Python an Immutable Object
Summary
TLDRThis video delves into the concept of object immutability in Python, using the example of an integer object with the value 2. It clarifies the misconception that variables can change by demonstrating that when 'first_number' is reassigned the value 5, a new object is created and the reference is updated, leaving the original object unreferenced and eligible for garbage collection. The video emphasizes the importance of understanding object references and the role of the garbage collector in managing memory, encouraging viewers to revisit these concepts as they advance in their Python journey.
Takeaways
- 🔑 The video assumes viewers have seen the previous one, which introduced key concepts for understanding this video's content.
- 📚 The previous video explained how assigning a value to a variable creates an execution space, an object reference, and an object, binding them together.
- 🔢 The video discusses the concept of an 'integer object', which is immutable, meaning its value cannot be changed once created.
- 🤔 The notion of immutability may seem counterintuitive when we can change the value of a variable in Python, but the video clarifies this by explaining the process of object reference reassignment.
- 🎯 When a variable is reassigned a new value, Python creates a new object for that value, and the object reference is updated to point to the new object, leaving the old object unbound.
- 🗑️ Unbound objects are eventually cleaned up by Python's garbage collector, which frees up memory space by removing objects that are no longer referenced.
- 🧩 The video uses the analogy of an execution space to illustrate how Python manages memory and object references, providing a deeper understanding of what happens 'under the hood'.
- 📈 The video encourages viewers to start with a simpler model of variables and objects and to revisit the concept of immutability as their understanding of Python deepens.
- 🔄 The process of variable assignment and reassignment in Python involves more steps than might be initially apparent, including object creation and reference updating.
- 🔗 The video suggests visiting a supporting website and subscribing to the YouTube channel for further updates and resources on Python programming.
Q & A
What is the key concept introduced in the previous video that is essential for understanding this video?
-The key concept introduced in the previous video is the process of assigning values to variables, which involves creating an execution space, generating an object reference, and binding the object reference to an object with a specific value.
What does it mean for an object to be immutable in Python?
-An object being immutable in Python means that once it is created, its value cannot be changed. This is in contrast to the common perception of variable assignment where it appears that the value of a variable can be changed.
How does Python handle the assignment of a new value to a variable that was previously assigned to an immutable object?
-When a new value is assigned to a variable that was previously bound to an immutable object, Python creates a new object with the new value and updates the object reference to point to this new object, leaving the old object unbound and available for garbage collection.
What is the role of the garbage collector in Python's memory management?
-The garbage collector in Python is responsible for identifying and removing objects that are no longer referenced by any variables or object references, thus freeing up memory space for other programs to use.
Why is it important to understand the difference between the simple model of variable assignment and the more complex reality of object references and immutability?
-Understanding the difference between the simple model of variable assignment and the complex reality of object references and immutability is important because it provides a deeper insight into how Python manages memory and how variables actually work under the hood, which can help in writing more efficient and error-free code.
What happens when an object reference is overwritten with a new address during variable assignment?
-When an object reference is overwritten with a new address, the previous binding to the old object is removed, and the object reference now points to the new object. The old object, if no other references point to it, becomes eligible for garbage collection.
How does the concept of immutability relate to the performance and efficiency of Python programs?
-The concept of immutability can contribute to the performance and efficiency of Python programs by preventing unintended side effects and allowing for certain optimizations, such as the reuse of immutable objects and the avoidance of unnecessary memory allocation.
What is the practical implication of understanding the difference between the simple model of variable assignment and the actual process in Python?
-The practical implication of understanding the difference is that it helps programmers to anticipate how their code will behave, especially in complex scenarios involving data structures and object-oriented programming, leading to better design and debugging strategies.
Why might a programmer initially be confused by the concept of immutability when they can seemingly change the value of a variable?
-A programmer might initially be confused by the concept of immutability because the act of changing a variable's value in Python actually involves creating a new object and updating the object reference, rather than changing the value of the existing object, which can seem counterintuitive to the common understanding of variable assignment.
How can the understanding of object references and immutability help in managing memory more effectively in Python?
-Understanding object references and immutability can help in managing memory more effectively by allowing programmers to write code that minimizes unnecessary object creation and takes advantage of Python's garbage collection, thus optimizing memory usage and performance.
Outlines
🔗 Understanding Object References and Immutability
This paragraph discusses the concept of object references and immutability in Python. It explains that when a variable is assigned a value, an object reference is created, and the object is given a value. The object reference is bound to the object, and the object's value is stored in the execution space. The paragraph highlights that integer objects in Python are immutable, meaning their values cannot be changed once created. It uses an example to illustrate how assigning a new value to a variable does not change the original object but instead creates a new object with the new value. The concept of garbage collection is introduced to explain how Python manages memory by removing objects that are no longer referenced, thus freeing up memory space.
🔄 Simplifying Python's Execution Space for Beginners
The second paragraph serves as a reminder that while the detailed explanation of Python's execution space and immutability is accurate, it can be overwhelming for beginners. It suggests that starting with a simpler model of variables and assignments is sufficient for those new to Python. The paragraph reassures viewers that they can return to the more complex understanding of objects and object references later on. It also encourages viewers to visit the supporting website and subscribe to the YouTube channel for updates on new video uploads.
Mindmap
Keywords
💡Execution Space
💡Object Reference
💡Immutable
💡Garbage Collector
💡Assignment Statement
💡Variable
💡Memory Address
💡Binding
💡Integer Object
💡Reassignment
Highlights
Understanding Python's execution space and object references is crucial for grasping programming concepts.
The video assumes viewers have watched the previous one for foundational concepts.
Assigning a value to a variable creates an object reference and an object in the execution space.
The object reference is bound to the object, which holds the assigned value.
Integer objects in Python are immutable, meaning their values cannot be changed once created.
The concept of immutability may seem counterintuitive when variables appear to change.
When a variable is reassigned, a new object is created, and the reference is updated, not the original object.
The original object, now unreferenced, becomes eligible for garbage collection.
Garbage collection is Python's mechanism for cleaning up unused objects and freeing memory.
For beginners, it's acceptable to think of variables as mutable, but understanding immutability is important.
The video encourages revisiting the concept of immutability as one progresses in learning Python.
The video provides a simplified model for understanding Python entities and their relationships.
The video explains the process of variable assignment and reassignment in Python's execution space.
The video uses animations to illustrate the movement of values and references in memory.
The video emphasizes the importance of understanding how Python manages memory and objects.
The video suggests that viewers check out the supporting website and subscribe to the YouTube channel for updates.
Transcripts
for this video it is assumed you've
watched the previous one in the playlist
as it introduced concepts that are key
to understanding what goes on in this
particular video here so if we remember
very briefly the last video looked at
assigning to two available call first
number ie this program statements here
and we should know that produced an
execution space and we generated an
object reference and an object and the
object reference is given the name first
number and the object is given the value
of two and we then load up the object
reference with the address of the
location of the object in the execution
space and we say these are bound
together now there was a fuller
explanation as I say in the previous
video so you didn't quite know what was
going on there I strongly recommend you
stop this video we're gonna look at the
previous one now something important
happens here you see what we're dealing
with is an object which is referred to
as an integer object because we're
storing an integer in it ie 2 is an
example of an integer and this sounds a
bit bizarre but once this integer object
is created its value cannot be changed
the integer object is said to be
immutable meaning it cannot be changed
but this seems not to make much sense
for example if we just forget this
execution space for a moment and let's
have a look at this if I have a variable
called first number and I assigned that
to we've looked at a model before which
has a variable called first number and
this assignment statement copies the 2
into the variable and we can see the
animation moving the value into my model
of what a variable is if I then execute
this program statement or should I say
when Python now execute this we can see
the first number is being assigned 5 so
5 is copied to first number and of
course what this will mean is the 5 will
move and replace the 2 so that variable
has changed so what's all this
we've been referring to as being
immutable because we can clearly see
here that the variable was changed
ie it first of all store two and then it
start five well let's go back and have a
look at this a game with the execution
space here we can see first number
assigned to this will create this
execution space here and of course we
will have an object reference and the
knob ship being created and the object
reference is given the name first number
and the 2 is moved to the center of the
object to the core of the object Python
then arranges for the object reference
to have the address of the object as
represented by this arrow which points
to the object in the execution space so
these are then bound so what happens
here if I say first number is assigned 5
well what will actually happen is this
when this executes another object is
created and that object is given the
value 5 now keep your eye on the object
reference first number what will happen
is another address will come in over
writing what's there before now the
consequence here is first number is the
object reference and it no longer has
the address of the object that contains
2 so it can't be bound so that bound
line is removed because this object
reference is now bound to 5 as we can
see here now a careful look at the
object containing 2 shows that nothing
is bound to it so what we need to do we
need to mark this now the trouble is
because that is not bound to an object
reference we don't know where it is in
the execution space there is nothing to
point to it now if this was allowed to
happen
ie we create objects and then through
our code we end up changing the pointer
that appears in the object reference
will have objects cluttering up the
execution space so what python does and
as programmers we don't have to worry
about this he's done for us it's garbage
collector
now what does that mean well it simply
means that every now and then panting
goes around having a look at its
execution space ie it's kind of memory
earlier and says well I've got an object
here well this thing is clogging up the
memory and nobody seems to be pointing
to it I know what I'll do I'll get rid
of it and that's precisely what happens
I'm not getting rid of it out of the
memory simply frees up the memory frees
up space to be used by other programs
and this is quite important freeing up
memory space especially on computers
where you can do lots of processing
however we can simply go with this model
here where that is assigned to first
numb but when we have this statement
five is assigned to it so the question
is are we okay to go with this for now
when we're starting off on understanding
your Python entities of course we are we
could think of variables as being
changed as you see here but this notion
of something being immutable is quite
important and we'll come back to that as
and when we move through our
understanding of the Python language
just to remind you what actually happens
is this
that's quite a lot of things happening
isn't there you see first number was
assigned to and then first number was
assigned five so it looks like well
that's pretty straightforward but in
fact when we look at the execution space
and we consider what went on under the
bonnet or under the hood of Python we
can see it was a little bit more
involved than possibly you might have
been expecting
however there'll be no harm if you just
go with the simpler model for the time
being and return to this understanding
of objects and object references at some
later date
check out the supporting website for
these videos and consider subscribing to
the YouTube channel that you'll get an
automatic update every time I upload a
new video on Piper
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