Some languages have more of a proper spec than others, of course. If we look at for example Scheme dialects, then they often change their implementation for better conformance with the various rnrs standards. New Scheme dialects often early on try to conform very much to the standard to avoid issues later on.
Not yet it isn't. At least not for Python. I mean there are plenty of alternative implementations of Python but they aren't practical because they don't work with CPython modules (as far as I know anyway) so approximately nobody uses them. The de facto spec is "what CPython does".
I have run it with pandas+numpy and postgres+timescale without issue for the last year.
With Python, you don't even have to go "alternative" to use LLVM compiler infrastructure. You can use CPython + Numba and compile your kernels for both CPU and GPGPU on the go. Which is very practical.
Which is ironic, to compile a CUDA kernel in C++, you would have to use an alternative NVidia implementation of the language.
> general real world implementation
By this they mean the real-world-implementation that is in general use, which in this case is interpreted.
Yes, Python can be compiled and C++ can be interpreted. 99% of the time, they're not though.
When somebody says X language is interpreted, they mean that the most common and widely implementation is an interpreter. That's a useful bit of information to know about a language. "Correcting" them and saying that all languages can be interpreted OR compiled, doesn't really add anything of value to the discussion, other than to label yourself as a pedant.
On one hand, the conception from the 90s based on the most common Python implementation, on the other - a compiler that saves us tons of money. Which one adds value again?
That would be important in high-school debate club. In the real world, most people don't care about obscure edge cases and pedantry. In the industry vernacular, Python is an "interpreted language" and C++ is a "compiled language". Arguing against that in anything other than a highly (highly!) specialized context is just lighting candles in the wind.
We adopted it last year, and we don't rewrite code in C++ for GPGPU anymore. We save tons of effort on rewriting and even more on support.
How is this computer science?