Re: [Numpy-discussion] dtype=object behavior change from 0.9.6 to beta 1

[Charles R H. <cha...@gm...>]

I submitted a ticket for this.

On 8/31/06, Tom Denniston <tom...@al...> wrote:
>
> wrote the last email before reading your a = array([1,'A', None])
> comment.  I definately agree with you on that.
>
>
> On 8/31/06, Tom Denniston <tom...@al...> wrote:
> >
> >  Yes one can take a toy example and hack it to work but I don't
> > necessarily have control over the input as to whether it is a list of object
> > arrays, list of 1d heterogenous arrays, etc.  Before I didn't need to worry
> > about the input because numpy understood that a list of 1d arrays is a
> > 2d piece of data.  Now it understands this for all dtypes except object.  My
> > question was is this new set of semantics preferable to the old.
> >
> > I think your example kind of proves my point.  Does it really make any
> > sense for the following two ways of specifying an array give such different
> > results?  They strike me as _meaning_ the same thing.  Doesn't it seem
> > inconsistent to you?
> >
> >
> > In [13]: array([array([1,'A', None], dtype=object),array([2,2,'Some
> > string'],dtype=object)], dtype=object).shape
> > Out[13]: (2,)
> >
> > and
> >
> > In [14]: array([array([1,'A', None], dtype=object),array([2,2,'Some
> > string'],dtype=object)]).shape
> > Out[14]: (2, 3)
> > So my question is what is the _advantage_ of the new semantics?  The two
> > examples above used to give the same results.  In what cases is it
> > preferable for them to give different results?  How does it make life
> > simpler?
> >
> >
> > On 8/31/06, Charles R Harris <cha...@gm... > wrote:
> >
> > >  On 8/31/06, Tom Denniston <tom...@al... > wrote:
> >
> > > But i have hetergenious arrays that have numbers and strings and
> > > NoneType, etc.
> > >
> > > Take for instance:
> > >
> > > In [11]: numpy.array([numpy.array([1,'A', None]),
> > > numpy.array([2,2,'Some string'])], dtype=object)
> > > Out[11]:
> > > array([[1, A, None],
> > >        [2, 2, Some string]], dtype=object)
> > >
> > > In [12]: numpy.array([ numpy.array([1,'A', None]),
> > > numpy.array([2,2,'Some string'])], dtype=object).shape
> > > Out[12]: (2, 3)
> > >
> > > Works fine in Numeric and pre beta numpy but in beta numpy versions i
> > > get:
> >
> >
> > I think you want:
> >
> > In [59]: a = array([array([1,'A', None],dtype=object),array([2,2,'Some
> > string'],dtype=object)])
> >
> > In [60]: a.shape
> > Out[60]: (2, 3)
> >
> >
> > Which makes good sense to me.
> >
> > Chuck
> >
> >
> >
> >
> >
> >
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