For many of the researchers and data scientists, Jupyter Notebooks are the de-facto platform when it comes to quick prototyping and exploratory analysis. Right from Paul Romer- the Ex-World bank chief Economist and also the co-winner 2018 Nobel prize in Economics to Netflix, Jupyter Notebooks are used almost everywhere. The browser-based computing environment, coupled with a reproducible document format has made them the choice of tool for millions of data scientists and researchers around the globe. But have we fully exploited the benefits of Jupyter Notebooks and do we know all about the best practises of using it? if not, then this talk is just for you.
Through this talk/demo, I'll like to discuss three main points:
Best Practises for Jupyter Notebooks since a lot of Jupyter functionalities sometimes lies under the hood and is not adequately explored. We will try and explore Jupyter Notebooks’ features which can enhance our productivity while working with them.
In this part, we get acquainted with Jupyter Lab, the next-generation UI developed by the Project Jupyter team, and its emerging ecosystem of extensions. JupyterLab differs from Jupyter Notebook in the fact that it provides a set of core building blocks for interactive computing (e.g. notebook, terminal, file browser, console) and well-designed interfaces for them that allow users to combine them in novel ways. The new interface enables users to do new things in their interactive computing environment, like tiled layouts for their activities, dragging cells between notebooks, and executing markdown code blocks in a console and many more cool things.
Every tool/features come with their set of pros and cons and so does Jupyter Notebooks/Lab and it is equally important to discuss the pain areas along with the good ones.