(This is the first post of my series of thoughts on the limitations of Jupyter notebooks, the tool that every AI/ML/Data person uses.)
The No.1 issue I have with Jupyter is that two semantically related pieces of code are usually spatially very far from each other. As a result, I have to scroll up and down frequently to connect the dots.
Here is an example from HuggingFace’s
The global variables
label_column are defined at the top of the notebook, but they are used nearly 70 lines later. When I saw their use, I had to scroll up to find out what they are. When I am done getting their meaning, I had to scroll down to continue reading the code.
Because execution results are mixed with code, two code blocks are more separated than in conventional code editors.
When I code in Jupyter, I spend a lot of time scrolling up and down. And there seems to be no way to for me to precisely jump to a line of code.
A solution: Codepod
However, with Codepod.io this can be solved. Codepod allows you to place code blocks on a canvas just like in Powerpoint or Miro. The screenshot below shows how the same code can be organized nicely in Codepod. Click here to see the “pod” live!
Related work (to be expanded)
Despite its dominance in AI/ML/DS, Jupyter has great limitations that have been well discussed in literature, including