Guido van Rossum writes: > That's food for thought.
Thank you. Let me confirm to the proponents that "food for thought" is all I intended. I know a fair amount about statistics, and almost as much about linear algebra, but nothing about physics or engineering. > Certainly I trust [Steven D'Aprano] to come up with a reasonable > strawman whose tires we can all kick. I do, too. It's only fair to give him a preview of (some?) of the arguments against inclusion in the stdlib, that's all. ;-) > [W]hich operations from the OP's list need more than > statistics._sum() when limited to NxM matrices for single-digit N > and M? (He named "matrix multiplication, transposition, addition, > linear problem solving, determinant.") I believe determinant can be efficiently implemented with statistics._sum. Linear problem solving (to which I would add the closely related operation of square matrix inversion) involves the same kind of principle, but I don't think it can be implemented with statistics._sum. The same methods that one would use for solving/ inversion for small M and N will work efficiently for large M and N. And the algorithms are as obvious as statistics._sum (in hindsight). I'm not sure whether matrices should try to implement all the different types that statistics._sum does, although I can imagine it might be pedagogically useful to support Fraction and Decimal. Steve _______________________________________________ Python-ideas mailing list -- python-ideas@python.org To unsubscribe send an email to python-ideas-le...@python.org https://mail.python.org/mailman3/lists/python-ideas.python.org/ Message archived at https://mail.python.org/archives/list/python-ideas@python.org/message/OHZ7PNPZQX4SZXPRUQGUNKQYR7HL774Q/ Code of Conduct: http://python.org/psf/codeofconduct/
