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copied!<p>On a Mac the easiest ways to get started are (in no particular order):</p> <ul> <li><a href="http://www.enthought.com/products/epd.php" rel="nofollow noreferrer">Enthought Python Distribution</a> which includes most scientific packages you are likely to need. Free for academic/non-commercial use.</li> <li><a href="http://www.macports.org/" rel="nofollow noreferrer">Macports</a> - up to date with latest releases, so <code>sudo port install py26-numpy py26-scipy py26-matplotlib py26-ipython</code> should get you started.</li> <li><a href="https://fonnesbeck.github.io/ScipySuperpack/" rel="nofollow noreferrer">Scipy Superpack</a> - script to install recent svn versions of all the important packages.</li> </ul> <p>I've done exactly this (replace Matlab with Python) about 2 years ago and haven't looked back. The broadcasting in Python, more intuitive memory model and other Numpy advantages make numerical work a complete pleasure. Plus with f2py, cython it is incredibly easy to put inner loops in another language. <a href="http://www.scipy.org/NumPy_for_Matlab_Users" rel="nofollow noreferrer">This</a> is a good place to start - other impressive pages to provide motiviation are <a href="http://wiki.scipy.org/PerformancePython" rel="nofollow noreferrer">PerformancePython</a> and <a href="http://wiki.scipy.org/ParallelProgramming" rel="nofollow noreferrer">ParallelProgramming</a>. Be sure to understand Pythons "variable is a reference to an object" semantics... after that adjustment everything is plain sailing. One of the coolest things that beats matlab is in 2 lines I run over 8 cores... <code>p = Pool(8); res = p.map(analysis_function,list_of_data)</code> - MATLAB parallels toolboxes are so expensive I've yet to see a University that actually has them.</p>

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