In article <mailman.417.1376104455.1251.python-l...@python.org>, Skip Montanaro <s...@pobox.com> wrote:
> Given that installing numpy or scipy is generally no more difficult > that executing "pip install (scipy|numpy)" I'm not really feeling the > need for a battery here... I just tried installing numpy in a fresh virtualenv on an Ubuntu Precise box. I ran "pip install numpy". It took 1.5 minutes. It printed almost 1800 lines of build crap, including 383 warnings and 83 errors. For a newbie, that can be pretty intimidating. That's for the case where I've already installed numpy elsewhere on that box, so I already had the fortran compiler, and the rest of the build chain. For fun, I just spun up a new Ubuntu Precise instance in AWS. It came pre-installed with Python 2.7.3. I tried "pip install numpy", which told me that pip was not installed. At least it told me what I needed to do to get pip installed. Unfortunately, I didn't read the message carefully enough and typed "sudo apt-get install pip", which of course got me another error because the correct name of the package is python-pip. Doing "sudo apt-get install python-pip" finally got me to the point where I could start to install numpy. Of course, if I didn't have sudo privs on the box (most corporate environments), I never would have gotten that far. At this point, "sudo pip install numpy" got me a bunch of errors culminating in "RuntimeError: Broken toolchain: cannot link a simple C program", and no indication of how to get any further. At this point, most people would give up. I don't remember the full set of steps I needed to do the first time. Obviously, I would start with installing gcc, but I seem to remember there were additional steps needed to get fortran support. Having some simple statistics baked into the standard python package would be a big win. As shown above, installing numpy can be an insurmountable hurdle for people with insufficient sysadmin-fu. PEP-450 makes cogent arguments why rolling your own statistics routines is fraught with peril. Looking over our source tree, I see we've implemented std deviation in python at least twice. I'm sure they're both naive implementations of the sort PEP-450 warns about. And, yes, backporting to 2.7 would be a big win too. I know the goal is to get everybody onto 3.x, but my pip external dependency list includes 40 modules. It's going to be a long and complicated road to get to the point where I can move to 3.x, and I imagine most non-trivial projects are in a similar situation. -- http://mail.python.org/mailman/listinfo/python-list
