Why Do Some Python Objects Share the Same ID?

Unveiling the Inconsistencies in Object ID Generation in Python

Creating multiple objects of the same class in Python without assigning names to them might lead to an intriguing observation: these objects sometimes share the same ID. This behavior may seem counterintuitive as one would expect each object instance to possess a unique ID.

The key to understanding this phenomenon lies in the nature of object lifetimes in Python. An object's ID is only guaranteed to be exclusive within its lifetime. In the case of unname objects created in rapid succession within a single print() call, their lifetimes do not overlap. Thus, it becomes feasible for subsequent instances to occupy the same memory location, resulting in the same ID.

This behavior arises from a combination of CPython implementation nuances. Firstly, Python uses reference counting to manage memory, and secondly, object IDs are linked to the memory address of the variable holding the object. After the initial object is created and immediately deallocated in the print() call, the next object is assigned the freed memory location, leading to the duplicate ID.

To overcome this inconsistency, it may be necessary to extend the lifetimes of the objects, for example by storing them in a list. Alternatively, one can create custom class-specific IDs that offer distinct guarantees. By implementing such measures, it is possible to ensure that each object instance has a unique identifier.

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