Seeking to velocity up your knowledge processing pipelines as much as 10 instances? Possibly it’s time to say goodbye to Pandas.

In a world the place compute time is billed by the second, it’s solely logical to attenuate it as a lot as you may. After which some.

Python’s huge knowledge processing ecosystem is nice for rookies, however difficult to scale up as dataset dimension grows. Parallel processing, question optimization, and lazy analysis are all issues unparalleled in Pandas, however are ideas you could wrap your head round if you wish to use Python in large-scale manufacturing environments.

Enter Polars. It’s a Python library written from the bottom up with efficiency in thoughts. Polars has a multi-threaded question engine written in Rust, which implies it’s best to anticipate to see blazingly quick knowledge processing instances, even 30–50 instances quicker than Pandas.

In the present day you’ll see how Polars compares to Pandas in a collection of 4 benchmarks carried out on a CSV file with 11 million rows.

However first, let’s go over the the explanation why it’s best to even contemplate Polars as a Pandas different.