Jupyter

Jupyter is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It is a popular tool for data analysis, scientific computing, and machine learning, and is widely used in academic research, industry, and data science education.

Jupyter gets its name from Julia-Python-and-R - some of the original programming languages supported.

Jupyter provides the following features.

Interactive Computing

Jupyter notebooks allow users to write and execute code interactively, providing an interactive computing environment. This allows users to explore data, prototype algorithms, and create visualizations in a single, cohesive environment.

Multiple Language Support

Jupyter supports multiple programming languages, including Python, R, and Julia. This makes it easy to integrate different tools and frameworks and collaborate with colleagues who use different programming languages.

Collaboration

Jupyter notebooks can be shared with others, allowing for easy collaboration and reproducibility of analyses. This also facilitates communication and knowledge sharing among team members and stakeholders.

Visualization

Jupyter notebooks support interactive visualization libraries such as Matplotlib, Bokeh, and Plotly, making it easy to create and share data visualizations.

Integration

Jupyter notebooks can be integrated with other tools and frameworks such as Git, GitHub, and Docker. This makes it easy to manage version control, share code and data, and deploy projects.

Ecosystem

Jupyter has a rich ecosystem of tools and services, such as JupyterLab, JupyterHub, and Binder, that can help streamline the development and deployment process. Many third-party tools and plugins also integrate with Jupyter to extend its functionality.

Jupyter Installation

The installation process for Jupyter depends on your operating system and your preferred installation method. Follow the instructions below based on your platform.

Jupyter Ecosystem

Here’s a short guide to clarify some of the terms used with Jupyter.

  • JupyterLab: An interactive development environment (IDE) for working with Jupyter notebooks, code, and data. It provides a flexible and powerful user interface that can be customized to suit the needs of individual users.

  • Jupyter Notebook: A web-based interactive computational environment for creating and sharing Jupyter notebooks, which allow you to create and share documents that contain live code, equations, visualizations, and narrative text.

  • JupyterHub: A multi-user server that allows multiple users to access Jupyter notebooks and other resources from a shared server. It is commonly used in educational settings or for collaborative research projects.

  • Jupyter Book: A tool for building beautiful, publication-quality books and documents from computational material, such as Jupyter notebooks. It provides a simple way to create interactive documents with executable code and visualizations.

  • nbconvert: A command-line tool that converts Jupyter notebooks to other formats, such as HTML, PDF, or Markdown. This allows you to share your work with others who may not have Jupyter installed.

  • ipywidgets: A library for creating interactive widgets in Jupyter notebooks. Widgets are user interface elements, such as buttons and sliders, that allow you to interact with and visualize data in real time.

  • nbviewer: A web application that allows you to view Jupyter notebooks without having to install Jupyter yourself. You can simply paste the URL of a notebook and view it in your browser.

Get Started with Jupyter Notebooks

There are excellent resources available for getting started with Jupyter Notebooks.

See: