3 ms·

hope they nim developers, port a lot of python data science tools e.g pandas, numpy etc so they can increase adoption in the language. Julia is supposed to fill

by dzonga 7y ago

hope they nim developers, port a lot of python data science tools e.g pandas, numpy etc so they can increase adoption in the language. Julia is supposed to fill that space, but feel strongly nim is better positioned

rayman22201 7y ago
This is definitely being worked on (slowly but steadily) and the initial results are very impressive. See: https://mratsim.github.io/Arraymancer/ https://mratsim.github.io/Arraymancer/
eigenspace 7y ago
Out of curiosity, what do you feel makes Nim better positioned in this space?
- juststeve 7y ago
  arrays start at 0?
  - eigenspace 7y ago
    I know I shouldn't feed these sorts of trollish remarks, but if it really bothers you, you can always just use https://github.com/JuliaArrays/OffsetArrays.jl https://github.com/JuliaArrays/OffsetArrays.jl
- rayman22201 7y ago
  As I said in another comment, the familiar python like syntax, while having the potential for much better performance. Many data scientists are familiar with Python. They can write similar code and will run much faster by default (or with very little tweaking). Then, if that's not fast enough, The language is positioned such that you can optimize the code to a much higher degree than you can with Python. I'm not saying that Python code can't be optimized, but there is a limit to the speed of python code even with optimization. At some point you have to drop down to a C extension (what Numpy, Pandas, etc... does) and then you are just writing glue code in Python to drive the C. You could also use one of the Python optimized compilers, but that has limits and edge cases as well. There is "developer friction" there. In Nim you can just have the whole program in one language. The program can be optimized to the same level as optimized C code with no special tools. This is much less friction. What's missing is the libraries. But that will come with time.
  - eigenspace 7y ago
    I guess I'm asking what you think Nim offers that julia doesn't here. Is it just syntax?

by dzonga 7y ago

rayman22201 7y ago

This is definitely being worked on (slowly but steadily) and the initial results are very impressive. See: https://mratsim.github.io/Arraymancer/ https://mratsim.github.io/Arraymancer/

eigenspace 7y ago

Out of curiosity, what do you feel makes Nim better positioned in this space?

juststeve 7y ago
arrays start at 0?
- eigenspace 7y ago
  I know I shouldn't feed these sorts of trollish remarks, but if it really bothers you, you can always just use https://github.com/JuliaArrays/OffsetArrays.jl https://github.com/JuliaArrays/OffsetArrays.jl
rayman22201 7y ago
As I said in another comment, the familiar python like syntax, while having the potential for much better performance. Many data scientists are familiar with Python. They can write similar code and will run much faster by default (or with very little tweaking). Then, if that's not fast enough, The language is positioned such that you can optimize the code to a much higher degree than you can with Python. I'm not saying that Python code can't be optimized, but there is a limit to the speed of python code even with optimization. At some point you have to drop down to a C extension (what Numpy, Pandas, etc... does) and then you are just writing glue code in Python to drive the C. You could also use one of the Python optimized compilers, but that has limits and edge cases as well. There is "developer friction" there. In Nim you can just have the whole program in one language. The program can be optimized to the same level as optimized C code with no special tools. This is much less friction. What's missing is the libraries. But that will come with time.
- eigenspace 7y ago
  I guess I'm asking what you think Nim offers that julia doesn't here. Is it just syntax?