How to Write Readable Code as a Data Scientist
Imagine stepping into a data science project halfway through its lifecycle. You’re tasked with understanding the codebase, making changes, and adding new features. But you're greeted with a labyrinth of code, a tangled web of variables, functions, and logic that seems to defy comprehension. As you attempt to decipher its mysteries, frustration sets in, and clarity becomes a distant dream.
This scenario is all too common in the world of data science, just as it is in software engineering. Usually, data scientists are more focused on the data and the models they build, rather than the code that powers them. But at the end of the day, code is the glue that holds everything together. It’s the bridge between your ideas and the end product.
In this article, we’ll explore the art of writing readable code and provide some practical tips.