Everything Is Just Functions: 1 week with David Beazley and SICP

I've watched the actual SICP lectures before (the 1986 recordings on MIT OCW). They're often praised for the information density, but it actually still wastes a lot of time listening to students' Q&A, the lecturers drawing the class' attention to various attempts at "multimedia" presentation in the classroom, simply not having the entire lesson plan worked out in advance (i.e., not being able to preempt the Q&A) etc. For that matter, the sheer amount of time spent on writing things on a chalkboard really adds up.

And of course the order of the material could be debated and rearranged countless ways. One of my future planned projects is to do my own video series presenting the material according to my own sensibilities.

It's nice to hear that the course apparently still stays true to its roots while using more current languages like Python. Python is designed as a pragmatic, multi-paradigm language and I think people often don't give it enough credit for its expressive power using FP idioms (if not with complete purity).

The course is using Python to implement a Scheme, then uses Scheme to implement a Scheme. Python could and should be removed from the course.

Python has very poor support for functional programming. Lists are not cons based, lambdas are crippled, pattern matching is horrible and not even expression based, namespaces are weird.

Python is not even a current language, it is stuck in the 1990s and happens to have a decent C-API that unfortunately fueled its growth at the expense of better languages.

Interesting, I think this is the first time I have seen anyone bash Python this hard.

Why would a decent C-API fuel its growth? Also can you give me some examples of better languages?

Am no senior developer but I find python very elegant and easy to get started with.

I’m not the parent poster, but I’ve seen two major spurts of Python’s popularity: (1) the mid-2000s when Python became a popular scripting language, displacing Perl, and (2) beginning in the first half of the 2010s when an entire ecosystem of Python APIs backed by code written in C, C++, and even Fortran made up the infrastructure for machine learning code (e.g., NumPy, SciPy, scikit-learn, Pandas, etc.). If Python didn’t have a good way of interfacing with code written in languages like C, then it might not have been as popular among machine learning researchers and practitioners, who needed the performance of C/C++/Fortran for numerical computing but wanted to work with higher levels of abstraction than what is provided in those languages.

What drew me to Python back in 2006 as a CS student who knew C and Java was its feeling like executable pseudocode compared to languages that required more “boilerplate.” Python’s more expressive syntax, combined with its extensive “batteries included” standard library, meant I could get more done in less time. Thus, for a time in my career Python was my go-to language for short- and medium-sized programs. To this day I often write pseudocode in a Python-like syntax.

Since then I have discovered functional programming languages. I’m more likely to grab something like Common Lisp or Haskell these days; I find Lisps to be more expressive and more flexible than Python, and I also find static typing to be very helpful in larger programs. But I think Python is still a good choice for small- and medium-sized programs.