Overview

• JupyterLab is a brilliant coding environment to perform data science tasks
• These 10 reasons will convince to switch to JupyterLab from Jupyter Notebooks for data science coding

Cozying up with Jupyter? Time to move to JupyterLab!

Ask Python programmers which coding environment they prefer, the answer is invariably Jupyter notebooks. Honestly, Jupyter is synonymous when talking about data science. We take it for granted that this is the best Python coding environment out there. Even I’ve fallen for this line of thought!

Let me share with you what one of my data science mentors told me a while back — “It’s time to get over Jupyter notebooks, there is something better out there”. I was taken aback. Better than Jupyter notebooks? I took my initial steps in data science with Jupyter holding my hand, how can I move on?

And what’s the alternate coding environment anyway? Step up, JupyterLab. Jupyter as we know it has transformed into JupyterLab with much-needed upgrades along with all the good old features. And trust me, you will love working won data science tasks in JupyterLab.

So in this article, I am going to give you 10 reasons that will make you want to migrate to JupyterLab straight away.

_Note: If you are new to the data science industry or have never used Jupyter before, I recommend going through this article which is a great introduction to Jupyter notebooks. You should also check out this nifty free course on data science hacks, tips, and tricks.

Table of Contents

1. Reason #1 — Everything under one roof
2. Reason #2 — Flexible Layouts
3. Reason #3 — Cell rearrangement
4. Reason #4 — Copying cells between notebooks
5. Reason #5 — Same notebook, more views
6. Reason #6 — Code Consoles
7. Reason #7 — Themes everywhere
8. Reason #8 — Run code from a Text file
9. Reason #9 — Simultaneous preview for Markdown
10. Reason #10 — Easy switch to classic Notebook view

#data-science #machine-learning #artificial-intelligence #python #jupyter-notebook