[Numpy-discussion] Different attributes for NumPy types
Travis E. Oliphant
oliphant at enthought.com
Thu May 22 20:38:10 EDT 2008
Charles R Harris wrote:
>
>
> On Thu, May 22, 2008 at 5:07 PM, Robert Kern <robert.kern at gmail.com
> <mailto:robert.kern at gmail.com>> wrote:
>
> On Thu, May 22, 2008 at 4:25 PM, Bruce Southey <bsouthey at gmail.com
> <mailto:bsouthey at gmail.com>> wrote:
> > On Thu, May 22, 2008 at 2:59 PM, Robert Kern
> <robert.kern at gmail.com <mailto:robert.kern at gmail.com>> wrote:
> >> On Thu, May 22, 2008 at 2:46 PM, Charles R Harris
> >> <charlesr.harris at gmail.com <mailto:charlesr.harris at gmail.com>>
> wrote:
> >>> It also leads to various inconsistencies:
> >>>
> >>> In [1]: float32(array([[1]]))
> >>> Out[1]: array([[ 1.]], dtype=float32)
> >>>
> >>> In [2]: float64(array([[1]]))
> >>> Out[2]: 1.0
> >>
> >> Okay, so don't do that. Always use x.astype(dtype) or
> asarray(x, dtype).
> >
> > So, should these return an error if the argument is an ndarray
> object,
> > a list or similar?
>
> I think it was originally put in as a feature, but given the
> inconsistency and the long-standing alternatives, I would deprecate
> its use for converting array dtypes. But that's just my opinion.
>
>
> I agree. Having too many ways to do things just makes for headaches.
> Should we schedule in a deprecation for anything other than scalars
> and strings.
I don't have a strong opinion either way.
-Travis
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