On Nov 16, 2007 5:55 PM, Emanuel Woiski <woiski@gmail.com> wrote:

Sorry for coming very late to the thread, but you mean something like:
for i in range(len(cols):

[sending too soon...]

On Oct 9, 2007 4:36 AM, Sven Schreiber < svetosch@gmx.net > wrote:
Alan G Isaac schrieb:
> On Mon, 8 Oct 2007, Robin apparently wrote:
>> However in my code (I am converting from MATLAB) it is
>> important to maintain 2d arrays, and keep the difference
>> between row and column vectors.
>

Well, I have noticed that numpy doesn't care very much about rows and cols. Mind you, if you slice along a col, you end up with a row - just try it and see. But how can you evaluate an expression such as a[i] - b[j], for all (i,j), with i for rows and j for cols?
The trick here is 'newaxis' . With 'newaxis' you have a temporary dimension for a or b, without actually changing a or b shapes. Following the usual meaning, the expression becomes: c = a[:,newaxis] - b
See:

>>> a = arange(3.)
>>> a
array([ 0., 1., 2.])
>>> b = a # just an example...
Now I want:
>>> c1 = zeros((3,3))
>>> for i in range(3):
    for j in range (3):
        c1[i,j] = a[i] - b[j]

>>> c1
array([[ 0., -1., -2.],
       [ 1., 0., -1.],
       [ 2., 1., 0.]])

That's exactly the same as the one-liner:

>>> c2 = a[:,newaxis] - b
>>> c2
array([[ 0., -1., -2.],
       [ 1., 0., -1.],
       [ 2., 1., 0.]])

Nice isn't it? Hope that help you somehow....:)
cheers
woiski