id()

The built-in id() function returns the identity of an object, which is a unique and constant integer that identifies the object during its lifetime.

In CPython, this identity corresponds to the memory address where the object resides:

Python 
>>> id(42)
4315605776

>>> id("Python")
4315120464

id() Signature

Python Syntax 
id(object)

Arguments

Argument Description
object The object to query

Return Value

  • Returns an integer representing the identity of the input object.

id() Examples

With an integer as an argument:

Python 
>>> id(42)
4343440904

With a float as an argument:

Python 
>>> id(3.14)
4376764112

With a list as an argument:

Python 
>>> id([1, 2, 3])
4399577728

id() Common Use Cases

The most common use cases for the id() function include:

  • Debugging to check if two variables point to the same object
  • Understanding object lifecycle and memory management
  • Inspecting how Python handles object references

id() Real-World Example

Say you want to implement a Fibonacci-like sequence with a caching mechanism to store computed values. You can use the id() function to verify that cached values are reused:

Python fibonacci.py 
class Fibonacci:
    def __init__(self, initial_value=1):
        self._cache = [0, initial_value]

    def __call__(self, index):
        if index < len(self._cache):
            fib_number = self._cache[index]
            print(f"{index} {fib_number} id = {id(fib_number)}")
        else:
            fib_number = self(index - 1) + self(index - 2)
            self._cache.append(fib_number)
        return fib_number

fibonacci_333 = Fibonacci(333)
fibonacci_333(2)
fibonacci_333(4)

This example prints the identity of each number in the sequence, allowing you to verify that the same objects are used when expected, confirming the cache’s effectiveness.

Memory Management in Python

Tutorial

Memory Management in Python

Get ready for a deep dive into the internals of Python to understand how it handles memory management. By the end of this article, you’ll know more about low-level computing, understand how Python abstracts lower-level operations, and find out about Python’s internal memory management algorithms.

intermediate python

For additional information on related topics, take a look at the following resources:

By Leodanis Pozo Ramos • Updated Nov. 22, 2024 • Reviewed by Dan Bader
Python’s Built-in Functions Share Feedback