On Fri, 14 Aug 2020 at 05:37, Guido van Rossum firstname.lastname@example.org wrote:
My own strawman would be to limit a Matrix to 2-dimensionality -- I believe that even my college linear algebra introduction (for math majors!) didn't touch upon higher dimensionality, and I doubt that what I learned in high school about the topic went beyond 3x3 (it might not even have treated non-square matrices).
Absolutely agree, dimensionality 2 is a must. For two reasons. First, it's easy to understand and implement. Second it actively discourages those who would like to use this matrix for real calculations. The main point is basic and simple: teaching, geometry computation (e.g. on point rotation/translation) etc. Things like that. Anything else is beyond the scope of such a module and should be implemented using numpy.
Calculations about determinant, element-wise operations, matrix multiplication, inverse, etc are also basic operations that are likely to come up during either a course or trivial computations for e.g. geometry. I would not add things like QR or Cholesky factorization.