I was going to say that such a matrix module would be better of in PyPI, but then I recalled how the statistics module got created, and I think that the same reasoning from PEP 450 applies here too (https://www.python.org/dev/peps/pep-0450/#rationale). So I'd say go for it! I think it would even be okay to deviate from the numpy.matrix API (though not needlessly).

On Thu, Aug 13, 2020 at 6:20 PM Steven D'Aprano <steve@pearwood.info> wrote:
On Thu, Aug 13, 2020 at 11:17:00PM +0100, Stefano Borini wrote:

> The math module has plenty of mathematical functions that are very
> interesting, but no Matrix object.

Funny you mention this, I have been working on a Matrix object for
precisely the use-case you discuss (secondary school maths), where
performance is not critical and the dimensions of the matrix is
typically single digits.

I, um, got distracted with some over-engineering and then distracted
further by work and life, but perhaps this is a good opportunity for me
to get it back on track.

> Generally, when a Matrix object is needed, numpy is the point of
> reference, but numpy requires understanding pip, installing modules,
> maybe creating a virtual environment and finally understanding numpy.

And numpy also offers a surprising interface that doesn't match
matrices. (Well, surprising to those who aren't heavy users of numpy :-)

    py> A = numpy.array([[1, 2], [3, 4]])
    py> A*A
    array([[ 1,  4],
           [ 9, 16]])

To get *matrix multiplication* you have to use the `@` multiplication
operator from PEP 465:

    py> A@A
    array([[ 7, 10],
           [15, 22]])


But what's really surprising about numpy arrays is broadcasting:

    py> A = numpy.array([[1, 2], [3, 4]])
    py> B = numpy.array([[10, 20]])
    py> A + B
    array([[11, 22],
           [13, 24]])

I don't know if broadcasting is actually useful or not, but
it's not what you want when doing matrix arithmetic at a
secondary school level.

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