On Thu, May 20, 2010 at 4:19 PM, <josef.p...@gmail.com> wrote:
> 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
not float, it should be int, here is float on my Win32:
>>> np.array(np.array([1., 2, 3]), dtype=str)
array(['1.0', '2.0', '3.0'],
dtype='|S8')
>>> np.array([8.]).itemsize
8
>
>
>>>> 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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>>
>
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