In Python, every value you work with—whether it is an integer, string, list, or even a function—is represented internally as an object. This object-oriented approach means that variables are simply names that refer to objects stored in memory. When you assign a value to a variable, Python creates an object in memory and associates the variable name with that object. The memory for these objects is managed automatically, so you do not need to allocate or free memory manually as you might in lower-level languages like C.

When you create a new variable or data structure, Python allocates memory for the object using its built-in memory manager. This memory manager handles the allocation and deallocation of memory blocks, ensuring that objects have enough space and that memory is reused efficiently. The memory address where an object is stored can be inspected using the built-in id() function, which returns a unique identifier for the object during its lifetime.

Python also tracks how many references exist to each object in memory. This is done using a technique called reference counting. Every time a new variable points to an object, the reference count increases. When a reference is deleted or reassigned, the count decreases. When the reference count drops to zero, Python knows that the object is no longer needed and can safely reclaim the memory it was using. This process is crucial for efficient memory management and helps prevent memory leaks.

Understanding reference counting can help you write better code and avoid common pitfalls such as accidentally holding onto objects longer than necessary. The following example demonstrates how reference counts change as you create and delete references to the same object.