On Thu, May 20, 2010 at 10:30 AM, Ryan May <rma...@gmail.com> wrote: > On Thu, May 20, 2010 at 9:44 AM, Benjamin Root <ben.r...@ou.edu> wrote: > >> I gave two counterexamples of why. > > > > The examples you gave aren't counterexamples. See below... > > > > On Wed, May 19, 2010 at 7:06 PM, Darren Dale <dsdal...@gmail.com> wrote: > >> > >> On Wed, May 19, 2010 at 4:19 PM, <josef.p...@gmail.com> wrote: > >> > On Wed, May 19, 2010 at 4:08 PM, Darren Dale <dsdal...@gmail.com> > wrote: > >> >> I have a question about creation of numpy arrays from a list of > >> >> objects, which bears on the Quantities project and also on masked > >> >> arrays: > >> >> > >> >>>>> import quantities as pq > >> >>>>> import numpy as np > >> >>>>> a, b = 2*pq.m,1*pq.s > >> >>>>> np.array([a, b]) > >> >> array([ 12., 1.]) > >> >> > >> >> Why doesn't that create an object array? Similarly: > >> >> > > > > > > Consider the use case of a person creating a 1-D numpy array: > > > np.array([12.0, 1.0]) > > array([ 12., 1.]) > > > > How is python supposed to tell the difference between > > > np.array([a, b]) > > and > > > np.array([12.0, 1.0]) > > ? > > > > It can't, and there are plenty of times when one wants to explicitly > > initialize a small numpy array with a few discrete variables. > > What do you mean it can't? 12.0 and 1.0 are floats, a and b are not. > While, yes, they can be coerced to floats, this is a *lossy* > transformation--it strips away information contained in the class, and > IMHO should not be the default behavior. If I want the objects, I can > force it: > > In [7]: np.array([a,b],dtype=np.object) > Out[7]: array([2.0 m, 1.0 s], dtype=object) > > This works fine, but feels ugly since I have to explicitly tell numpy > not to do something. It feels to me like it's violating the principle > of "in the face of ambiguity, resist the temptation to guess." >
I have thought about this further, and I think I am starting to see your point (from both of you). Here are my thoughts: As I understand it, numpy.array() (rather, array_like()) essentially builds the dimensions of the array by first identifying if there is an iterable object, and then if the contents of the iterable is also iterable, until it reaches a non-iterable. Therefore, the question becomes, why is numpy.array() implicitly coercing the non-iterable type into a numeric? Is there some reason that I am not seeing for why there is an implicit coercion? At first glance, I did not see a problem with this behavior, and I have come to expect it (hence my original reply). But now, I am not quite so sure. > Ryan > > -- > Ryan May > Graduate Research Assistant > School of Meteorology > University of Oklahoma > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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