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I've migrated off of pandas to polars for my workflows to reap the benefit of, in my experience a 10-20x speedup on average. I can't imagine anything bringing me back short of a performance miracle. LLMs have made syntax almost a non-barrier.
Earlier quoted context omitted.
Do you not experience LLM generated code constantly trying to use Pandas' methods/syntax for Polars objects?
There were some growing pains in gpt-3.5 to gpt-4 era, but not nowadays (shoutout to the now-defunct Phind, which was a game changer back then).
I've migrated off of pandas to polars for my workflows to reap the benefit of, in my experience a 10-20x speedup on average. I can't imagine anything bringing me back short of a performance miracle. LLMs have made syntax almost a non-barrier.
Do you not experience LLM generated code constantly trying to use Pandas' methods/syntax for Polars objects?
How soon will the leading LLMs ingest the updated documentation? Because I'm certainly not going to.
I've migrated off of pandas to polars for my workflows to reap the benefit of, in my experience a 10-20x speedup on average. I can't imagine anything bringing me back short of a performance miracle. LLMs have made syntax almost a non-barrier.
Same. I don't even use LLM normally as I found polars' syntax to be very intuitive. I just searched my ChatGPT history and the only times I used it are when I'm dealing with list and struct columns that were not in pandas.
Earlier quoted context omitted.
There were some growing pains in gpt-3.5 to gpt-4 era, but not nowadays (shoutout to the now-defunct Phind, which was a game changer back then).
The fact they pivoted away from their very compelling core offering (AI stack overflow) to complete with loveable etc in the "AI generated apps" giant fight continues to baffle me. Though I guess model updates ate their lunch.
Like you said, perhaps the demise of phind was inevitable, with large models displacing them kind of like how Spotify displaced music piracy.
Earlier quoted context omitted.
Do you not experience LLM generated code constantly trying to use Pandas' methods/syntax for Polars objects?
Yes, ChatGPT 5.2 Pro absolutely still does this. Just ask it for a pivot table using Polars and it will probably spit out code with Pandas arguments that doesn’t work.
It’s probably not worth incurring the pain of a compatibility-breaking Pandas upgrade. Switch to Polars instead for new projects and you won’t look back.
I've migrated off of pandas to polars for my workflows to reap the benefit of, in my experience a 10-20x speedup on average. I can't imagine anything bringing me back short of a performance miracle. LLMs have made syntax almost a non-barrier.
OT, but I can’t imagine data science being a job category for too long. It’s got to be one of the first to go in AI age especially since the market is so saturated with mediocre talents.
The design of Pandas is inferior in every way to Polars: API, memory use, speed, expressiveness. Pandas has been strictly worse since late 2023 and will never close the gap. Polars is multithreaded by default, written in a low-level language, has a powerful query engine, supports lazy, out-of memory execution, and isn’t constrained by any compatibility concerns with a warty, eager-only API and pre-Arrow data types th…