Looking to speed up your data processing pipelines up to 10 times? Maybe it’s time to say goodbye to Pandas.
In a world where compute time is billed by the second, it’s only logical to minimize it as much as you can. And then some.
Python’s vast data processing ecosystem is great for beginners, but challenging to scale up as dataset size grows. Parallel processing, query optimization, and lazy evaluation are all things unheard of in Pandas, but are concepts you must wrap your head around if you want to use Python in large-scale production environments.
Enter Polars. It’s a Python library written from the ground up with performance in mind. Polars has a multi-threaded query engine written in Rust, which means you should expect to see blazingly fast data processing times, even 30–50 times faster than Pandas.
Today you’ll see how Polars compares to Pandas in a series of 4 benchmarks performed on a CSV file with 11 million rows.
But first, let’s go over the reasons why you should even consider Polars as a Pandas alternative.