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')
On Thu, May 20, 2010 at 4:04 PM, Keith Goodman <kwgoodman@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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On Thu, May 20, 2010 at 4:19 PM, <josef.pktd@gmail.com> wrote:
On Thu, May 20, 2010 at 4:04 PM, Keith Goodman <kwgoodman@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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On Thu, May 20, 2010 at 1:19 PM, <josef.pktd@gmail.com> wrote:
On Thu, May 20, 2010 at 4:04 PM, Keith Goodman <kwgoodman@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 (?)
Yes, I'm on a 64-bit machine. That's what I thought so I tried this:
a = np.array([1, 2, 3]) type(a[0]) <type 'numpy.int64'> np.array([a[0], a[1], a[2]], dtype=str) array(['1', '2', '3'], dtype='|S1')
But it gives '|S1' too. I guess I'm lost.
On Thu, May 20, 2010 at 4:28 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
On Thu, May 20, 2010 at 1:19 PM, <josef.pktd@gmail.com> wrote:
On Thu, May 20, 2010 at 4:04 PM, Keith Goodman <kwgoodman@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 (?)
Yes, I'm on a 64-bit machine.
That's what I thought so I tried this:
a = np.array([1, 2, 3]) type(a[0]) <type 'numpy.int64'> np.array([a[0], a[1], a[2]], dtype=str) array(['1', '2', '3'], dtype='|S1')
But it gives '|S1' too. I guess I'm lost.
for sure it doesn't look very consistent, special treatment of 0-dim ?
np.array(a[0], dtype=str) array('1', dtype='|S1') np.array(a[:1], dtype=str) array(['1'], dtype='|S4') a[:1].shape (1,) a[0].shape ()
Josef
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Thu, 20 May 2010 13:04:11 -0700, Keith Goodman 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')
Scalars seem to be handled specially. Anyway, automatic determination of the string size is a bit dangerous to rely on with non-strings in the array:
np.array([np.array(12345)], dtype=str) array(['1234'], dtype='|S4')
When I looked at this the last time, it wasn't completely obvious how to make this to do something more sensible. -- Pauli Virtanen
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