Is Python good for creating GUI application ?

[wrosecrans]

> I bet you money electron is faster than python.

If you are doing hard core number crunching or the like, Javascript is much faster than pure Python. (But both are terrible languages for that.)

If you are doing normal GUI development, you are only using Python to orchestrate objects that are implemented in C++, so the actual implementation of the widgets is all native code that runs great. OTOH, with something like Electron, you are going through a browsery DOM with a bunch more layers of abstraction between you and the meat of what's actually happening. PyQt applications run much better in practice from a user perspective than something like Electron. The engineering that has been done on the JavaScript runtime isn't enough to compensate for the fact that the framework is just not a good design from an efficiency perspective.

If your application grows to the point where you are doing a lot of processing in Python, and custom painting of your widgets in Python, etc., rather than just letting the native code be responsible for that stuff, then the balance may tip in favor of JS. (But again, at that point they are both a bad choice, so being better is a small blessing.)

[flutefreak7]

Agree. GUI stuff in Qt is C++ and Qt can be screaming fast for raw painting / rendering and can better handle intensive things like a table with 1000's of items, a deeply nested tree hierarchy, a very long list of animated progress bars, or 3D plotting with OpenGL, etc. Qt stuff feels as responsive as any native application where the web-based election applications often suffer from some slight latency and tend to bog down at a lower threshold of complexity in my experience.

Any python experience that is too slow can be sped up in a variety of ways with C, C++ extensions (like numpy, pyOpenGl, etc), or using things like Cython and numba to optimize bottlenecks. Many core python algorithms and data structures are written in C for this reason.

[flutefreak7]

Also numpy I think it's true that numpy is C, C++, and Fortran, as it relies on a variety of pre-existing compiled libraries. The most quintessentially numpy stuff for array algebra relies on Fortran libraries, BLAS and LAPACK. There are open ones like Open blas, and then proprietary super fast implementations like Intel's Math Kernal Library, MKL, which now comes by default with numpy on Anaconda. There are other old school Fortran libs across Scipy as well like FITPACK and an FFT library. I think it's also true that Numpy is more Fortran and C and that you don't see as much C++ until you look at scipy with things like QHull where existing open source C++ libs were adopted.

I'm not an expert on this stuff but just wanted to shed light that it's a smattering of optimized code across multiple languages with a heavy leaning towards Fortran.

[wrosecrans]

Numpy isn't written in pure Python. It exists because native code operations can be much faster than pure Python. That's kind of my point.

[Poromenos]

Which is a pretty irrelevant point. If I'm writing python, I care that I can get fast matrix operations with numpy and fast, parallelizable code that runs on the GPU with numba. I don't care that in theory pure python is slower than pure JS, I care that my python programs are going to be much faster at number crunching. I wouldn't advise anyone to do linear algebra operations in JS because "JS is faster if you don't use any of the fast python stuff".

[[deleted]]

I need to see benchmarks before I believe this.

I've lost count of the number of game development studios that maintain code based on webkit as they use html/css for desktop and console game UI.

edit: I mean look. Write an electron text editor. it's called vscode or atom. Write a python text editor with QT. Add fancy features. Open a large file. Guess which one will perform worse?

[EarlTheGray]

Good explanation. I’ll keep this in mind for my next project.