On 08/01/2015 1:19 PM, Ryan Nelson wrote:
Colin,

I'll second the endorsement of Sage; however, for teaching purposes, I would suggest Sage Math Cloud. It is a free, web-based version of Sage, and it does not require you or the students to install any software (besides a new-ish web browser). It also make sharing/collaborative work quite easy as well. I've used this a bit for demos, and it's great. The author William Stein is good at correcting bugs/issues very quickly. 

Sage implements it's own Matrix and Vector classes, and the Vector class has a "column" method that returns a column vector (transpose). 
http://www.sagemath.org/doc/tutorial/tour_linalg.html

For what it's worth, I agree with others about the benefits of avoiding a Matrix class in Numpy. In my experience, it certainly makes things cleaner in larger projects when I always use NDArray and just call the appropriate linear algebra functions (e.g. np.dot, etc) when that is context I need. 

Anyway, just my two cents.

Ryan
Ryan,

Thanks.  I agree that Sage Math Cloud seems the better way to go for students. However your preference for the dot() world may be because the Numpy Matrix Class is inadequately developed.

I'm not suggesting that development, at this time, but proposing that the errors I referenced be considered as bugs.

Colin W.

On Wed, Jan 7, 2015 at 2:44 PM, cjw <cjw@ncf.ca> wrote:
Thanks Alexander,

I'll look at Sage.

Colin W.


On 06-Jan-15 8:38 PM, Alexander Belopolsky wrote:
On Tue, Jan 6, 2015 at 8:20 PM, Nathaniel Smith <njs@pobox.com> wrote:


Since matrices are now part of some high school curricula, I urge that

they

be treated appropriately in Numpy.  Further, I suggest that

consideration be

given to establishing V and VT sub-classes, to cover vectors and

transposed

vectors.

The numpy devs don't really have the interest or the skills to create
a great library for pedagogical use in high schools. If you're
interested in an interface like this, then I'd suggest creating a new
package focused specifically on that (which might use numpy
internally). There's really no advantage in glomming this into numpy
proper.

Sorry for taking this further off-topic, but I recently discovered an
excellent SAGE package, <http://www.sagemath.org/>.  While it's targeted
audience includes math graduate students and research mathematicians, parts
of it are accessible to schoolchildren.  SAGE is written in Python and
integrates a number of packages including numpy.

I would highly recommend to anyone interested in using Python for education
to take a look at SAGE.




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