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I have often wondered when something Python-ish will come along to compete with R. I like the power of R, and there is no better choice if you require an esoter

I have often wondered when something Python-ish will come along to compete with R. I like the power of R, and there is no better choice if you require an esoteric statistical estimator, but there are also a lot of downsides. It's difficult to debug, the interpreter seems flaky, and parts of the SPLUS syntax feels dated. Also, I know it claims to be object oriented, but I have never really understood the OO system in R. So much more time and effort is being poured into improving the Python runtime, that it seems silly not to try to build something on top of it. I am starting a stats PhD in the fall and have about nine months to kill between now and then. I'm seriously considering devoting all that time to building something like this.

chillaxn 16y ago
I also have some time to kill before finishing my degree. As an entrepreneur, I'm looking for supportive platforms that can incorporate empirical research in rapid development. For instance, I really treasure the rapid development capable in lisp (particularly PLT Scheme for home-brewed libraries). My market research methodology is focussed around measurables and "surveys". Do you know of any resources where one could learn about statistical data processing in a rapid development environment? Perhaps more specifically: smooth dataflow between processes? Best of luck on your PhD.
wesm 16y ago
I'm actually working on a stats PhD (in the early stages) and I've made it my goal to build a lot of Python software as I go along. So if you have the inclination I'd recommend giving it a shot. When you consider the wealth of tools out there for both high level (NumPy-based) and lower-level computation (e.g. Cython, for speeding up algorithms), and software development (especially interactive debugging and testing), it's a fairly compelling proposition (for me, at least). But long story short is that people are working on making Python more amenable to applied statistics work. And the more people working toward that goal, the faster we'll get there. So I would recommend: join numpy-discussion and scipy-user mailing lists, explore projects out there-- last few SciPy conferences are a decent place to start.
- hyperbovine 16y ago
  Yes, it's a tantalizing prospect because so much of the groundwork has already been laid. I have the sense that something is just needed to tie it all together into a nice, easy-to-use package with a consistent interface, easily-understood object model, etc. My masters is in math, and I have watched Sage create a small revolution in the past couple years by doing exactly this. Food for thought...
  - wesm 16y ago
    I agree-- and doing so could make some pretty serious waves across both academia and industry. You should join the discussion on the mailing lists: in particular pystatsmodels and the numpy/scipy lists. Any input would be much appreciated.

chillaxn 16y ago

I also have some time to kill before finishing my degree. As an entrepreneur, I'm looking for supportive platforms that can incorporate empirical research in rapid development. For instance, I really treasure the rapid development capable in lisp (particularly PLT Scheme for home-brewed libraries). My market research methodology is focussed around measurables and "surveys". Do you know of any resources where one could learn about statistical data processing in a rapid development environment? Perhaps more specifically: smooth dataflow between processes? Best of luck on your PhD.

wesm 16y ago

I'm actually working on a stats PhD (in the early stages) and I've made it my goal to build a lot of Python software as I go along. So if you have the inclination I'd recommend giving it a shot. When you consider the wealth of tools out there for both high level (NumPy-based) and lower-level computation (e.g. Cython, for speeding up algorithms), and software development (especially interactive debugging and testing), it's a fairly compelling proposition (for me, at least). But long story short is that people are working on making Python more amenable to applied statistics work. And the more people working toward that goal, the faster we'll get there. So I would recommend: join numpy-discussion and scipy-user mailing lists, explore projects out there-- last few SciPy conferences are a decent place to start.

hyperbovine 16y ago
Yes, it's a tantalizing prospect because so much of the groundwork has already been laid. I have the sense that something is just needed to tie it all together into a nice, easy-to-use package with a consistent interface, easily-understood object model, etc. My masters is in math, and I have watched Sage create a small revolution in the past couple years by doing exactly this. Food for thought...
- wesm 16y ago
  I agree-- and doing so could make some pretty serious waves across both academia and industry. You should join the discussion on the mailing lists: in particular pystatsmodels and the numpy/scipy lists. Any input would be much appreciated.