What is id() Function in Python?

Python id() is a built-in function that provides you with the unique identifier or memory address of an object. This identifier takes the form of a non-negative integer, ensuring its uniqueness and constancy throughout the object’s existence, unless changes to the object’s memory location are made. You’ll find the id() function useful for differentiating between various objects and for making comparisons based on their identities.

In practical situations, it’s essential to comprehend the syntax and parameter of the Python id() function. Grasping these aspects holds immense importance as they have a pivotal role in executing practical examples. Acquiring a strong grasp of the function’s syntax and parameter empowers you to fully harness its capabilities across diverse scenarios.

Python id() Syntax and Parameter

Now that you have a clear understanding of Python id(), let’s delve into the syntax of the id() function:


When you are working with Python id(), keep in mind that it requires a single parameter: the object for which you want to retrieve the ID. This parameter can have any data type, and you can provide variables, literals, or expressions that result in an object.

Now that you’ve comprehended the id() function’s syntax and parameter, let’s delve into its return value. This will provide you with a practical understanding of how the id() function operates in real-world scenarios.

Python id() Return Value

The id() function in Python returns the integer representation of the memory address where the provided object is stored. This memory address is distinct for every object and remains unchanged while the object exists. The result you get is of the int data type. Consider the following illustration:

Example Code
x = 42 x_id = id(x) print(f"Memory address of x: {x_id}")

Here, we first define a variable named x and assign it the value 42. Then, we use the id() function to retrieve the unique memory address of the object that x is pointing to. This memory address is stored in the variable x_id. Finally, we print out a message that displays the memory address of the object stored in x using an f-string, combining the text Memory address of x: with the value of x_id.

Memory address of x: 139815025657360

This helps you to see where the object is stored in the computer’s memory. Remember, each time you run this code, the memory address might differ.

As previously discussed, the id() function serves to fetch an object’s memory address. Now, let’s delve into different scenarios to enhance your comprehension of its functionalities. By studying these examples, you’ll develop a more profound insight into the id() function and its efficient application within Python programming.

I. Creation of id() Object

The creation of an id() object is automatic and intrinsic to every Python object you create. As soon as you create an object, Python assigns a unique ID to it, which is based on the object’s memory location. This process is transparent to you as a programmer, and you don’t need to explicitly manage id() objects. Consider the following example:

Example Code
name = "Harry" name_id = id(name) print(f"Memory address of 'name': {name_id}")

In this example, we’re exploring how the id() function works by using a string variable named name. First, we assign the value Harry to the name variable. Then, we use the id() function to obtain the memory address of the name object and store it in the name_id variable. Finally, we display the memory address using an f-string, which provides us with insight into where the name object is stored in the computer’s memory.

Memory address of ‘name’: 139934143454640

As you can see in the above example, the id() function allows you to retrieve the memory address of the object stored in the variable name.

II. Python id() for Object Identification

Every time you create an object in Python, whether it’s a basic integer, a complex data structure, or even a function, it finds a home in a particular spot in memory. This is where Python id() comes into play. It hands you the key to that exclusive memory address, essentially providing a distinct fingerprint for each object you work with. For example:

Example Code
x = 100 y = "Hello, Python Helper!" x_id = id(x) y_id = id(y) print(f"Memory address of x: {x_id}") print(f"Memory address of y: {y_id}") if x_id != y_id: print("The memory addresses are different, indicating distinct objects.") else: print("The memory addresses are the same, indicating the same object.")

In this example, we start by creating two objects: an integer x with the value 100, and a string y containing the message Hello, Python Helper!. To understand more about these objects, we use the id() function to retrieve their memory addresses and assign these values to x_id and y_id. Afterward, we print out the memory addresses of x and y using formatted strings. This gives us a glimpse into where these objects are stored in memory.

To delve deeper, we employ an if-else statement to compare the memory addresses stored in x_id and y_id. If the addresses are different, it indicates that x and y are distinct objects occupying separate memory locations. In such a case, we print a message stating that the memory addresses are different, pointing to distinct objects.

On the contrary, if the memory addresses are the same, it signifies that both x and y are referencing the same object in memory. In this scenario, we print a message affirming that the memory addresses are the same, indicating the same object being referred to.

Memory address of x: 139901383183696
Memory address of y: 139901382716240
The memory addresses are different, indicating distinct objects.

By examining this code, you gain insights into how the id() function helps you identify whether objects are the same or distinct based on their memory addresses.

Using id() with Different Object Types

The beauty of the id() function in Python lies in its universality across all object types. Whether you’re dealing with strings and floats the id() function is your faithful companion in unveiling their memory addresses. Let’s explore this with a hands-on examples:

I. Python id() with Strings

While using Python id() with strings, it allows you to retrieve the unique memory addresses of string objects. This helps you determine whether two string variables point to the same or different objects by comparing their memory addresses. Consider the following illustration:

Example Code
text1 = "Python" text2 = "id() Function" text1_id = id(text1) text2_id = id(text2) print(f"Memory address of 'Python': {text1_id}") print(f"Memory address of 'id() Function': {text2_id}") if text1_id != text2_id: print("The memory addresses are different, indicating distinct string objects.") else: print("The memory addresses are the same, indicating the same string object.")

In this example, We start by defining two string variables, text1 and text2, with the values Python and id() Function respectively. This creates two separate string objects in memory. Then, we proceed to use the id() function to retrieve the unique memory addresses of these string objects. We assign these memory addresses to the variables text1_id and text2_id.

With the memory addresses in hand, we utilize the print() function to display the memory addresses of both strings. The first print statement shows the memory address of Python using the text1_id variable, and the second print statement displays the memory address of id() Function using the text2_id variable.

The next step involves comparing the memory addresses using an if statement. If the memory addresses are not the same, we interpret this as an indication that the two strings are distinct objects stored separately in memory. Consequently, we print a message stating that the memory addresses are different, which essentially tells us that the strings are not the same object.

On the contrary, if the memory addresses are the same, we conclude that both variables are referring to the same string object stored in memory. This prompts us to print a message stating that the memory addresses are the same, indicating that the strings are the same object.

Memory address of ‘Python’: 140261655800176
Memory address of ‘id() Function’: 140261656002032
The memory addresses are different, indicating distinct string objects.

As you can see, this code showcase the utilization of the id() function on strings, revealing their individual memory addresses and indicating whether they are the same or different objects.

II. Python id() with Float

You can use Python id() with floating-point numbers, Just like string and other data types, the id() function offers you a backstage pass to the memory addresses where floating-point numbers are stored. By using the id() function with floats, you’re diving into the fascinating world of how Python manages the identities and locations of these values. For example:

Example Code
float_number1 = 3.14 float_number2 = -2.71 float_number1_id = id(float_number1) float_number2_id = id(float_number2) print(f"Memory address of float_number1: {float_number1_id}") print(f"Memory address of float_number2: {float_number2_id}")

For this example, we have two floating-point numbers, float_number1 and float_number2. We use the id() function to retrieve the memory addresses of these float objects and then print them out. This helps us to understand how Python assigns memory locations to floating-point numbers. Remember, the memory addresses will likely differ each time you run the code due to the dynamic nature of memory management.

Memory address of float_number1: 139682897418672
Memory address of float_number2: 139682897421968

This code provides a clear illustration of how the id() function can be used to explore the memory addresses of different even float objects.

Python id() Advanced Examples

In the following section, we will examine several advanced examples of Python id() function, highlighting its flexibility and wide range of applications.

I. Python id() with Set

Python id() flexibility shines as it navigates through the realm of sets. A set is an unordered collection of unique elements, and much like other Python objects, it has its own distinct memory address. Here’s a glimpse of the id() function in action with sets.

Example Code
def set_memory_address(data): data_id = id(data) return data_id even_numbers = {2, 4, 6, 8, 10} address = set_memory_address(even_numbers) print("Memory address of even numbers are are: ", address)

Here, we’ve crafted a custom function called set_memory_address() that accepts a parameter named data. Inside this function, we use the id() function to obtain the unique memory address of the provided data. Next, we have a set named even_numbers, containing the even numbers (2, 4, 6, 8, 10). By invoking the set_memory_address() function and passing the set even_numbers as an argument, we calculate and acquire the memory address of the set. We store this memory address in the variable address.

After this, we utilize the print() function to showcase the memory address of the even_numbers set. The message Memory address of even numbers are: is accompanied by the actual memory address retrieved through the set_memory_address() function.

Memory address of even numbers are are: 139952679714400

As you run this code, you’ll witness the id() function unveiling the memory address of your set, showcasing its power in identifying objects.

II. Python id() with Tuple

Tuples, with their immutability and ordered nature, offers you an interesting playground for the id() function. Let’s take a peek at how the id() function operates with tuples:

Example Code
def tuple_memory_address(elements): my_tuple = tuple(elements) tuple_id = id(my_tuple) return tuple_id prime_numbers = (2, 3, 5) address = tuple_memory_address(prime_numbers) print(f"Memory address of the tuple: {address}")

In this example, we’ve crafted a function here to dive into the world of tuples and memory addresses. We start by defining a function named tuple_memory_address(elements). It takes a tuple of elements as input. Inside the function, we create a tuple named my_tuple using the provided elements. This tuple encapsulates the elements and keeps them ordered and unchangeable.

Using the id() function, we then fetch the memory address of the my_tuple we just created and store it in the variable tuple_id. To wrap it up, the function returns the tuple_id, which is essentially the memory address of the tuple. Now, we’re all set to explore this function with a specific tuple of prime numbers.

In our case, we’ve got a tuple of prime numbers: (2, 3, 5). We call our tuple_memory_address() function, passing this prime number tuple as an argument. The function does its magic – it forms a tuple out of the elements, identifies its memory address, and hands it over to us. The final touch is printing out the result. With the f-string format, we display the memory address we just acquired.

Memory address of the tuple: 140533712518208

As you can observe in the above output, by using this approach you can easily access the memory address of tuple object.

III. Python id() for Custom Objects

Using the id() function with custom objects lets you unveil their unique memory addresses, giving you a way to distinguish them from each other. It’s like having a backstage pass to their hidden world. This comes in handy when you want to compare objects, track their individual identities, or perform specific operations based on their memory addresses. Consider the below example:

Example Code
class Book: def __init__(self, title, author): self.title = title self.author = author my_book = Book("The Python Odyssey", "Pythonista") print("Memory address of my_book:", id(my_book))

For this example, we’re venturing into the world of custom objects using a Python class called Book. As a team of learners, we’re diving into the code to understand how the id() function interacts with our custom objects.

First, we define a Book class with an __init__ method. This method acts as the constructor and takes two parameters, title and author. Inside the method, we assign these parameters to instance variables self.title and self.author. Next, we create an instance of our Book class called my_book. We initialize it with the title “The Python Odyssey” and the author “Pythonista“. This book instance is now like a unique object in the Python universe.

Now comes the fascinating part. We’re using the id() function to unveil the memory address of our my_book object. It’s as if we’re revealing the secret coordinates of where this custom object lives in the computer’s memory. The id() function lets us see this hidden address. When we run the code, we print out the memory address of my_book. It’s like we’re showing off the backstage pass that allows us to peek into the inner workings of our custom object. This memory address is a unique identifier for our book instance, setting it apart from other objects in the Python world.

Memory address of my_book: 139747180003968

By understanding this code, you are getting a glimpse of how Python handles the memory and identities of custom objects, enriching our knowledge of the Python programming landscape.

IV. Difference between id() and type() Functions

Python id() and type() Functions reveals the contrast between two essential functions: id() and type(). These functions offer unique insights into objects, serving distinct purposes. By studying the examples provided below, you’ll gain a clear understanding of how id() functions compared to type(). This discussion clarifies their distinct roles and functionalities, enhancing your comprehension and practical application in Python programming.

A. Python id() Function

As you observed in the above scenarios, which showcase the various functionalities of id() in different contexts, let’s now delve into another example that will provide you with a clearer understanding of the id() function over type() function. For example:

Example Code
class Car: def __init__(self, brand, model): self.brand = brand self.model = model def get_car_id(car): car_id = id(car) return car_id car1 = Car("Ford", "Mustang") car2 = Car("Tesla", "Model S") car1_id = get_car_id(car1) car2_id = get_car_id(car2) print(f"Memory address of {car1.brand} {car1.model}: {car1_id}") print(f"Memory address of {car2.brand} {car2.model}: {car2_id}")

Here, we’ve established a class termed Car, characterized by attributes like brand and model. By initializing instances, car1 and car2, representing the Ford Mustang and Tesla Model S respectively, we’ve showcased the flexibility of the id() function in unique object identification. The get_car_id() function accepts a car instance as input, employing the id() function to procure its specific memory address.

By presenting the memory addresses of each car instance, such as {car1.brand} {car1.model} and {car2.brand} {car2.model}, we’ve highlighted how the id() function distinguishes individual objects.

Memory address of Ford Mustang: 139811063825168
Memory address of Tesla Model S: 139811063824448

By utilizing this example, you can see firsthand how the id() function operates within the context of a class.

B. Python type() Function

The Python type() serves as a function to determine the data type of an object in Python. This function allows you to identify the classification of an object within the language’s type hierarchy. By utilizing the type() function, you can gain insights into the fundamental nature of the object. Consider the following illustration:

Example Code
city = "Paris" population = 2260341 landmarks = ["Eiffel Tower", "Louvre Museum", "Champs-Elysées"] city_type = type(city) population_type = type(population) landmarks_type = type(landmarks) print(f"The data type of '{city}' is {city_type}") print(f"The data type of {population} is {population_type}") print(f"The data type of {landmarks} is {landmarks_type}")

For this example, we’ve created variables that store different types of data. First, there’s a variable named city which holds the string Paris, then there’s population which holds the integer value 2260341, and finally, landmarks is a list containing famous landmarks in Paris.

We use the type() function to determine the data types of these variables. We assign the results to new variables: city_type, population_type, and landmarks_type. After that, we print out the results using formatted strings. So, for the city variable, we’re using the city_type to print its data type, which is a string. Similarly, for population, we’re using population_type to indicate that its data type is an integer. Lastly, for landmarks, we’re using landmarks_type to show that its data type is a list.

The data type of ‘Paris’ is <class ‘str’>
The data type of 2260341 is <class ‘int’>
The data type of [‘Eiffel Tower’, ‘Louvre Museum’, ‘Champs-Elysées’] is <class ‘list’>

This example essentially showcases how the type() function helps you to identify the data types of different objects in Python.

V. Handling Exceptions and Errors with id()

Like any function, Python id() might encounter situations that lead to exceptions or errors. For example, trying to obtain the id() of an undefined variable will result in a NameError. To handle such situations, you can use try and except blocks. For instance:

Example Code
try: undefined_variable = some_value obj_id = id(undefined_variable) print("Memory address of undefined_variable:", obj_id) except NameError: print("Oops! The variable is not defined.")

In this example, we’re attempting to access the memory address of an undefined variable using the id() function. We use a try block to execute the code inside it. Within the try block, there’s an assignment undefined_variable = some_value, where some_value is not defined anywhere in the code. As a result, a NameError exception is raised when trying to assign the value to undefined_variable.

We then catch this exception using the except block with the NameError exception type. Inside the except block, we print out an error message, indicating that the variable is not defined.

Oops! The variable is not defined.

As observed through this method, you can readily foresee the occurrence of a NameError and offer a user-friendly error message in its place.

Having gained a thorough understanding of Python id() function, its applications, and its adaptability in diverse situations, you now possess a solid groundwork. To enhance your understanding, let’s delve into some theoretical concepts that will prove incredibly valuable on your Python programming journey.

Advantages of id() Function

Below, you will explore a handful of benefits offered by the id() function, so let’s delve into these advantages:

I. Object Identification

With the id() function, you can easily distinguish and tell apart objects based on their unique memory addresses.

II. Effective Error Handling

You can anticipate and handle errors like NameError using the id() function, improving how errors are managed in your code.

III. Tracking Custom Objects

For your custom classes and objects, id() helps you track instances and understand their individuality in memory.

Unique Use Cases of the id() Function

The id() function has a range of unique use cases that make it an invaluable tool in your Python toolbox.

I. Object Identity Comparison

As illustrated throughout this article, the id() function is perfect for comparing whether two variables point to the same memory location, helping you discern object identity.

II. Memory Management Insights

By using the id() function, you can gain insights into Python’s memory optimization techniques, understanding when objects share memory addresses and when they don’t.

III. Debugging and Optimization

The id() function can aid in debugging by helping you identify whether an object is being duplicated or modified in place, which is especially useful for lists and other mutable objects.

Congratulations! on making it through this journey of exploring the Python id() function!  You’ve delved into the heart of this flexible and convenient built-in function, learning how it reveals the unique identifiers or memory addresses of objects. This hidden code lets you uncover the magic happening behind the scenes in Python.

Throughout this Python helper tutorial, you’ve discovered that Python id() function is like a backstage pass to the inner world of Python objects. It allows you to differentiate between various objects and compare them based on their identities. You’ve grasped the syntax and parameters of the id() function, empowering you to use it in different scenarios.

From strings and integers to sets and custom objects, you’ve witnessed how Python id() function adapts to various data types, unveiling the memory addresses where these objects reside. You’ve seen how it plays a crucial role in comparing object identities, tracking instances, and even aiding in debugging and optimization. By comparing the id() function to the type() function, you’ve gained insights into both their similarities and differences. You’ve become equipped to handle errors gracefully using try-except blocks, turning potential pitfalls into learning opportunities.

Remember, Python id() isn’t just about technicalities; it’s about empowerment. It promotes you to dive deeper into Python’s memory management, gain a more profound understanding of your code’s inner workings, and ultimately become a more confident and proficient Python programmer. With your newfound knowledge of the id() function, you’re well-prepared to explore the vast world of Python programming with a heightened awareness of object identities, memory addresses, and the magic that ties it all together. Keep exploring, keep coding, and keep pushing the boundaries of what you can achieve with Python!

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