[ballooney]

I would add that an advantage of Matlab for number bashing is a much more native handling of linear algebra. Let's say you have two matrices A and B, in matlab I could write:

  A*B*A'

Whereas in python it would be (approximately):

  dot(A, dot(B, inverse(A)))

so matlab can evaluate everything in the right order (right to left) whereas for numpy I end up writing a little recursive function to dot a list of arguments together from right to left, which feels a bit cludgey and more of an impediment to getting your ideas down in code. Especially when your equations get very big as they often do with stats!

[lbolla]

If you intend "matrix multiplication", there is no "right order": "matrix multiplication" is associative (http://en.wikipedia.org/wiki/Matrix_multiplication).

Numpy has a "matrix" type, so you can write:

        In [7]: A = numpy.matrix('1 2; 3 4; 5 6')                                                         
        In [8]: B = numpy.matrix('1 2 3; 4 5 6')                                                          
        In [9]: C = numpy.matrix('1; 2; 3')                                                               

        In [10]: A * B * C
        Out[10]: 
        matrix([[ 78],                                                                                    
                [170],                                                                                    
                [262]])                                                                                   
        
        In [11]: (A * B) * C                                                                              
        Out[11]:                                                                                          
        matrix([[ 78],
                [170],                                                                                    
                [262]])                                                                                   

        In [12]: A * (B * C)                                                                              
        Out[12]: 
        matrix([[ 78],                                                                                    
                [170],
                [262]])

[maxerickson]

It isn't native, but numexpr is a nice in between:

http://code.google.com/p/numexpr/