On Thu, May 20, 2010 at 4:04 PM, Keith Goodman <kwgood...@gmail.com> wrote:
> Why do the follow expressions give different dtype?
>
>>> np.array([1, 2, 3], dtype=str)
> array(['1', '2', '3'],
> dtype='|S1')
>>> np.array(np.array([1, 2, 3]), dtype=str)
> array(['1', '2', '3'],
> dtype='|S8')
you're on a 64bit machine?
S8 is the same size as the float
>>> np.array([8]).itemsize
4
>>> np.array(np.array([1, 2, 3]), dtype=str)
array(['1', '2', '3'],
dtype='|S4')
>>> np.array([8]).view(dtype='S4')
array(['\x08'],
dtype='|S4')
>>> np.array([8]).view(dtype='S1')
array(['\x08', '', '', ''],
dtype='|S1')
But I don't know whether this is a desired feature, numpy might reuse
the existing buffer (?)
Josef
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