#4 seems to refer to the Python basic system. However numpy.array does deal with multi-dimension arrays. On 23-Mar-2014 9:08 PM, python-ideas-request@python.org wrote:

------------------------------ Message: 4 Date: Mon, 24 Mar 2014 11:49:43 +1100 From: Steven D'Aprano To: python-ideas@python.org Subject: Re: [Python-ideas] [RFC] draft PEP: Dedicated infix operators for matrix multiplication and matrix power Message-ID: <20140324004943.GX16526@ando> Content-Type: text/plain; charset=us-ascii On Sun, Mar 23, 2014 at 11:40:10AM +0100, Sturla Molden wrote:

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On 15/03/14 01:09, Antoine Pitrou wrote:

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Really? That should be up to the third-party library implementing the @ operator for its types, not to the language itself: Python _suggests_ an use case for @, it doesn't mandate it (especially as there's no appropriate data type in the stdlib). array.array is an appropriate type for supporting @ for matrix multiplication. No it isn't. The PEP even discusses it:

[quote] array objects cannot represent multidimensional data at all, which makes ``__matmul__`` much less useful. Second, providing a quality implementation of matrix multiplication is highly non-trivial. Naive nested loop implementations are very slow and providing one in CPython would just create a trap for users. But the alternative -- providing a modern, competitive matrix multiply -- would require that CPython link to a BLAS library, which brings a set of new complications. In particular, several popular BLAS libraries (including the one that ships by default on OS X) currently break the use of ``multiprocessing`` [#blas-fork]_. And finally, we'd have to add quite a bit beyond ``__matmul__`` before ``memoryview`` or ``array.array`` would be useful for numeric work -- like elementwise versions of the other arithmetic operators, just to start. [end quote]