I Use Nim Instead of Python for Data Processing

1

Numba

>Not type safe That's the point. Look up what duck typing means in Python. Your program is meant to throw exceptions if you pass in data that doesn't look and act how it needs to. This means that in Python you don't need to do defensive programming. It's not like in C where you spend many hundreds of lines safe-guarding buffer lengths, memory allocation, return codes, static type sizes, and so on. That means that Python code can really just look directly like the algorithms they need to implement. That makes Python genuinely shorter and more readable than almost every other language out there. >Everything is global Not really. Python passes basic types as a copy. This is 'pass by value.' Objects are passed as a 'reference' to an object. So you can change that named reference without effecting the object or you can use it to manipulate the item it points to. Your own classes, lists, and dicts fall under this category. But I don't think ints, strings, or bytes do. >virtual environments hassle Yeah, you're not wrong. Pyenv doesn't work that well and installs often break when you try set it up. It's well worth getting it properly installed though. I mostly just test from Python 3.6 (the version that introduced asyncio) and the scattering of major versions between it. I also test on all major operating systems using esxi by vmware. >very slow. This is mostly a meme. If you're doing CPU-bound work where each instruction counts then there are ways to tune Python performance. There's https://numba.pydata.org/ and https://cython.org/ 'easily tune readable Python code into plain C performance by adding static type declarations, also in Python syntax.' Python can use multiple processes, multiple interpreters, and soon 'real threads.' I should add that a lot of algorithms aren't parallel so they're not likely to benefit from such improvements. But Python byte code already is quite fast as it is. As for 'data processing' -- if the work-load is I/O bound its not going to matter if you use Python or C. It will be just as fast.

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2

Cython

>Not type safe That's the point. Look up what duck typing means in Python. Your program is meant to throw exceptions if you pass in data that doesn't look and act how it needs to. This means that in Python you don't need to do defensive programming. It's not like in C where you spend many hundreds of lines safe-guarding buffer lengths, memory allocation, return codes, static type sizes, and so on. That means that Python code can really just look directly like the algorithms they need to implement. That makes Python genuinely shorter and more readable than almost every other language out there. >Everything is global Not really. Python passes basic types as a copy. This is 'pass by value.' Objects are passed as a 'reference' to an object. So you can change that named reference without effecting the object or you can use it to manipulate the item it points to. Your own classes, lists, and dicts fall under this category. But I don't think ints, strings, or bytes do. >virtual environments hassle Yeah, you're not wrong. Pyenv doesn't work that well and installs often break when you try set it up. It's well worth getting it properly installed though. I mostly just test from Python 3.6 (the version that introduced asyncio) and the scattering of major versions between it. I also test on all major operating systems using esxi by vmware. >very slow. This is mostly a meme. If you're doing CPU-bound work where each instruction counts then there are ways to tune Python performance. There's https://numba.pydata.org/ and https://cython.org/ 'easily tune readable Python code into plain C performance by adding static type declarations, also in Python syntax.' Python can use multiple processes, multiple interpreters, and soon 'real threads.' I should add that a lot of algorithms aren't parallel so they're not likely to benefit from such improvements. But Python byte code already is quite fast as it is. As for 'data processing' -- if the work-load is I/O bound its not going to matter if you use Python or C. It will be just as fast.

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