Explore the world of programming with Jupyter | Opensource.com

Explore the world of programming with Jupyter

Download the JupyterLab cheat sheet to make it easier to use the Jupyter user interface.

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JupyterLab is the next-generation web-based Jupyter user interface. It allows you to work with Jupyter Notebooks, as well as editors, terminals, and more, to produce interactive documents for data science, statistical modeling, data visualization, and more.

It has native viewers for PDF, CSV, JSON, images, and more. It is also extensible to support other formats.

JupyterLab's left sidebar has tabs for using it as a file manager, a Jupyter kernel manager, or a Jupyter Notebook metadata editor.

Writing code in Jupyter Notebooks enables an interactive development experience. You can write code, see the results, and modify the code—all without restarting your process or losing your in-memory data. This is a great fit for exploratory programming when you are not sure what your end result will look like.

Exploration is common in data science; after all, science is the process of finding out answers not known before. But exploration is not limited to data science. Jupyter works well for system diagnostics and automation where you don't know the answer or solution in advance. Whenever feedback is useful for the next step, whether it is image manipulation, analyzing your exercise data, or writing games, Jupyter's bias toward exploration can be helpful.

Jupyter and JupyterLab are great tools, so this JupyterLab cheat sheet will make it easier for you to get started.

Download the JupyterLab cheat sheet 

Polaroids and palm trees

Who needs to learn an image-editing application when you can do the job with open source tools you already know?
Python in a coffee cup.

JupyterLab, the successor to Jupyter Notebook, feels like playing video games with the cheat codes enabled.


About the author

Moshe sitting down, head slightly to the side. His t-shirt has Guardians of the Galaxy silhoutes against a background of sound visualization bars.
Moshe Zadka - Moshe has been involved in the Linux community since 1998, helping in Linux "installation parties". He has been programming Python since 1999, and has contributed to the core Python interpreter. Moshe has been a DevOps/SRE since before those terms existed, caring deeply about software reliability, build reproducibility and other such things. He has worked in companies as small as three people and as big as tens of thousands -- usually some place around where software meets system administration...