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.
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]
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