Have you ever wondered why changing the value of one variable affects another variable in some cases but not in others? This phenomenon is related to the concepts of mutable and immutable objects in Python. Let's explore this with an example:
Consider the scenario where we have two variables x and y, both initially set to 7. When we assign y = x, both x and y refer to the same object in memory, which is the integer 7. Therefore, if we print the values of x and y, we'll see 7 for both:
x = 7
y = x
print(x) # Output: 7
print(y) # Output: 7
Now, let's say we update the value of x to 8:
x = 8
print(x) # Output: 8
print(y) # Output: 7
Surprisingly, even though we only changed the value of x, the value of y remains 7. This behavior might seem confusing at first glance, but it's crucial to understand the difference between mutable and immutable objects in Python.
Memory Allocation in Python
In Python, every object occupies some space in memory. When we create a variable and assign it a value, Python allocates memory for that value and stores a reference to the memory location where the value is stored.
In our example, when we initially set x = 7, Python allocates memory for the integer 7 and stores it at a certain memory address. Both x and y then reference this memory address.
When we update the value of x to 8, Python creates a new integer object 8 and allocates memory for it at a different memory address. The reference of x is then updated to point to this new memory address, while y still maintains its reference to the original memory address containing 7.
Mutable vs. Immutable Objects
In Python, objects are classified into two categories: mutable and immutable.
Mutable Objects: These are objects whose state can be modified after creation. Examples include lists, dictionaries, and sets.
Immutable Objects: These are objects whose state cannot be modified after creation. Examples include integers, floats, strings, and tuples.
In our example, integers are immutable objects. When we assign y = x, both x and y refer to the same integer object 7. However, when we update the value of x to 8, Python creates a new integer object 8 and updates the reference of x to point to this new object. Since y still points to the original object 7, its value remains unchanged.
Conclusion
Understanding the distinction between mutable and immutable objects is essential for writing efficient and bug-free Python code. Remember, when working with mutable objects, changes to one variable can affect other variables that reference the same object. On the other hand, changes to immutable objects create new objects, leaving existing references unchanged.
By mastering these concepts, you'll be better equipped to harness the full power of Python in your projects! Happy coding!
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