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You cant both claim that it is major language for complex data analysis and that it is "unsuitable for large parts of the job". If it is a major language for co
by fastasucan 5y ago
You cant both claim that it is major language for complex data analysis and that it is "unsuitable for large parts of the job". If it is a major language for complex data analysis it is apparently not unsuitable. It can be not optimal/not the best/bad but it cannot be unsuitable, because then your first statement isn't true. Which one is it?
- lmm 5y agoIt's a great language for exploratory prototyping of complex data analysis and a terrible language for robust implementation of complex data analysis. Well-managed organisations prototype in Python but take the time to rewrite the prototype for production use; poorly-managed ones skip step 2.
- maccard 5y agoWhat language do you rewrite it in so?
- chalst 5y agoIt's typically pretty easy to turn Python+numpy into performant Julia.
- rofrol 5y agoI remember reading this comment: >I recently tried to migrate my R code to Julia. Even though I already knew R data.table is faster than DataFrames.jl, I was totally blown away by how slow Julia is. So I quickly gave up. I think I will have to write unavoidable hard loop in cpp, which I really don't want to do... https://news.ycombinator.com/item?id=27404910 https://news.ycombinator.com/item?id=27404910
- pjmlp 5y agoApparently it is working for some, https://juliacomputing.com/case-studies/ https://juliacomputing.com/case-studies/
- lmm 5y agoIn the case I was thinking of, mainly Scala (we were a very JVM-oriented place and used Spark clusters for our big data stuff).
- pharmakom 5y agoDoes this seem really inefficient to anyone else? Why is there not a language that can do the prototype and offer a high performance ceiling? Julia seems like a candidate.
- penteract 5y agoThere's a language design tradeoff - Python is is the result of the designers almost never saying "We should do this to make Python faster, although it would make the language slightly harder to program in." This even goes as far as choosing not to make improvements that improve performance but make the implementation more complex (for example adding a JIT), which allows Python to continue to add language features.
- FuckButtons 5y agoIt doesn’t work like that in any other industry, why should it work for software? Mechanical engineers don’t put prototypes into production, why should software engineers?
- deleted 5y ago[deleted]
- pharmakom 5y agowe are writing software, which is a totally different category to mechanical engineering.
- lmm 5y agoThe language isn't the hard part. One of the more programming-oriented data scientists was actually happy to write Scala to start with, but honestly getting from a prototype to a production-quality implementation is pretty much a rewrite anyway (e.g. you have to consider all the error cases).
- nick_ 5y agoThey're saying that Python has gained a reputation for being the language for complex data analysis, but in fact it is objectively "unsuitable to large parts of the job".