Introduction to Jupyter

Goals:

• Introduce what Jupyter is and why it’s useful

• Differentiate between Jupyter Notebooks and Jupyter Labs and when to use each

• Identify cell types in a notebook and how they’re used

• What to watch out for

What is Jupyter?

Jupyter, formerly known as an ipython notebook, is a popular tool used in data science and data analysis.

• An open-source, browser-based, web application with a wide variety of functions

  • Allows users to create and share computational documents, called notebooks.

  • Notebooks facilitate the development of live code that can then be run in a number of different coding languages.

    • Code can be run step by step in “chunks” called cells.

  • Users can combine live code cells with other cells - Markdown text, images, plots, and other rich media in a single interactive canvas.

  • Can produce a wide variety of interactive output including HTML, videos, LaTeX, and custom MIME types.

  • Can be shared through e-mail, GitHub, or other cloud storage and sharing services.

  • Easily exported to other formats like, books, slides, web apps, static web pages, or PDF documents.

• We see this tool used for a number of things:

  • To organize work and display the thought process or logic associated with a project

  • Collaboration

  • As an IDE (Integrated Development Environment)

    • Easy to look at or run code line-by-line to simplify debugging

  • For teaching

  • Displaying and manipulating data frames

• Requires a kernel to launch

Jupyter Notebooks

Jupyter Notebooks are just the Notebook application itself Simple interface where users can open and run notebooks Straightforward linear flow, where you can create and run cells in a single notebook Beginners may find Notebooks easier to use, initially Lacks some of the functionality of Jupyter Lab Supports extensions, but the process of installing and managing them more difficult. Available extensions is smaller compared to JupyterLab. Does not have a built-in terminal or text editor. Users need to rely on external tools or extensions for these tasks.

Notebook cell types

By default, there are 4 types of Notebook cells:

• Markdown

• Code

• NBConvert

• Heading

Markdown Text Cells allowing you to write and render Markdown syntax Where you describe and document your workflow

Code Code cells allow you to write and run programming code in a language of your choosing (e.g., Python) Languages supported in Jupyter include Python, R, Julia, and many others On ARCC HPC resources, we support jupyter code in Python and R After running, they can and usually do provide some form of output

Raw NBConvert Stands for “Raw Notebook Convert” Retains any text in these cells in their raw form and does not run them Enables the conversion of your notebook to another format as given by the FORMAT string using Jinja templates. Presenting: PDF Publishing: LaTeX Collaboration Sharing: HTML Setting to “none” just makes it a “Raw” cell in which nothing is run on it.

Jupyter Labs