Numpy Object Array Of Numerical Arrays

I want to create an array with dtype=np.object, where each element is an array with a numerical type, e.g int or float. For example: >>> a = np.array([1,2,3]) >>>

Solution 1:

It's not exactly pretty, but...

import numpy as np

a = np.array([1,2,3])
b = np.array([None, a, a, a])[1:]

print b.dtype, b[0].dtype, b[1].dtype
# object int32 int32

Solution 2:

a = np.array([1,2,3])
b = np.empty(3, dtype='O')
b[:] = [a] * 3

should suffice.

Solution 3:

I can't find any elegant solution, but at least a more general solution to doing everything by hand is to declare a function of the form:

defobject_array(*args):
    array = np.empty(len(args), dtype=np.object)
    for i inrange(len(args)):
        array[i] = args[i]
    return array

I can then do:

a = np.array([1,2,3])
b = object_array(a,a,a)

I then get:

>>>a = np.array([1,2,3])>>>b = object_array(a,a,a)>>>print b.dtype
object
>>>print b.shape
(3,)
>>>print b[0].dtype
int64

Solution 4:

I think anyarray is what you need here:

b = np.asanyarray([a,a,a])
>>> b[0].dtype
dtype('int32')

not sure what happened to the other 32bits of the ints though.

Not sure if it helps but if you add another array of a different shape, it converts back to the types you want:

import numpy as np
a = np.array([1,2,3])
b = np.array([1,2,3,4])
b = np.asarray([a,b,a], dtype=np.object)
print(b.dtype)
>>> objectprint(b[0].dtype)
>>> int32