Funny, for about every point I feel the opposite way :-). Python is an elegant language that hits the sweet spot where it is very expressive, and easy to do what you want, and at the same time has enough structure so you don't produce a mess all the time.
The libraries are very powerful, but a lot of them are hacks. Numpy and Pandas totally rely on magic and overloading the array indexing operator. You can't express a problem the natural way or it will be slow, you have to think the numpy way. I find it especially confusing if you create expressions with numpy arrays. Am I operating on them element wise, or creating a "cross product", or am I creating some kind of magic indexing object? Matplotlib is also annoying, there are so many ways to do things and half of it is object oriented, while half of it uses global state.
One thing I like about Python are the web frameworks (Django, Flask). Async is also good, although it took almost 10 years for vanilla Python to catch up with what Twisted already had.
About code breaking I also have the opposite experience. I wrote a little Gtk app in the late 2000s and it worked with little or no changes for many Python and Gtk versions, thanks to the dynamism of Python. That would be completely impossible with a compiled language.
Interesting, and nice to have a contrasting datapoint.
I do think that "The libraries are very powerful, but a lot of them are hacks. Numpy and Pandas totally rely on magic and overloading the array indexing operator. You can't express a problem the natural way or it will be slow, you have to think the numpy way. I find it especially confusing if you create expressions with numpy arrays. Am I operating on them element wise, or creating a "cross product", or am I creating some kind of magic indexing object? Matplotlib is also annoying, there are so many ways to do things and half of it is object oriented, while half of it uses global state." is an illustration of my point: the language itself isn't powerful enough to do the job so you rely on a lot of libraries written in different languages to glue it all together.
An 'elegant' language would provide a way to do so without all of these leaky abstractions.
To me Clojure is such a language, but it doesn't nearly have the kind of ease of use that Python has.
> is an illustration of my point: the language itself isn't powerful enough to do the job so you rely on a lot of libraries written in different languages to glue it all together.
The language is 100% capable. Matplotlib's API is horrible and confusing because one of their design goals was/is to be similar to matlab (that's where the "mat" in it's name comes from) to allow people who are comfortable with matlab to switch more easily.
Matlab's plotting facilities are a mess of global state, so matplotlib has copied it.
> An 'elegant' language would provide a way to do so without all of these leaky abstractions.
It would have to do something else, first: Exist.
In 10+ years of programming, I've yet to ever see a implementation of a complex abstraction that was comprehensive and complete enough that I didn't find a corner case requiring me to reach through it and deal with platform specific something-or-other.
You are right, you can't separate the language and the libraries, and a language would profit if it had a way to express these abstractions better. But if you'd that kind of syntax the Python language I think it would become bloated.
I think I like Python, because coming from C, a lot of things that are tedious there are effortless in Python (Strings! Lists and dicts, libraries, and so on.)
I came from C as well and even though Python does make strings, dicts and so on easier the main difference between the two (compiled vs interpreted) is exactly why you get all those libraries in the first place: it is super hard to get good performance out of an interpreted language if you want to crunch numbers.
That's why each and every number crunching problem will eventually make use of the various escape hatches to call libraries written in a language that is performant.
As for strings: BASIC also had strings. That doesn't mean that having strings (or even dicts) is what makes a language elegant.
The problem really is that there are only so many sweet spots for programming languages and usually those come with limitations with respect to the domain you want to use them for. Pick any two: expressive, fast, easy to use.
NumPy/matplotlib make perfect sense for Matlab users (it's basically a port of Matlab semantics). If Matlab were free and didn't suck so much as a general purpose language, I wonder to what extent Python would have been adopted in scientific computing (Matlab is still pretty strong in some communities, to be fair).
The libraries are very powerful, but a lot of them are hacks. Numpy and Pandas totally rely on magic and overloading the array indexing operator. You can't express a problem the natural way or it will be slow, you have to think the numpy way. I find it especially confusing if you create expressions with numpy arrays. Am I operating on them element wise, or creating a "cross product", or am I creating some kind of magic indexing object? Matplotlib is also annoying, there are so many ways to do things and half of it is object oriented, while half of it uses global state.
One thing I like about Python are the web frameworks (Django, Flask). Async is also good, although it took almost 10 years for vanilla Python to catch up with what Twisted already had.
About code breaking I also have the opposite experience. I wrote a little Gtk app in the late 2000s and it worked with little or no changes for many Python and Gtk versions, thanks to the dynamism of Python. That would be completely impossible with a compiled language.