Why Polars Destroy Pandas in All Possible Ways for Data Scientists?

Author(s): Lazar Gugleta

Originally published on Towards AI.

One of the first Data Science libraries, Pandas, has been improving the lives of many developers across the globe, but Polars shows it is time to move on.

Pandas needs no introduction, but this article will dive deep into answering the question of why Polars is better than Pandas (even the Author of Pandas agrees).

You might be aware of some basics like memory and speed improvements, but why? How does Polars do their magic to achieve such high speeds and less memory usage?

Generated by Copilot — edited by Author

This article will provide all the reasons why Polars has an advantage over Pandas as well as what it is lacking in comparison (for now).

Let’s jump right into it!

There are so many tricks and hacks you can do with Pandas that probably developers themselves are not aware. Daily usage is no different because If I gave you a piece of code in Pandas like this: data.iloc[:, 2:] >= 4 and assuming you don’t have hyperthymesia, you would not know what this code does. It is known that developers use Google and AI bots to produce code and do not know everything off the top of their heads, but the point here is different.

The functions that the library provides should be straightforward, clear and dedicated to one use.

That is what Polars provides with their excellent documentation, function names, and overall feel… Read the full blog for free on Medium.

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Published via Towards AI