupyter-datsette-proxy
jupyter-datasette-proxyUsingdatasettein JupyterHub or Binder withjupyter-server-proxyLaunch into notebook interface (rundatasettefrom the notebookNewmenu):Launch directly into datasette:Thedatasetteport is assigned dynamically, although a known port could be specified if datasette is used to provide an API.InstallationYou can install from PyPI:pipinstalljupyter-datasette-proxyThis will also bring in datasette.
Package: jupyterdevview
Jupyter Dev View contains a style sheet that splits the jupyter output into a side by side view of code cells on the left, and output on the right.
In addition to the stylesheet this package provides two functions to easily switch between the Dev View and the traditional notebook view.:import jupyterdevview as jdv

# Switch to dev view
jdv.dev_view()
# Refresh your browser (ctrl-F5 for most browsers)

# Switch back to notebook view
jdv.notebook_view()
# Refresh your browser (ctrl-F5 for most browsers)NOTE: You will need to restart jupyter after the first time you run jdv.dev_view() for the changes to take effect.  Subsequently you will just need to refresh your browser
Package: jupyter-disable-upload
disable_uploada jlab extension to disable uploadsRequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstalldisable_uploadUninstallTo remove the extension, execute:pipuninstalldisable_uploadContributingDevelopment 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 disable_upload directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# 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 uninstallpipuninstalldisable_uploadIn 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 nameddisable_uploadwithin that folder.Packaging the extensionSeeRELEASE
Package: jupyter-disqus
You may use this package to inject and displayDisqusin your jupyter notebook.DemoInstallation$ pip install jupyter_disqusUsagefromjupyter_disqusimportinject# make sure to run this in a cell of your jupyter notebookinject(page_url="https://costahuang.me/SC2AI/",page_identifier="1f527ae5-5a59-4dc3-9bb0-d77c2ccf5cab",# unique identifiersite_shortname="costahuang"# your disqus site shortname)Authors: Costa Huang
Package: jupyter-docker
🪐 🐳 Jupyter DockerManage Docker from Jupyter.For MacOShttps://github.com/docker/docker-py/issues/3059https://github.com/gh640/wait-for-docker/issues/12Allow the default Docker socket to be used (requires password)pipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist
yarnjupyterlab