[tacostakohashi]

I see this cycle where simplified "scripting" languages for "non-programmers" never last.

See: BASIC, perl, php, 4GL languages, etc.

The cycle goes like this:

1. A new simplified language for non-programmers is created

2. Non-programmers jump on board and get as far as they can...

3. Now they need just a little bit more from the language, so it is added (for perl, that was perl5 with OO, use strict, etc, for php, also the OO stuff when they turned it into java, for python, it's python3)

4. The non-programmers have become programmers, and the language has become a bit of a mess, so former non-programmers jump to C++ or Java or Clojure, and next generation of non-programmers don't like the look of the now complicated mess of a language, so back to step 1.

I guess it also doesn't help that a lot of these non-programmer languages are tightly coupled with some particular use case, like server side web apps for php, or "data science" for php, once that goes away, there isn't much of a general-purpose language left.

Anyway, yes, the appeal of C++, rust, Java, etc is definitely more stable over time - nobody learns those because they are simple, and drops them when it turns out not to be the case.

[ipaddr]

People are dropping C++ for rust because memory management is hard. People are dropping Java for golang because it's easier. People are not dropping php for Ruby because Ruby is easier to learn they drop it because wasn't it's not as cool at the time. No one left jQuery for react because jQuery was too hard.

My assembly knowledge is wasted today replaced by these higher level languages.

[midwesternerer]

Python is not tightly coupled to data science by any stretch of the imagination. The data science parts of the language have no impact on the other use cases. It isn't like someone is forcing you to use a pandas dataframe as the backend to your Django website.