The science community will get there. The problem is that for a long time, NumPy/SciPy/etc. didn't support 3.x. And when you're more interested in end results, why would you rewrite your code (or spend days/weeks/months relearning) when you could use a still perfectly acceptable and supported version?

There's also a lot of reuse and expansion on existing code bases, which would involve a lot of work to migrate to 3.x. There's also the matter that on top of moving to 3.x, you also have the task of making sure there's no hidden bugs that may alter your results in ways you may not notice. A lot of scientific code has been repeatedly vetted to make sure that there's no bias or glitches that may skew your results.

Hell, I know astronomers who are still using Fortran code that was written in the 80's. It still works (though now it requires a long build process, as it is no longer compatible with the latest Fortran compilers), so no reason to try to rewrite it just because the language is dated.

Half the core scientific packages are already P3-compatible. Numpy has supported P3 since 2010[0] and scipy since early 2011[0][1]

[0] http://sourceforge.net/projects/numpy/files//NumPy/1.5.0/NOT... https://pypi.python.org/pypi/numpy/1.5.0

[1] http://docs.scipy.org/doc/scipy-0.9.0/reference/release.0.9....

Yeah that's true. Getting everyone to 3 is going to be quite a slog. I have a feeling once Python 3 becomes the default Python installation for OSX and Linux systems you'll see a big uptick in adoption (and probably some abandonment as well).

NLP will definitely be on Python3 sooner rather than later. unicode strings is a really killer feature for us.

I'm hearing more rumblings about using 3.x now. Certainly people in this community aren't racing to switch, but I wouldn't say it will never happen.