Polars

“Tools like Polars and dplyr and even SQL require code that's far closer to how people think.”

“It is optimized for analytic workloads (default in-mem columnar layout), whereas PG and sqlite are OLTP DBs.”

“It's like you get a really good 'query planner' like a DB would give you, but for your notebooks/scripts/etc.”

“we have some low-hanging fruit in Polars when it comes to optimizing our constant overhead for smaller queries”

“polars takes about 2.5x as long. Polars tends to shine with larger datasets or where it can heavily take advantage of query planning.”

“polars and duckdb are objectively better APIs in addition to their implementation improvements”

“Polars is more than mature enough in both documentation and resources for it to be a daily driver.”

“it is easier to decompose polars (and all dataframe api) queries and to build them up from pieces than it is to do the same with sql”

“This has been my go-to library for data processing due to the enhanced ergonomics compared to Pandas and SQL.”

“The cool thing about polars is that you can conditionally collect expressions over many layers of business logic”

“I love polars. Did a lot of evangelizing in work to get people to give up pandas in favor of it.”

“the fact that you can do var= in place of assign in with_columns/agg changed my whole outlook on polars”

“I'm sure Polars is great, but I can't get over needing 10 characters of ceremony every time I want to refer to a column”

“Polars is able to lazy evaluate query plans without any unnecessary intermediate allocations”

“Can I do the same with Polars? Yes, but it comes with exponentially more cognitive overhead.”

“Polars has timeseries support, but I need to write a paragraph of extra code to deal with it.”

“If I get stuck in polars, its almost always just a "what's the name of the function to use?" type problem”

“the API (according to my subjective opinion) is much more logical and has far fewer surprises”

“Z-ordering and deletion vector can also be handed by polars and some rust functions, which is efficient”

“Personally I find it extremely rare that I need to do this given Polars expressions are so comprehensive”

“Polars specifically has lazy mode which enables a query optimizer, so you get predicate push down and all the goodies if SQL”

“method chaining in Polars is much more composable and ergonomic than SQL once the pipeline gets complex”

“Polars is undeniably great software, it's my standard tool today. ... I do have a concern with their VC funding/commercialization with cloud.”

“Polars is great, but it is better precisely because it learned from all the mistakes of Pandas”

“That was probably about what I got when I migrated some heavy number crunching code from Pandas to Polars”

“Polars being so fast, and embeddable into other languages, has made it a no brainer for me to adopt it.”

“Polars is VC backed Opensource project with cloud offering, which may become an opencore project”

“Polars has all of these benefits (to some degree), but also allows for larger-than-memory datasets.”

“What put me off was that I was essentially designing my own database which I didn't trust as much”

“This makes Polars data frames work much more naturally with typical programming abstractions.”

“The number one benefit of Polars, imho, is that Polars works using Expressions that allow you to trivially compose and reuse fundamental logic”

“Speed and better nested data support (arrays/JSON) alone are extremely useful to every data scientist.”

“This is a non-issue with Polars dataframes to_pandas() method. You get all the performance of Polars for cleaning large datasets”

“The performance improvements are secondary to me compared to the dramatic improvement in interface.”

“In Python it's more verbose, as the other commenter points out. In Rust it's very painful.”

“in general gets even more verbose (apparently by design), which gets annoying fast when doing explorative data analysis”

“the author can use Pandas or Polars or another data-science-related library/framework in Python to get the job done”

“It's way faster, has an ergonomic API, seamless pandas interop and amazing support for custom extensions.”

“The greatest speed up I got was using polars to process the mostly-string CSVs, but didn't help much.”

“The dataframe paradigm (a good example being polars) is another good alternative that's more composable”