View object array under different dtype

Question

I would like to view an object array with a dtype that encapsulates entire rows:

data = np.array([['a', '1'], ['a', 'z'], ['b', 'a']], dtype=object)
dt = np.dtype([('x', object), ('y', object)])
data.view(dt)

I get an error:

TypeError: Cannot change data-type for object array.

I have tried the following workarounds:

dt2 = np.dtype([('x', np.object, 2)])
data.view()
data.view(np.uint8).view(dt)
data.view(np.void).view(dt)

All cases result in the same error. Is there some way to view an object array with a different dtype?

I have also tried a more general approach (this is for reference, since it's functionally identical to what's shown above):

dt = np.dtype(','.join(data.dtype.char * data.shape[1]))
dt2 = np.dtype([('x', data.dtype, data.shape[1])])

Note that the error is raised by np.core._internal._view_is_safe(oldtype, newtype). The condition is that either oldtype or newtype has an object field. My guess is that the developer(s) decided to treat all such conversions as unsafe, rather than deal with edge cases that might or might not be safe. — hpaulj
– hpaulj, Commented Oct 5, 2020 at 18:06

Mad Physicist · Accepted Answer · 2020-10-05 14:52:35Z

2

It seems that you can always force a view of a buffer using np.array:

view = np.array(data, dtype=dt, copy=not data.flags['C_CONTIGUOUS'])

While this is a quick and dirty approach, the data gets copied in this case, and dt2 does not get applied correctly:

>>> print(view.base)
None
>>> np.array(data, dtype=dt2, copy=not data.flags['C_CONTIGUOUS'])
array([[(['a', 'a'],), (['1', '1'],)],
       [(['a', 'a'],), (['z', 'z'],)],
       [(['b', 'b'],), (['a', 'a'],)]], dtype=[('x', 'O', (2,))])

For a more correct approach (in some circumstances), you can use the raw np.ndarray constructor:

real_view = np.ndarray(data.shape[:1], dtype=dt2, buffer=data)

This makes a true view of the data:

>>> real_view
array([(['a', '1'],), (['a', 'z'],), (['b', 'a'],)], dtype=[('x', 'O', (2,))])
>>> real_view.base is data
True

As shown, this only works when the data has C-contiguous rows.

edited Oct 5, 2020 at 14:52

answered Oct 5, 2020 at 14:40

Mad Physicist

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