On 9 Sep 2013 20:46, "Skip Montanaro" email@example.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,
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
think you very quickly will find that you need numpy arrays for
reasons, at which point you might as go "all the way" and install SciPy.
think slicing along multiple dimensions should be a significant concern
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?
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.
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