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Jupyter

Multi-language interactive computing environments.
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What is Jupyter?

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.
Jupyter is a tool in the Data Science Notebooks category of a tech stack.
Jupyter is an open source tool with 7.2K GitHub stars and 3K GitHub forks. Here鈥檚 a link to Jupyter's open source repository on GitHub

Who uses Jupyter?

Companies
154 companies reportedly use Jupyter in their tech stacks, including Ruangguru, Delivery Hero, and trivago.

Developers
565 developers on StackShare have stated that they use Jupyter.

Jupyter Integrations

GitHub, Python, Dropbox, Scala, and TensorFlow are some of the popular tools that integrate with Jupyter. Here's a list of all 17 tools that integrate with Jupyter.
Private Decisions at about Jupyter

Here are some stack decisions, common use cases and reviews by members of with Jupyter in their tech stack.

Mark Melnykowycz
Mark Melnykowycz
Artist - Scientist - Wearable Computing at idezo GmbH | 1 upvotes 8K views
Shared insights
on
JupyterJupyter

When integrated with cloud platforms it's an easy way to code and test data science projects. The web-based nature makes it easy to transition between coding on your local machine or the cloud. Jupyter

See more
Shared insights
on
JupyterJupyter

Code and results + Markdown all in a shareable web page. Jupyter

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Shared insights
on
JupyterJupyterG SuiteG Suite

I use Jupyter and JupyterLab specifically because it's such a great interface for Python, SQL, data exploration, and data viz. Documentation is built into the design of a notebook, and it's commonly used enough in people's workloads that not much educating is required.

I know some on G Suite like using CoLab, but it runs pretty slow and disconnects runtimes often.

Here's a fun little example to make it feel more real Jupyter/CoLab Example

See more
Guillaume Simler
Guillaume Simler
at Velchanos.io | 4 upvotes 180.1K views

Jupyter Anaconda Pandas IPython

A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

See more
Public Decisions about Jupyter

Here are some stack decisions, common use cases and reviews by companies and developers who chose Jupyter in their tech stack.

Guillaume Simler
Guillaume Simler
at Velchanos.io | 4 upvotes 180.1K views

Jupyter Anaconda Pandas IPython

A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

See more
Mark Melnykowycz
Mark Melnykowycz
Artist - Scientist - Wearable Computing at idezo GmbH | 1 upvotes 8K views
Shared insights
on
JupyterJupyter

When integrated with cloud platforms it's an easy way to code and test data science projects. The web-based nature makes it easy to transition between coding on your local machine or the cloud. Jupyter

See more
Shared insights
on
JupyterJupyterG SuiteG Suite

I use Jupyter and JupyterLab specifically because it's such a great interface for Python, SQL, data exploration, and data viz. Documentation is built into the design of a notebook, and it's commonly used enough in people's workloads that not much educating is required.

I know some on G Suite like using CoLab, but it runs pretty slow and disconnects runtimes often.

Here's a fun little example to make it feel more real Jupyter/CoLab Example

See more

Jupyter Alternatives & Comparisons

What are some alternatives to Jupyter?
Apache Zeppelin
A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.
PyCharm
PyCharm鈥檚 smart code editor provides first-class support for Python, JavaScript, CoffeeScript, TypeScript, CSS, popular template languages and more. Take advantage of language-aware code completion, error detection, and on-the-fly code fixes!
IPython
It provides a rich architecture for interactive computing with a powerful interactive shell, a kernel for Jupyter. It has a support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing.
Spyder
It is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.
Anaconda
A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
See all alternatives

Jupyter's Followers
656 developers follow Jupyter to keep up with related blogs and decisions.
Felix Zhou
gs-findev-osx
starqiu
Charles Achilefu
arasiappalam
Yannis Pappas
Torsten Palenschat
Devesh Tarasia
Jose Henrique Luckmann
Carlos Irigoyen