numpy数组的多个元素具有相同的id

Question

I am trying to understand why elements with different value have the same id . Could someone explain me what is going with respect to memory management in NumPy. Example below

import numpy as np
x=np.array([1,2,3,4])
print([id(a) for a in x])

[140718968034552, 140719258631960, 140718968034552, 140719258631960]

Here, first and the third element has the same id (140718968034552) though they hold different numerical values. Same as for second and fourth elements.

Answer 1

In [54]: x=np.array([1,2,3,4])
In [55]: [type(a) for a in x]
Out[55]: [numpy.int64, numpy.int64, numpy.int64, numpy.int64]
In [56]: [id(a) for a in x]
Out[56]: [140147220886728, 140147220887808, 140147220886728, 140147220887808]

The id of small integers is unique, but that's not what the array contains:

In [57]: [type(a) for a in x.tolist()]
Out[57]: [int, int, int, int]
In [58]: [id(a) for a in x.tolist()]
Out[58]: [10914496, 10914528, 10914560, 10914592]
In [59]: id(2)
Out[59]: 10914528

Another way to get the int objects:

In [60]: [id(a.item()) for a in x]
Out[60]: [10914496, 10914528, 10914560, 10914592]

edit

If I assign the elements of x to a tuple of variables, the id are not reused. id(x0) is still in use, so id(x2) cannot take it. The alteration in Out[56] is just an artifact of memory reuse by the interpreter.

In [73]: x0,x1,x2,x3 = x
In [74]: id(x0),id(x1),id(x2),id(x3)
Out[74]: (140146931335720, 140146931335504, 140146931335600, 140146931335576)
In [75]: type(x0)
Out[75]: numpy.int64