Also what is wrong in having a switch statement? (I`d like one with range cases 1<x<10..)

Accesing dicts with a struct syntax.

'Inline' C functions.

JIT, especially for loop acceleration.

  if par:
    do_stuff()

  if par = None:
    do_stuff()

  def myFunc(par=None):
    if par:
      # do whatever is needed when the parameter exists
    pass
    #finish processing

  if not par:
    pass

  _notset = object()

  def foo(x=_notset):
      if x is _notset:
          print("foo was called as foo()")

    if par is None: ...

And for JIT, there's numba!

Agreed on the switch statement...

It's a personal project so I like to experiment, but it's also a CLI program that accepts command line arguments, runs, and then exits, so a 3-5 second delay _each_time_ it runs is pretty painful.

Luckily for me there's a 'single directory' option, which puts everything (uncompressed) into a folder. Snappy start because there's no decompression step, and while I like the idea of a single file .exe it turns out I don't really need that (I'm not copying the program from computer to computer).

So - thumbs up for PyInstaller!

Try including pandas with a few other popular libraries.

Suddenly you have 500MB blobs. Not very practical for passing around multiple projects.

I suppose that is not worse than Electron apps...

In the python world you have to do all the work by hand. I've literally spend time copying and pasting code out of libraries and into my code just so that my build wouldn't balloon by 100MB because I wanted to call a 100 line function.

This is an extremely unscientific comparison, because I don't feel like creating custom binaries specifically to test with. But I happen to have a copy of youtube-dl on my hard drive, and it's 1.8 MB. I'm not sure whether youtube-dl creates their binaries with PyInstaller, but youtube-dl is written in Python, and the binary is a single file that can be run without a Python installation.

I also have a copy of docker-compose, which I know for a fact is created with Pyinstaller. That one clocks in at a significantly worse 10.8 MB (so perhaps Pyinstaller is the culprit and youtube-dl is doing something unique), but that's still relatively small for a considerably complex program.

Lastly, I have a binary called "toggle-switchmate3", which I created myself some years ago. I have a set of Switchmate light switches set up in my apartment, and the best way I could find to control them from a Mac was via this Node package: https://www.npmjs.com/package/node-switchmate3. NPM's dependency mess freaks me out, so I set everything up in a virtual machine, and then created a binary with "pkg", the Node equivalent of pyinstaller.

"Toggle-switchmate3" is 36.1 MB. And it's not a standalone binary—it requires a separate "binding.node" file to be in the same directory.

Perhaps not in the general case, but in many cases it absolutely is. The problem isn't python itself but libraries. While JavaScript tends towards dozens of small libraries that do only one thing, python tends towards one library that does everything. Which is really handy in most cases, but very painful when you want to package something that just uses one of those functions.

In a concrete case I had a small script that ran a particular edge detection algorithm on an image and returned the edges in geojson format. The whole script was less than 300 lines. But the built dist weighed in at several hundred MB as it pulled in the entirety of numpy, scipy, scikit-image, fiona, shapely and rasterio, despite needing only maybe a couple of percent of the functionality of each library.

I did this before and found it really easy to integrate. The provided C API surface is really easy to integrate with. It was literally just a case of detailing my types, calling the Py runtime and then querying the results/side effects.

Having never done something like that before, i was full of trepidation - i was amazed at how easy it was.