I think the main reason to use any of these array languages (for work) is job security. Since it's so hard to find expert programmers if you can get your employer to sign off on an array language for all the mission-critical stuff then you can lock in your job for life! How can they possibly replace you if they can't find anyone else who understands the code?
Otherwise, I don't see anything you can do in an array language that you couldn't do in any other language, albeit less verbosely. But I believe in this case a certain amount of verbosity is a feature if you want people to be able to read and understand the code. Array languages and their symbol salad programs are like the modern day equivalent of medieval alchemists writing all their lab notes in a bespoke substitution cipher. Not unbreakable (like modern cryptography) but a significant enough barrier to dissuade all but the most determined investigators.
As an aside, I think the main reason these languages took off among quants is that investing as an industry tends toward the exultation of extremely talented geniuses. Perhaps unintelligible "secret sauce" code has an added benefit of making industrial espionage more challenging (and of course if a rival firm steals all your code they can arbitrage all of your trades into oblivion).
I'm sorry but you really sound like you judge APLs from an outsider pov. For sure, it's not job security that's keeping APLs afloat, because APLs are very easy to learn. A pro programmer would never use K or any APL. But pro mathematicians or scientists needing some array programming for their job will.
I have been a programmer, scientist, etc, at various times in my life and I have programmed in J. I don't think there is any compelling reason to use J over Matlab, R, or Python and very many reasons not to. Vector languages are mind expanding, for sure, but they have done a very poor job keeping up with the user experience and network effects of newer languages.
A few years ago I wrote a pipeline in J and then re-implemented it R. The J code was exactingly crafted and the R code was naive, but the R code was still faster, easier to read and maintain and, frankly, easier to write. J gives a certain perverse frisson, but beyond that I don't really see the use case.
Are you a quant, or doing very specialized numerical things that aren't in libs on 2D datasets? Then 10X yes. If not, no. Everything else will be better
I'm not a quant but having used "data science" languages, ocaml, J, R, etc, I strongly doubt that an array language offers any substantial advantages at this date. I could be wrong, of course, but it seems unlikely.
J only is a data science language in the sense that its core is suited to dataset manipulation, but it's severely lacking in modelling librairies. If you're doing exotic stuff and you know exactly what you're doing, I can see the rationale but otherwise it's R/Python any day. It makes plenty of sense for custom mathematical models, such as simulations though.
I think the main reason to use any of these array languages (for work) is job security. Since it's so hard to find expert programmers if you can get your employer to sign off on an array language for all the mission-critical stuff then you can lock in your job for life! How can they possibly replace you if they can't find anyone else who understands the code?
Otherwise, I don't see anything you can do in an array language that you couldn't do in any other language, albeit less verbosely. But I believe in this case a certain amount of verbosity is a feature if you want people to be able to read and understand the code. Array languages and their symbol salad programs are like the modern day equivalent of medieval alchemists writing all their lab notes in a bespoke substitution cipher. Not unbreakable (like modern cryptography) but a significant enough barrier to dissuade all but the most determined investigators.
As an aside, I think the main reason these languages took off among quants is that investing as an industry tends toward the exultation of extremely talented geniuses. Perhaps unintelligible "secret sauce" code has an added benefit of making industrial espionage more challenging (and of course if a rival firm steals all your code they can arbitrage all of your trades into oblivion).
I'm sorry but you really sound like you judge APLs from an outsider pov. For sure, it's not job security that's keeping APLs afloat, because APLs are very easy to learn. A pro programmer would never use K or any APL. But pro mathematicians or scientists needing some array programming for their job will.
I have been a programmer, scientist, etc, at various times in my life and I have programmed in J. I don't think there is any compelling reason to use J over Matlab, R, or Python and very many reasons not to. Vector languages are mind expanding, for sure, but they have done a very poor job keeping up with the user experience and network effects of newer languages.
A few years ago I wrote a pipeline in J and then re-implemented it R. The J code was exactingly crafted and the R code was naive, but the R code was still faster, easier to read and maintain and, frankly, easier to write. J gives a certain perverse frisson, but beyond that I don't really see the use case.
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Are you a quant, or doing very specialized numerical things that aren't in libs on 2D datasets? Then 10X yes. If not, no. Everything else will be better
I'm not a quant but having used "data science" languages, ocaml, J, R, etc, I strongly doubt that an array language offers any substantial advantages at this date. I could be wrong, of course, but it seems unlikely.
J only is a data science language in the sense that its core is suited to dataset manipulation, but it's severely lacking in modelling librairies. If you're doing exotic stuff and you know exactly what you're doing, I can see the rationale but otherwise it's R/Python any day. It makes plenty of sense for custom mathematical models, such as simulations though.
J and OPFS