Python development setup

So, id like to start serious python development, and its proven to be a big pain. Im not worried at all about the language itself; I like it well enough and I will have no problems picking it up. But the ecosystem is driving me crazy.

First I tried to get up and running under windows. I gave up on that after a few days, as 90% of packages dont include windows support / install instructions. So I switched to macosx, which people said was good for mac development.

More frustration ensues. Id like to use python as a matlab replacement and tool development platform, so spyderlib seems like an excellent tool. But now ive been busy trying to build pyqt on my mac for two days, to no avail, and im starting to question the wisdom of it all. Obviously, following several guides literally invariantly ends in cryptic errors. For which platform was this dependency built? What arcane compiler flags need to be set? I dont know and I dont care; why doesnt the installer figure it out开发者_Python百科? Oh wait, there isnt any... I want to USE these tools, not first completely reverse engineer them to find out how to build them.

There is a vast amount of implied knowledge in all the documentation I can find on these matters, both with regard to unix and pythonic quirks. Is there any way to scale this mountain, in a place with a managable learning curve? Right now I have no idea what im doing. Or should I go back to windows and try to coerce the unix packages I need into cooperation?

On Mac OS X, you can get spyder with macports. This should build everything needed.

If you prefer Windows, take a look at Python(x,y). It has a bunch of scientific tools pre-built, including spyder.

Finally, the Enthought Python distribution is worth considering for scientific work.

Have you tried ActivePython?

Why battle with compiling the modules yourself when you can get the pre-built packages from PyPM?

pypm install pyqt4 matplotlib scipy numpy

From my experience the best platform for kind of project you're describing is Linux. There you just install the libs you need from package manager and that's it. Binary packages, so compiling is not required.

If you want to stick with MacOS X, you should install either MacPorts or Fink. It's usually easy to use. Problem is, that things like Qt take forever to compile. But you won't be doing that very often.

As for installing Python modules, the best is PIP, which is very nice replacement for easy_install did does much more. Especially useful if you want virtualenv setup.

This is nearly the exact opposite of my experience with Python on Windows. Python itself installs with a binary installer, most add-on packages support easy_install, others provide binary installers of their own. The IDE I use is SciTE, which uses the old DOS install model - copy the files to a directory and run the SciTE.exe file. If you get a source distribution of a Python package, go to the directory containing setup.py and run python setup.py install. Maybe that's the implied knowledge you're talking about.

You can also find many unofficial Windows binaries at http://www.lfd.uci.edu/~gohlke/pythonlibs/.

I switched to Mac a few years ago and found that it took me quite a while of googling to properly install all the packages I needed for Python development. While I installed everything I made a list of the steps required to setup a functional system that may be appropriate for you as well. I usually use NetCDF4, HDF5, Numpy, Matplotlib, f2py, and Fortran in combination with Python. I published my list of 22 setup-steps for installing from source on my website. Installing from source is somewhat more time-consuming than using macports and fink, but enables you to have a working environment that is optimized for your system.