4 ms·

I'm curious what the failure of MacOS was in this instance. This comment at face value seems like you're trolling.

• x0x0 10y ago
  The intermixing of macos python and brew installed python has been painful for me in the past. Specifically, I spent hours and hours trying to fix import matplotlib.pyplot producing ImportError: cannot import name _thread but only in ipython, not the interpreter. (HAHAHAHA fuck you developers). A ton of googling and swearing produced the fact that a package named six that does god knows what was being read out of /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/six.pyc instead of /Library/Python/2.7/site-packages rm -rf'ing the former path fixed things, but fuck only knows what else I may have broken. Just because I want to plot things from ipython. ps -- 6 months ago the above used to work. Why did it stop? I have no bloody idea. But this consumed multiple hours of my life.
  • mason55 10y ago
    Seems like this would have been straight forward with a virtualenv?
    • geofft 10y ago
      Yup. Really, no OS is developer-friendly if you're going to mess with the system Python interpreter, because the system Python interpreter exists to serve system software written in Python. You can easily get yourself into very similar problems with Debian or Fedora or whatever else. Install a virtualenv, and on OS X, maybe install Python from python.org, and then everything works amazingly.
      • x0x0 10y ago
        After I learned more that was probably the right thing to do, but I'm not a python dev. I just want to use sklearn, pandas, and associated tools. I'm not sure what the smooth path to doing that is. At various times people recommend macports, brew, anaconda, and so on... Note that compiling numpy from source sucks, and I'd much rather someone else do so.
        • tanlermin 10y ago
          Anaconda. Its the best game in town right now.
          • AlexCoventry 10y ago
            I have found Anaconda to be substantially slower running on OS X than running on the same hardware in a docker container running on boot2docker.
        • geofft 10y ago
          There are now wheels (binary distributions of Python modules) of numpy, scipy, and scikit-learn for both Linux and Mac OS X, so it should just be a matter of `virtualenv /tmp/v` (or wherever), `source /tmp/v/bin/activate`, `pip install -U pip` to make sure your pip is new enough to handle wheels, and `pip install numpy scipy scikit-learn`. Nothing gets built from source. Unfortunately the same can't (yet) be said for pandas; `pip install pandas` at this point will try to compile it. Actually acquiring virtualenv on OS X seems not to be super straightforward, especially if you want to use the system Python interpreter. The best option seems to be to `brew install python`, and then `pip install virtualenv`. Or download Python from python.org, and maybe get Python 3 while you're at it.
          • x0x0 10y ago
            Thank you! I'll give that a whirl.
          • solotronics 10y ago
            one way is to get a standalone python and git clone virtualenv, then use the standalone python to make virtualenv for each project.. this leaves out anything to do with the system python
  • pyre 10y ago
    These issues also crop up with installing python libraries via pip in a system managed by apt/yum/whatever, from what I remember. Dealing with Python versions / libraries that are not part of the OS's package manager can be painful. It's why people use things like virtualenv / pyenv / Docker even in controlled server environments.
• newman314 10y ago
  TBH, it really is not dev friendly due to odd/old versions, having to install brew etc. Thus, I use my Mac as a fronted with business apps but all dev is done on Linux VMs. I like the separation and avoid all that hair pulling. Now, dealing with proprietary HW is a whole other can of worms. I spent 3hrs last night trying to load a LE cert into a Polycom phone but had ZERO success getting the phone to use it instead of the factory cert. ARGH.