Why the elements of numpy array not same as themselves?

Question

How do I explain the last line of these?

>>> a = 1
>>> a is a
True
>>> a = [1, 2, 3]
>>> a is a
True
>>> a = np.zeros(3)
>>> a
array([ 0.,  0.,  0.])
>>> a is a
True
>>> a[0] is a[0]
False

I always thought that everything is at least "is" that thing itself!

Answer 1

NumPy doesn't store array elements as Python objects. If you try to access an individual element, NumPy has to create a new wrapper object to represent the element, and it has to do this every time you access the element. The wrapper objects from two accesses to a[0] are different objects, so a[0] is a[0] returns False.