Pandas vs Polars — Run-time and Memory Comparison

A comprehensive benchmarking.

Pandas is an essential library in almost all Data Science projects.

But it has many limitations.

For instance, Pandas:

• always adheres to single-core computation

• offers no lazy execution

• creates bulky DataFrames

• is slow on large datasets, and many more.

Polars is a lightning-fast DataFrame library that addresses these limitations.

It provides two APIs:

• Eager: Executed instantly, like Pandas.

• Lazy: Executed only when one needs the results.

The visual above presents a comparison of Polars and Pandas on various parameters.

It’s clear that Polars is much more efficient than Pandas.

👉 Over to you: What are some other better alternatives to Pandas that you are aware of?

Find my notebook for this post here: GitHub.

Get started with Polars: Polars Docs.

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.