Why Do Multiple Python Objects Sometimes Share the Same ID?

Same ID for Multiple Objects: Python's Quirky Behavior

In Python, creating multiple objects without assigning them names can indeed result in them sharing the same ID. This apparent paradox stems from the fact that the ID of an object is only unique within its lifetime.

When you create a variable that refers to an object, such as someClass(), the object's lifetime begins when the variable is assigned and ends when it is out of scope. In the example you provided:

print(someClass())
print(someClass())

The two someClass() calls result in two objects, but they are both created and destroyed within the same call to print. Therefore, they share the same lifetime and can have the same ID.

This behavior is further accentuated in Python 3.3.3's use of reference counting for garbage collection. When an object's reference count reaches zero (i.e., no variables refer to it), it becomes eligible for deletion. In our example, the second call to someClass() causes the reference count of the first object to reach zero, making it eligible for immediate deallocation.

Since the next object to be allocated is placed in the same memory location as the first object, it ends up with the same ID. This explains why calling the same class multiple times in rapid succession can result in objects with the same ID.

If you require unique IDs for objects, you can overcome this behavior by explicitly assigning them to variables and holding onto them:

a = someClass()
b = someClass()
print(id(a))
print(id(b))

Here, a and b refer to separate objects with distinct IDs, even if they belong to the same class. Alternatively, you can implement your own class-specific ID mechanism to ensure uniqueness.

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