python - Numpy arrays containing iterable objects -

normally it's possible put arbitrary objects numpy arrays:

class foo(object):     pass np.array([ foo() ]) >>> array([<__main__.foo object @ 0x10d7c3610>], dtype=object) 

however, appears objects implementing __len__ , __getitem__ "unpacked" automatically:

class foo(object):     def __len__(self): return 3     def __getitem__(self, i): return i*11 np.array([ foo() ]) >>> array([[0, 11, 22]]) 

is there way stop numpy unpacking objects in way? want put objects numpy array , have them stored objects themselves, without being unpacked. desired behavior is:

class foo(object):     def __len__(self): return 3     def __getitem__(self, i): return i*11 np.array([ foo() ]) >>> array([<__main__.foo object @ 0x10d7c3610>], dtype=object) 

now understand duck typing idea implies numpy should unpack looks list. perhaps it's possible mark class foo in way tell numpy not unpack it? example, abc like:

numpy.nonenumerable.register(foo) 

x = numpy.empty(appropriate_shape, dtype=object) x[:] = your_list_of_foos 

for example, 1-dimensional array of 1 foo,

x = numpy.empty([1], dtype=object) x[:] = [foo()] 

this has benefit of working types don't control or other parts of system may wish unpacked. example, if want list of 2 lists treated 1d array of lists,

x = numpy.empty([2], dtype=object) x[:] = [[], []]