I'm transitioning from database development to working with Python and I'm curious about whether using the Polars framework could be more beneficial than sticking with Pandas for data analysis. Has anyone made the switch, and what are your thoughts on the advantages or drawbacks?
13 Answers
If you already know Pandas well, consider using Koalas or PySpark Pandas. They give you the benefits of Polars while working within a familiar framework.
I recently processed a 4GB CSV file using Polars, and it was almost 18 times faster than Pandas! I'm really impressed with Polars so far. If you're dealing with large datasets, it makes a huge difference.
Polars has a neat advantage with query planning, especially if you have a background in database development. Plus, exploring libraries like Ibis could add more flexibility as it works across different execution engines, including Polars and DuckDB.
What exactly do you mean by query planning?
Overall, Polars tends to be faster and has a more modern API, which a lot of people are starting to prefer. If you don't specifically need something that Pandas offers, like geographic data handling with GeoPandas, then Polars is definitely the way to go. Just remember, Pandas has a solid place in the ecosystem and a lot of existing support.
I hope they update GeoPandas to be compatible with Polars someday; it's a great library!
Does Polars even have a foothold in professional environments? I've always seen Pandas as the standard in places like major PEF firms. Most performance-sensitive work is done in C++, so Polars' relevance feels limited to me.
Absolutely! Polars runs its queries in a Rust engine, which makes it super efficient. I enhanced an old tool considerably using Polars, achieving an 80x speed improvement. Opting for the optimizations provided by the Polars team saves a lot of hassle.
I'm curious too; does Polars really have traction in the industry?
I think by 2026, Polars will become the go-to tool for data analysis. It feels more intuitive for those coming from SQL backgrounds. Sure, it took me some time to adjust from my Pandas habits, but it's worth the learning curve. Plus, I'm starting to look into DuckDB, which seems promising, especially for local OLAP operations. But gaining experience with both could be beneficial, honestly.
Yes, Polars is shaping up to be a big deal. It's much faster in most use cases compared to Pandas!
True, but remember that Pandas has a more established user base and transferable skills.
Thanks for the insight! Appreciate your response.
Always go with Polars in 2026! It's way faster and it's evolving into a standard over Pandas. Plus, it even has a SQL-like interface, so it's helpful if you're still learning the API.
I've experimented with both libraries recently. While Polars works well with larger datasets, both are resource-intensive. We generally transitioned our reporting needs to SQL and only use Pandas or Polars occasionally. It was mostly a cost-saving move from SAS fees!
Not sure, but searching for "Pandas vs. Polars" returns tons of results if you want to read further on the topic!
You can always explore Fireducks as a faster drop-in replacement for Pandas. It claims to outperform Polars in terms of speed without needing code changes, which is interesting.
I've been using Pandas for years and it's awesome, but I recently started working with Polars. While Pandas has great support for GIS through GeoPandas, with a few additional packages, you can cover those functionalities in Polars too. Plus, Polars' LazyFrame really speeds up development time, so I recommend giving it a try!
It really depends on your goals. While Polars is faster and many find it easier to use, if you're interviewing for data manipulation positions, most companies will expect you to know Pandas since it's the industry standard. Polars is a fantastic tool to have in your arsenal, but know that Pandas knowledge will be critical for job hunting.
In my experience conducting interviews, as long as the candidate can explain their choice and provide efficient solutions, using Polars would still be considered valid.
Do you think it's worth it for someone who usually uses Pandas but wants the speed benefits just for reading large files and then converting to a Pandas DataFrame?