That's a long way from the ease of, say, TurboPascal circa 1983.
I figured there had to be a better way, but Google only found me some pro-level IDEs. It fell to Twitter to clue me in to the modern scene. The 4 good modern options turn out to be:
- Google Colab: absolute easiest and least painful. I believe the Python code executes in the browser, so it's substantially slower than execution directly in macOS.
- Microsoft Visual Studio Code for macOS: this does require the traditional Python install with Homebrew, but it's a very beginner friendly environment. The Python plugin provides Jupyter support.
- Homebrew Jupyter: similar to Colab but like Visual Studio is part of the Homebrew/Python path.
- Azure does Jupyter Notebooks (via @jhovland) at notebooks.azure.com.
Years ago I ran into iPython as a novice environment; turns out it morphed into Juypter.
It's a sign of the times that Google search didn't turn up a blog post with these options. (It won't find this one either, I'm way off Google's radar now.) Once I'd identified the above options however I could do a Google search to find an educational resource that did mention them:
There are many ways to write and execute Python code:Python tutor (online, visual debugger)Python interpreter (command line)Visual Studio Code (editor, good debugger)Jupyter (notebook)Google Colab (online, collaborative)
During this lab we see all of them and familiarize with the exercises format. For now ignore the exercises zip and proceed reading.
That site is the University of Trento's data science lab course, updated 2019/2020. The U of Trento was founded in 1962. Reading the wikipedia page it seems to have started out focusing on sociology (and, given the era, was likely a wee bit Left) but now seems to be very tech.
The course material is presumably translated from Italian. It's quite readable though it would benefit from a native speaker updating the GitHub content. Judging by my little test it may be one of the best resources of its kind.