It's been a while since I've seen an actual data science post submitted to Hacker News: both because AI has superset a lot of DS tasks (e.g. vector embeddings), but also because not much new has happened in DS. Polars has been around for a bit and as noted it is much better than pandas, but otherwise the DS ecosystem has been somewhat stagnant.

I'd write more tutorials about how to use data science tooling but one consequence of AI is that all the old data sources I used to analyze such as social media and Reddit are now completely locked down (I am surprised NYC Taxi is still being updated, though). Therefore in the meantime, I'm working on making better data science tooling...although unclear to what end due to the data issue above.

Am I crazy or did the OP swap the contents of the posts around accidentally?

https://eddie.codes/posts/pandas-should-go-extinct/ <=> https://eddie.codes/posts/source-code-comments/

> People typically start with Excel and graduate to Pandas somewhere in the GB range. Pandas serves them well into the 10s of GBs range, and then they start hitting memory issues, slow computation, or become frustrated with Pandas’ baroque API.

Assumes that a project moves beyond 10s of GBs. I guess 99.9% of projects that import pandas fall well below this threshold.

Strongly disagree with the author.

For dumb simple select group by OLAP queries on medium data ? Sure clickhouse local or duckdb works perfectly well.

But if you need to construct dataset ? Or process existing dataset, do heavy filtering, transformation, reshaping, splitting? The proper ETL work, then pandas is really the perfect use case.

And pandas can work with small memory footprint as well, its actually trivial to do that, plus there are libraries like Modin that are upgrades over pandas with pandas api

Pandas is the swiss knife tool of data science that lets you do anything with the data and it integrates well with ML libraries

Nitpick for the author: it looks like you've got (a,b] when you actually mean [a,b). Either that or change the ≥ to be >.

Sup Eddie, the actual motivating example is we finally nixed Pandas from Data Ingestion, now reading excel files takes 2 seconds instead of 2 minutes. However unfortunately your

> TODO: rewrite this entire service

remains.

Besides the performance benefits, I use Polars at work because it’s just (subjectively) nicer to work with. The “pl.col” API lets you create arbitrary generated/virtual columns anywhere you want, declaratively. You can throw in these column expressions in wherever without actually computing their values and storing that in memory. Very powerful stuff.

Makes sense, especially with AI coding tools the rewrite and familiarity arguments hold less water. Similar for the rustify everything crazy.

The problem is, orgs who see themselves as big data orgs want to act that way, even if they're medium data. "But we'll need it when we grow", "we need to know the state of the art tools"

Only in the last few years did I start using SQL properly. Before that my pipelines would live in python. Now I offload as much to the db as possible, and keep my python simple glue. I'm very happy with this compared to other methods in pandas or polars.

If I still need to do db-like things in python I think duckdb is better.

Real link here: https://eddie.codes/posts/source-code-comments/

Something going wonky on their blog, where two posts got their links swapped.

Liked the title

Agreed on all counts.

In many cases I’ve found directly using python primitives to be less confusing than pandas.

Similarly, in companies I’ve worked at, the datasets just aren’t that big. Especially if you’ve got access to modern hardware.

I've been saying this since using Databricks at a company almost a decade ago. Most folks do not need big data tools, and it's just so entrenched because everyone wanted to be a "big data" company and pandas was how you handled big data.

Pandas should go extinct? I’m confused Edit: Oh link was broken before

I use polars or duckdb now exclusively. Better syntax, better performance. But pandas is deeply entrenched - I try to get my team off it but it’s an uphill battle. It’s not going anywhere anytime soon.

I'd drop pandas if polars worked eamlessly with sklearn.

Title is currently, err...: Useful Code Comments

Hoping OP can fix this on their end so the url has the expected content. Whoops!

Polars seems nice but in my experience using it, the "lazy" APIs would still immediately materialize a ton of stuff in memory and had very spotty support on what data formats and storage integrations were possible with scan_* functions (though that was half a year ago and the support is slowly improving). It's frustrating, I mean really frustrating, to think I could solve a lot of my "scan through heinous amounts of data without any memory hungry things like window aggregations without blowing out my memory" with Polars and then watch my scan_thisorthat() call result in instant memory usage ballooning.

DuckDB on the other hand is wonderful and truly doesn't use any more memory than it really needs to.

so this story is something that's really important for everybody to know about and should not get downvoted...