Python is by far the fastest to write, cleanest, more maintainable programming language I know
Maintainable? I have not ever had to work with any large Python projects, but from what I have heard, maintenance is a large pain point.
There’s one key qualifier in this sentence: I know. One’s skill set matters, I guess…
Let me guess: The author knows Python, C, C++, Java and maybe PHP.
Also fastest to write? I’d say JS or Ruby are just as fast or barley slower.
What most people mean is that Python has great Libraries which do the thing you want without much fuss. But thats more on the libraries than on the language.
I do not know Ruby, but Python has a lot of syntactic sugar that, if one becomes used to and proficient with it, makes writing much faster than other languages I know (including JavaScript).
i’m proficient in all 3, and i can say with great certainty that generally python is far faster to write
certainly when you’re talking about JS rather than TS, and achiving a usable outcome rather than bashing together bug-ridden trash
My work uses python and it hasn’t been bad for new code that has tests and types. Old code we inherited from contractors and “yolo startup” types is less good, but we’ve generally be improving that as we touch it.
i had the misfortune once of having to try to understand a >400kLoC python codebase in a critical position and let me tell you that maintainability is a Problem. the system was older than most of the best practices of today and had a structure i can only describe as “a duolith of sqlalchemy soup”.
It’s not too bad if you strictly enforce Pyright, Pylint and Black.
But I have yet to work with Python code other than my own that does that. So in practice you are right.
There are dozens of us. Dozens!
My small Python (~100 lines of codes) codes aren’t maintainable, but I’m happy with them. I don’t ever plan to work on serious projects with Python, so I can’t say much about it’s maintainability. But, from limited experience, I’d rather use C++, C#, or in my special case, G’MIC if maintainability matters to me.
Yup. Part of what makes python so easy and fast is the lack of things built into languages so they are maintainable in a large project.
Take duck typing. It’s so easy when you have a small project that can fully understood by a developer. Get into a big project with 10000 classes and you need explicit classes and interfaces just to understand what is going on.
Python is by far one of the worst languages I’ve ever seen in relation to maintainability, second only to Javascript (due to missing types, which are fixed by Typescript).
Seriously, it’s rare for a Python project with more than 1,000 lines to not turn into an absolute mess thanks to the layers upon layers of meta programming, weird edge cases and so on. There are whole bad patterns I’ve never seen beyond Python codebases.
Things are improving slowly thanks to type hints and so on, but they are still far from where they need to be. Python is used in even more dynamic ways than JS, so the type system needs to be more expressive than TS. You can’t even define a function that appends two tuples with proper type hints!
Yes.
It seems “vanilla” Python is slow, but a JIT implementation like PyPy can speed things up significantly. The major downside seems to be that PyPy does not support Python code that relies on some CPython libraries.
But it’s fast enough for us.
Oh man, it’s only from reading this that I’ve realised that ‘pypy’ isn’t the same as ‘pypi’
Python is a great language for folks who occasionally have to do a little bit of programming, but if you have to go into any depth with it you hit shit like that.
For normal stuff, yes. For things that are implemented in C and just interfaced with in Python, so the majority of data analyis and ML libraries, no.
In my experience, it is.
I had converted a Python code into G’MIC, and then some one else did a Python version of my own code. G’MIC is interpretative with JIT math parser. The results:
Reversing digits in a 1024x1024 RGB image.
Python: Without lookup table and numpy - 3+ minutes
Python: With lookup table and numpy - 6.5 s (Some one else machine, but it shouldn’t take that long)
G’MIC: Without lookup table - .3 s
G’MIC: With lookup table - .005 s
And I did Lavander Binary Map on my machine, you can find code for Python version in github/gmic-community/include/reptorian.gmic:
Python: 3 s (Without lookup table)
G’MIC:.15 s (Without lookup table)
G’MIC: .05 s (With lookup table)
Honestly, I find Python pretty bad for image processing in general.
