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Comment by therobots927 | Hacker News Reader

The mere existence of Pandas makes me extremely grateful for SQL, because my job would be absolute hell if I had to use pandas or a similar syntax. It’s hard to overemphasize just how perfect SQL is for the job that it does.

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Agree that Pandas is horribly irregular - the only worse query language I’ve had to work with is Mongo’s. After about a decade of regular Pandas use, switching to Polars was such a relief. It’s not perfect since it’s slightly limited by being a Python library rather than an embedded query language but it’s so much better designed than Pandas - even ignoring the huge performance improvement. In my circle, Pandas is being abandoned en mass for Polars.

I don't think SQL is "perfect" and I'm not sure it's rational to even be saying that. For instance, why is it that the syntax for an SQL query is "select A from B" when many SQL-inspired syntaxes have switched to something like "from B select A" to make it more compositional?

The relational model is pretty simple though. Pandas is an awful mess.

From-before-select has nothing to do with composition as far as I can think of? That’s to solve the auto-complete issue — put the table first so the column list can be filtered.

Things like allowing repeat clauses, compute select before where, etc are what solve for composition issues

Is that really how it should be judged? If I need to get data from state A to state B, SQL is the most elegant and efficient way to do it. If I need to modify the states, or break them up to optimize for memory, SQL is the easiest way to do it. It’s been around for over 50 years and no one’s come up with anything better.

And some very simple computations become much more cumbersome in SQL, e.g. Panda's df.diff() or df.cumsum() is much more awkward using window "functions".

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