The Myriad Ways to Run Jupyter Lab Notebook for Python

My MA Economics college to begins in August/September 2024. So, turning myself into an awesome, super cool Python coding dude. First Step, run Jupyter Lab Notebooks.

For many years now, I have been hearing about python. With the AI goldrush, it seems like Python is everywhere. I am noticing the reduction in demand for tutoring (which is my main bread and butter) for JavaScript and C# come down drastically.

So, everybody wants to do Python. My own reasons are something else. I am gearing up to become an Economist. As I have been blogging on this publication, I am studying, reading, learning all things that I believe will help me in my upcoming master's in economics college degree.

It seems like there is some sort of a marriage that has already happened between Data Science tools and Economics. Plenty of overlap, I hear, on the streets (of Economics). I have avoided Python most of my life, but now, it looks like, I will need to start using Python.

Thanks to more than 20 years of coding in so many languages, I don’t have to ‘learn’ Python. Rather, I simply have to get used to its syntax, and I will soon be a Python programmer. That’s the good thing about being an experienced programmer. It’s easy to go in and out of other languages. It’s a curse and a blessing.

Anyway, I found out that, the most preferred way (or perhaps the only way) to write Python (which includes generating a lot of text, number and graphical visualizations in line with the code) is using JupyterLab notebooks.

After much wrangling around, I found out the following methods that seem most ideal for me.

1. Anaconda Cloud. Surprisingly the easiest way to get started. Just sign in with your GitHub account and start coding away. (You can also install Anaconda locally, but I don’t recommend Anaconda full suite installation as of this post)
2. JupyterLab running locally. This requires a few steps. but once you jump past all the hoops, it’s easy as butter.
   1.JupyterLab Notebooks inside VSCode. My personal favorite. Look, I am already using VS Code for many years. Microsoft has excellent extensions ecosystem. Ultimately, I decided to go with this.

Further, another plus point about VSCode is the GitHub CodeSpaces option.

As it is, I already commit all the code I write to GitHub. So, if I am away from my main gaming computers, I could simply login to GitHub on any browser, and start doing Python programming by spinning up a CodeSpace directly from GitHub.

I have the GitHub Pro subscription which gives sufficient amount of CPU cloud time, so, I am all set.

So, there you go. Hope it helps.

this is a copy of my original blog post available to read here.