What version of Python should we use to for computational science courses?

Brian O'Shea (a physics prof at Michigan State) asked me the following, and I thought I'd post it on my blog to get a broader set of responses. I know the answer is "Python 3", but I would appreciate specific thoughts from people with experience either with the specific packages below, or in teaching Python 3 more generally.

(For the record, I continue to teach and develop in Python 2, but each year comes closer to switching to Python 3...)


We're going to start teaching courses for the new CMSE [ computational science] department next year, and we're using Python as the language. I need to decide whether to teach it using python 2.x or python 3.x. I'm curious about which one you have chosen to use when teaching your own courses, and why you made that choice. (Also, it'd be interesting to know if/why you regret that choice?)

The intro courses are aimed at students who plan to use computational and data science for research, other classes, and ultimately in their academic/industrial careers. We anticipate that it'll mostly be science/math and engineering students in the beginning, but there's significant interest from social science and humanities folks on campus. Given the audience and goals, my choice of programming language is fairly utilitarian - we want to introduce students to standard numerical packages (numpy, scipy, matplotlib, h5py, pandas, ipython) and also some of the discipline-specific packages, and get them into the mindset of "I have a problem, somebody has probably already written a tool to address this, let's not reinvent the wheel." So, I want to choose the version of Python that's likely to work well with pretty much any relatively widely-used Python package. My impression, based on a variety of blog posts and articles that I've found, is that the mainstream libraries work just fine with Python 3 (e.g., matplotlib), but a lot of other stuff simply doesn't work at this point.

This course is going to be the gateway course for our new minor/major, and a lot of later courses will be based on it (the graduate version of the course will be the gateway course for the certificate, and presumably taken by lots of grad students here at MSU). I'd like to make the most sensible choice given that we'll be creating course materials that will be used by other faculty, and which may stick around for a while... Anyway, any thoughts you have would be appreciated!


Brian sent me these links as examples of the discussion:

http://sjbyrnes.com/?page_id=67

http://cyrille.rossant.net/whats-wrong-with-scientific-python/

https://jakevdp.github.io/blog/2013/01/03/will-scientists-ever-move-to-python-3/

http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-0-Scientific-Computing-with-Python.ipynb


My strongest personal advice at this point is that Brian should invest in the Anaconda distribution as a teaching foundation.

Thoughts? Comments?

--titus

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