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." 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
