Adopting a Functional Paradigm With Jupyter Notebooks
By Valentin Mouret
April 19, 2021
In case you don’t know, a Jupyter Notebook is a webpage running along with some REPL (Read Eval Print Loop) — most often Python — where you can interact with the program and write a document as you go. It supports Markdown and LaTex-like mathematical annotations, which help to build good-looking documents. Jupyter Notebook is a tool widely used by data analysts, engineers, scientists, and others. It is popular because Jupyter makes development interactive: You can run some code, get immediate feedback, and easily turn it into a shareable document.
However, it’s hard to strike a balance between lightweight and organized code. Notebooks tend to be a place of chaos and random behavior, but don’t give up! It’s possible to do better.
Limits of Most Notebooks
From my experience with Notebooks, I found three main limits:
They are error-prone.
They are hard to follow.
Taking the code to production is “hell”.
1. They Are Error-Prone
Commonly, most of the variables and functions are kept in the main scope. Like the example below:
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