I was going to ask, "Why Python 2.x"? But then I just bought the book. Hope you don't mind if I post this excerpt:
> As I write this, the latest version of Python is 3.4. At DataSciencester, however, we use old, reliable Python 2.7. Python 3 is not backward-compatible with Python 2, and many important libraries only work well with 2.7. The data science community is still firmly stuck on 2.7, which means we will be, too. Make sure to get that version.
I use the more popular scientific libraries, e.g. numpy, scikit, nltk....and the bigger ones seem to have been ported over to 3.x. A few libs that haven't that come to mind: mechanize and opencv. Has anyone here had success with using 3.x as a data science professional, or is there some massive gaping hole that I'm missing? (I agree that, "Well, this is what the company has been using" is a decent enough excuse to stay on 2.x in most situations)
Even some projects that claim have been ported, will often have bugs in them because it is new code. Then it is a question of do I have time or want test the port on my production system? I just kind of look at the issue or commit stream and see when issues appearing related to Python 3 start to slow down a bit.
> As I write this, the latest version of Python is 3.4. At DataSciencester, however, we use old, reliable Python 2.7. Python 3 is not backward-compatible with Python 2, and many important libraries only work well with 2.7. The data science community is still firmly stuck on 2.7, which means we will be, too. Make sure to get that version.
I use the more popular scientific libraries, e.g. numpy, scikit, nltk....and the bigger ones seem to have been ported over to 3.x. A few libs that haven't that come to mind: mechanize and opencv. Has anyone here had success with using 3.x as a data science professional, or is there some massive gaping hole that I'm missing? (I agree that, "Well, this is what the company has been using" is a decent enough excuse to stay on 2.x in most situations)