Displaying 1 to 20 from 27 results

beakerx - Beaker Extensions for Jupyter Notebook

  •    Java

BeakerX is a collection of JVM kernels and interactive widgets for plotting, tables, autotranslation, and other extensions to Jupyter Notebook. BeakerX is in beta and under active development. The documentation consists of tutorial notebooks on GitHub. You can try it in the cloud for free with Binder. And here is the cheatsheet.

docker-stacks - Ready-to-run Docker images containing Jupyter applications

  •    Dockerfile

Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools. The two examples below may help you get started if you have Docker installed know which Docker image you want to use, and want to launch a single Jupyter Notebook server in a container.

nteract - 📘 The interactive computing suite for you! ✨

  •    Javascript

nteract is first and foremost a dynamic tool to give you flexibility when writing code, exploring data, and authoring text to share insights about the data. Edit code, write prose, and visualize.

geonotebook - A Jupyter notebook extension for geospatial visualization and analysis

  •    Python

GeoNotebook is an application that provides client/server environment with interactive visualization and analysis capabilities using Jupyter, GeoJS and other open source tools. Jointly developed by Kitware and NASA Ames. Documentation for GeoNotebook can be found at http://geonotebook.readthedocs.io.

IPython - Interactive Computing

  •    Python

IPython provides a rich toolkit to help you make the most of using Python interactively. It provides a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends.

sparkmagic - Jupyter magics and kernels for working with remote Spark clusters

  •    Python

Sparkmagic is a set of tools for interactively working with remote Spark clusters through Livy, a Spark REST server, in Jupyter notebooks. The Sparkmagic project includes a set of magics for interactively running Spark code in multiple languages, as well as some kernels that you can use to turn Jupyter into an integrated Spark environment. There are two ways to use sparkmagic. Head over to the examples section for a demonstration on how to use both models of execution.

data-science-your-way - Ways of doing Data Science Engineering and Machine Learning in R and Python

  •    Jupyter

These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.

plotly-notebook-js - A package for using plotly in Tonicdev and Jupyter notebooks.

  •    Javascript

Using plotly with Jupyter requires installing the IJavascript kernel for Jupyter, then requiring the plotly-notebook-js code. You will need to be using a notebook running from the same directory as where plotly-notebook-js is installed. Using the IJavascript kernel, $$html$$ is a global variable that will output html.

spark-r-notebooks - R on Apache Spark (SparkR) tutorials for Big Data analysis and Machine Learning as IPython / Jupyter notebooks

  •    Jupyter

This is a collection of Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the R language. If your are interested in being introduced to some basic Data Science Engineering concepts and applications, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R. Additionally, if you are interested in using Python with Spark, you can have a look at our pySpark notebooks.


  •    Javascript

TODO: add screenshots. ThebeLab is a based on the Jupyter technology, and thus supports a wealth of programming languages. The original implementation, called Thebe was a fork of the Jupyter code base. ThebeLab is a reimplementation of Thebe as a thin layer on top of JupyterLab, making it more sustainable.

nbformatjs - JS implementation of the jupyter notebook format

  •    Jupyter

If you just want the schemas for v3 and v4, check out nbschema instead.

forward - Sherlock Port Forwarding Utility

  •    Shell

Forward sets up an sbatch script on your cluster resource and port forwards it back to your local machine! Useful for jupyter notebook and tensorboard, amongst other things. The folder sbatches contains scripts, organized by cluster resource, that are intended for use and submission. It's up to you to decide if you want a port forwarded (e.g., for a jupyter notebook) or just an instruction for how to connect to a running node with your application.

jgscm - Jupyter support for Google Cloud Storage

  •    Python

Jupyter Google Storage Contents Manager allows working with Jupyter notebooks directly in Google Cloud Storage. It aims to be a complete drop-in replacement for the stock filesystem ContentsManager. Thus JGSCM is only compatible with a relatively modern IPython/Jupyter stack (version 4 and above). The root level of the virtual file system is the list of buckets, which are presented as directories. In turn, each bucket is presented as an ordinary folder where users can create files, subdirectories and notebooks. Besides, snapshots are completely supported too.

nbformat - Reference implementation of the Jupyter Notebook format

  •    Python

nbformat contains the reference implementation of the Jupyter Notebook format, and Python APIs for working with notebooks. There is also a JSON schema for notebook format versions >= 3.

commuter - 🚎 Migrated to nteract/nteract

  •    Javascript

/kəˈmyo͞odər/ a person who travels some distance to work on a regular basis. Like commuters, our data travels around too. Sometimes we need a notebook at work and other times at a client's site. Wherever and whenever you need your notebooks, commuter has you covered.

docs - 🏖 User written and user focused documentation for working with nteract! Join us!

  •    HTML

nteract is an ecosystem of React components, JavaScript packages, and applications built on top of the Jupyter specification that enhances interactive computing and data science workflows.

react-jupyter-display-area - :bar_chart: Jupyter Display Area as a React Component

  •    Javascript

Render Jupyter notebook outputs in a trim little React component. ⚠️ This package has been deprecated in favor of @nteract/display-area.

molecular-design-toolkit - Notebook-integrated tools for molecular simulation and visualization

  •    Python

Molecular modeling without the pain - a Python library offering integrated simulation, visualization, analysis, and cloud computing. The toolkit aims to lower the barriers between you and your science by integrating mature, open source simulation packages with a readable abstract API, Jupyter notebook visualization, and native cloud computing.

juniper - 🍇 Edit and execute code snippets in the browser using Jupyter kernels

  •    Javascript

Juniper is a lightweight JavaScript library for adding interactive, editable and runnable code snippets to any website. It uses JupyterLab components and Binder (or your own self-hosted version of BinderHub) to launch Python, R or Julia environments based on a GitHub repository and an auto-built Jupyter-enabled Docker image. This project was heavily inspired by Min RK's Thebelab package – thanks for the great work on this. It was also instrumental in helping me understand how JupyterLab works under the hood. Also thanks to Binder for making their great service available and allowing such a smooth integration.