Discover more from Daily Dose of Data Science
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.
Thanks for reading Daily Dose of Data Science! Subscribe for free to learn something new and insightful about Python and Data Science every day. Also, get a Free Data Science PDF (350+ pages) with 250+ tips.
👉 Tell the world what makes this newsletter special for you by leaving a review here :)
👉 If you liked this post, don’t forget to leave a like ❤️. It helps more people discover this newsletter on Substack and tells me that you appreciate reading these daily insights. The button is located towards the bottom of this email.
👉 If you love reading this newsletter, feel free to share it with friends!
👉 Sponsor the Daily Dose of Data Science Newsletter. More info here: Sponsorship details.
Find the code for my tips here: GitHub.