Go mostly adds a lot of busywork for exchange of a speed bump (that you could get with another decent language with types, like D or Nim, if only they weren't unpopular).
Nothing about Go makes someone a better programmer the way learning C or Lisp or Haskell does.
This is true, but hopefully by the time Python library maintainers drop 2.7 support, the Go space will be more than ready.
Should be plenty of time for that to happen. There's probably a few decades of 2.7 library support, given the stats I've seen show 3.x at 10-17% of the userbase. Even if that doubles in 5 years (won't happen), it'll still be ~34% on Python3.
Which is so long from the 2008 Python3 launch date, and a small enough number migrating that we can effectively concede these are two separate communities going forward.
The majority popular Python libraries have gone the polyglot route of supporting both Python 2 and Python 3 from the same codebase, so its really not accurate to characterize Python folks as two separate communities.
Two problems with this. There is a very long tail of libraries on 2.x only that are not moving to 3. Second problem is that many 3.x only libraries are not widely used or tested and you'll find obvious errors that would not be there if they were widely used.
And as you said, some popular libs are 2/3 compatible.
So what should an end user do? Build on 3, which still has many things missing and less reliable 3.x libraries? Or stick with 2 which has everything the 3-only ecosystem has and much more.
3.x in reality makes no sense unless you have a political bone to pick. The big attraction was the unicode by default.
Python has supported unicode since 2.6. Python3 presents a new default way of handling it, nothing more. Py3 is pure technical churn at its worst. There's no innovation there. As a result the users outside of a vocal minority aren't coming.
When I started my current project two and half years ago I stuck with Python 2 because I was concerned about library support even though the major libraries I was using (Pyramid and SQLAlchemy) already supported Python 3.
While I think that was the right decision at the time, for me the balance shifted about 6 months. Needing access to the much improved multiprocessing library in Python 3 I ported my application to polyglot with the help of the python-future library. It really wasn't very difficult.
This was a modern codebase of about 20K LOC with reasonably high test coverage. For much larger or older applications then not porting is probably the right choice. But for new applications I think Python 3 is a fairly good bet now. Even if only 15-20% of the Python ecosystem have switched then that's still a significant enough userbase for most libraries to have had their Python 3 bugs shaken out.
For me specifically, StatsModels, sklearn, REPL plotting (more of a language feature) with seaborn/matplotlib, scipy, various NLP libraries, pandas for parsing CSVs and fast vector operations on table-like data, an easy to use requests library (compared to http://golang.org/pkg/net/http/) and 10^6 C libraries that either someone already made bindings for or that you can easily make bindings for with Swig/boost (not sure if it's as easy with Go).
Not to mention, all the "convenience" libs that are easy to implement but would take forever to make them all yourself. Examples like jaro distance, jaro-winkel distance, etc.
Unrelatedly, I wish Nim was more popular and I wish they concentrated on math more. Having a general purpose fast language that you can prototype in is amazing. Although you can't really blame such a small community for not focusing on a niche.
Julia might be great eventually, and it's really fun to write in and much easier to implement new things in compared to Cython, but not so great right now for my purposes. You end up importing the Julia library from Python and it takes a month to JIT compile some things. Also, string processing is not nearly as easy as it is in Python.
The problem for scientific users seems to be to achieve enough consensus about which language to use as an "obvious choice" that a bunch of other scientific users make packages.
Since we are talking about "fast" languages like Nim and Julia, I wonder why R and Python have done so well with the scientific audience but C++, Java, C# and the like never developed anything quite comparable. Or did they and I just didn't notice?
