[Numpy-discussion] question about creating numpy arrays

[Benjamin Root]

On Thu, May 20, 2010 at 10:30 AM, Ryan May <rmay31 at gmail.com> wrote:

> On Thu, May 20, 2010 at 9:44 AM, Benjamin Root <ben.root at 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 <dsdale24 at gmail.com> wrote:
> >>
> >> On Wed, May 19, 2010 at 4:19 PM,  <josef.pktd at gmail.com> wrote:
> >> > On Wed, May 19, 2010 at 4:08 PM, Darren Dale <dsdale24 at 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
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