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On 9 Sep 2013 20:46, "Skip Montanaro" <skip@pobox.com> wrote:
\>
\> > However, it's common in economic statistics to have a rectangular
\> > array, and extract both certain rows (tuples of observations on
\> > variables) and certain columns (variables). �For example you might
\> > have data on populations of American states from 1900 to 2012, and
\> > extract the data on New England states from 1946 to 2012 for analysis.
\>
\> When Steven first brought up this PEP on comp.lang.python, my main concern
\> was basically, "we have SciPy, why do we need this?" Steven's response, which
\> I have come to accept, is that there are uses for basic statistics for
\> which SciPy's
\> stats module would be overkill.
\>
\> However, once you start slicing your data structure along more than one axis, I
\> think you very quickly will find that you need numpy arrays for performance
\> reasons, at which point you might as go "all the way" and install SciPy. I don't
\> think slicing along multiple dimensions should be a significant concern for this
\> package.
\>
\> Alternatively, I thought there was discussion a long time ago about
\> getting numpy's
\> (or even further back, numeric's?) array type into the core. Python
\> has an array type
\> which I don't think gets a lot of use (or love). Might it be
\> worthwhile to make sure the
\> PEP 450 package works with that? Then extend it to multiple dimensions? Or just
\> bite the bullet and get numpy's array type into the Python core once
\> and for all?
\>
\> Sort of Tulip for arrays...

Aka memoryview :)

Stefan Krah already fixed most of the multidimensional support issues in 3.3 (including the "cast" method to reinterpret the contents in a different format). The main missing API elements are multidimensional slicing and the ability to export them from types defined in Python.

Cheers,
Nick.

>
\> Skip
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