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Is it fine to pass whole object to function just for it arguments [duplicate]

This question already has answers here:
What is the most preferred way to pass object attributes to a function in Python?
(5 answers)
Closed 2 months ago.
A class
class Test:
self.model = model
self.type = type
self.version = version
...
test = Test()
Functions
def get_type_1(test):
if test.model == "something" and test.type == "something" and type.version == "something"
return "value"
def get_type_2(model, type, version):
if model == "something" and type == "something" and version == "something"
return "value"
From the perspective of "clean code" which type of function should I use? I couch myself using type_1 when there are more arguments and type_2 where there is 1-2 of them. Which is making a logical mess in my program. Do I need to worry in Python about speed and memory passing class all the time?

Prefer the 1st form, for three reasons.
You're not shadowing the type builtin. (Trivial, could use alternate spelling type_)
More convenient for the caller, and for folks reading the calling code.
Those three things go together. Better to show that, with the representation.
When we speak of (model, type, version),
they could be nearly anything.
There's no clear relationship among them,
and no name to hang documentation upon.
OTOH the object may have well-understood constraints,
perhaps "model is never Edsel when version > 3".
We can consult the documentation,
and the implementation,
to understand the class invariants.
Sometimes mutability is a concern.
That is, a caller might have passed in an
object with foo(test), and then we're
worried that library routine foo might possibly have
changed model "Colt" to "Bronco".
Often the docs, implicit or explicit,
will make clear that such mutations
are out of bounds, they will not happen.
To make things very obvious with
minimal documentation burden, consider
using a named tuple
for those three fields in the example.
need to worry in Python about speed and memory passing class all the time?
No.
Python is about clarity of communicating a technical
idea to other humans. It is not about speed.
Recall Knuth's advice. If speed was a principal
concern, you would have already used
cProfile
to identify the hot spots that should be
implemented in e.g. Rust, cython, or C++.
Usually that only becomes important when you
notice you're often looping more than a thousand
or a million times.
Use dis.dis()
to disassemble your two functions.
Notice that caller1 pushed a single reference
to test, while caller2 spent more time and
more stack memory pushing three references.
Down in the target code, we still need to
chase three references, so that's mostly a wash.
If you pass an object with a dozen attributes,
of which just three will be used, that's no
burden on the bytecode interpreter, the other
nine are simply never touched.
It can be an intellectual burden on an engineer
maintaining the code, who might need to reason
about those nine and dismiss them as not a concern.
Another concern that a paranoid caller might have
about called library code relates to references.
Typically we expect the called routine will not
permanently hold a reference (or weakref) on
the passed test object, nor on attributes
such as test.version or test.version.history_dict.
If the library routine will store a reference for a
long time, or pass a reference to someone that will
store it, well, that's worth documenting.
Caller will want to understand memory consumption,
leaks, and object lifetime.

Python instance methods

I was ashamed of a question that occupied my mind, if it is possible and you have the opportunity, thank you for answering: that when we create a instance of a class, the methods of that instance object, especially that instance, are created with the instance (object) or i mean that to run a method, the address of that method in the class with object parameters is referred to as the method class, and if this is not done, it does not cause memory consuming? I did a lot of research on this subject, but I was not arrested much, and I wrote and executed this code:
class a:
def func1(self,name):
print("hello")
b=a()
c=a()
print(id(a.func1))
print(id(b.func1))
print(id(c.func1))
The address I got from the last two lines is exactly the same. The output was something like this:
76767678900
87665677888
87665677888
And why 2 last address is alike?
Thanks a lot

The first address corresponds to the original function (you accessed it on the class, so it didn't bind it, you just saw the address where the raw function itself was allocated).
The other two (identical) addresses are bound method objects. You immediately released the bound method it allocated, and CPython makes use of both per-type freelists (not sure if any involved here) and a small object allocator that will frequently return the same memory just freed if you ask for the same amount of memory immediately thereafter. If you extracted the underlying function from the bound method, e.g.:
print(id(b.func1.__func__))
you'd see it is the same as a.func1 (and that value will be stable, where the address of the bound methods could differ every time you bind them).
In short, ids are only unique within the current set of objects in the program; if you release one of those objects, its id could appear attached to some other newly allocated object immediately thereafter.

What is the reasoning behind Python using identifiers rather than variables, if there is any?

My previous question probably wasn't phrased the clearest. I didn't want to know what happened, I wanted to know why the language itself uses this philosophy. Was it an arbitrary choice, or is there some interesting history between this design choice?
The answers that the question was marked as a duplicate to simply stated that Python does this but didn't explain whether there was any reasoning behind it.

If you know C and C++, you know what pointers and references are. In Java or Python, you have two kinds of things. On one side the native numeric types (integers, characters and floating points) and on the other the complex ones which derive from a basic empty type object.
In fact, the native types are the ones that can fit into a CPU register, and for that reason they are processed as values. But object (sub-)types often require a complex memory frame. For that reason, a register can only contain a pointer to them, and for that reason they are processed as references. The nice point with references for languages that provide a garbage collector, is that they are processed the same as a C++ shared_pointer: the system maintains a reference count, and when the reference count reaches 0, the object can be freed by the GC.
C has a very limited notion of object (struct) and in early K&R versions from the 1970s, you could only process them element by element or as a whole with memcopy but could neither return from a function nor assign them nor pass them by value. The ability to pass struct by values was added into ANSI C during the 1980s, to make the language more object friendly. C++ being from the beginning an object language, allowed to pass objects by value, and the smart pointers shared_ptr and unique_ptr were added to the standard library to allow to easily use references to objects because copying a large object is an expensive operation.
Python (like java) being a post-C++ language decided from the beginning that objects would be processed as references with a reference counter and deleted by a garbage collector when the ref count reaches 0. That way assigning objects is a cheap operation, and the programmer has never to explicitely delete anything.

When to Py_INCREF?

I'm working on a C extension and am at the point where I want to track down memory leaks. From reading Python's documentation it's hard to understand when to increment / decrement reference count of Python objects. Also, after couple days spending trying to embed Python interpreter (in order to compile the extension as a standalone program), I had to give up this endeavor. So, tools like Valgrind are helpless here.
So far, by trial and error I learned that, for example, Py_DECREF(Py_None) is a bad thing... but is this true of any constant? I don't know.
My major confusions so far can be listed like this:
Do I have to decrement refcount on anything created by PyWhatever_New() if it doesn't outlive the procedure that created it?
Does every Py_INCREF need to be matched by Py_DECREF, or should there be one more of one / the other?
If a call to Python procedure resulted in a PyObject*, do I need to increment it to ensure that I can still use it (forever), or decrement it to ensure that eventually it will be garbage-collected, or neither?
Are Python objects created through C API on the stack allocated on stack or on heap? (It is possible that Py_INCREF reallocates them on heap for example).
Do I need to do anything special to Python objects created in C code before passing them to Python code? What if Python code outlives C code that created Python objects?
Finally, I understand that Python has both reference counting and garbage collector: if that's the case, how critical is it if I mess up the reference count (i.e. not decrement enough), will GC eventually figure out what to do with those objects?

Most of this is covered in Reference Count Details, and the rest is covered in the docs on the specific questions you're asking about. But, to get it all in one place:
Py_DECREF(Py_None) is a bad thing... but is this true of any constant?
The more general rule is that calling Py_DECREF on anything you didn't get a new/stolen reference to, and didn't call Py_INCREF on, is a bad thing. Since you never call Py_INCREF on anything accessible as a constant, this means you never call Py_DECREF on them.
Do I have to decrement refcount on anything created by PyWhatever_New()
Yes. Anything that returns a "new reference" has to be decremented. By convention, anything that ends in _New should return a new reference, but it should be documented anyway (e.g., see PyList_New).
Does every Py_INCREF need to be matched by Py_DECREF, or should there be one more of one / the other?
The number in your own code may not necessarily balance. The total number has to balance, but there are increments and decrements happening inside Python itself. For example, anything that returns a "new reference" has already done an inc, while anything that "steals" a reference will do the dec on it.
Are Python objects created through C API on the stack allocated on stack or on heap? (It is possible that Py_INCREF reallocates them on heap for example).
There's no way to create objects through C API on the stack. The C API only has functions that return pointers to objects.
Most of these objects are allocated on the heap. Some are actually in static memory.
But your code should not care anyway. You never allocate or delete them; they get allocated in the PySpam_New and similar functions, and deallocate themselves when you Py_DECREF them to 0, so it doesn't matter to you where they are.
(The except is constants that you can access via their global names, like Py_None. Those, you obviously know are in static storage.)
Do I need to do anything special to Python objects created in C code before passing them to Python code?
No.
What if Python code outlives C code that created Python objects?
I'm not sure what you mean by "outlives" here. Your extension module is not going to get unloaded while any objects depend on its code. (In fact, until at least 3.8, your module probably never going to get unloaded until shutdown.)
If you just mean the function that _New'd up an object returning, that's not an issue. You have to go very far out of your way to allocate any Python objects on the stack. And there's no way to pass things like a C array of objects, or a C string, into Python code without converting them to a Python tuple of objects, or a Python bytes or str. There are a few cases where, e.g., you could stash a pointer to something on the stack in a PyCapsule and pass that—but that's the same as in any C program, and… just don't do it.
Finally, I understand that Python has both reference counting and garbage collector
The garbage collector is just a cycle breaker. If you have objects that are keeping each other alive with a reference cycle, you can rely on the GC. But if you've leaked references to an object, the GC will never clean it up.

Why is the id of a Python class not unique when called quickly?

I'm doing some things in Python (3.3.3), and I came across something that is confusing me since to my understanding classes get a new id each time they are called.
Lets say you have this in some .py file:
class someClass: pass
print(someClass())
print(someClass())
The above returns the same id which is confusing me since I'm calling on it so it shouldn't be the same, right? Is this how Python works when the same class is called twice in a row or not? It gives a different id when I wait a few seconds but if I do it at the same like the example above it doesn't seem to work that way, which is confusing me.
>>> print(someClass());print(someClass())
<__main__.someClass object at 0x0000000002D96F98>
<__main__.someClass object at 0x0000000002D96F98>
It returns the same thing, but why? I also notice it with ranges for example
for i in range(10):
print(someClass())
Is there any particular reason for Python doing this when the class is called quickly? I didn't even know Python did this, or is it possibly a bug? If it is not a bug can someone explain to me how to fix it or a method so it generates a different id each time the method/class is called? I'm pretty puzzled on how that is doing it because if I wait, it does change but not if I try to call the same class two or more times.

The id of an object is only guaranteed to be unique during that object's lifetime, not over the entire lifetime of a program. The two someClass objects you create only exist for the duration of the call to print - after that, they are available for garbage collection (and, in CPython, deallocated immediately). Since their lifetimes don't overlap, it is valid for them to share an id.
It is also unsuprising in this case, because of a combination of two CPython implementation details: first, it does garbage collection by reference counting (with some extra magic to avoid problems with circular references), and second, the id of an object is related to the value of the underlying pointer for the variable (ie, its memory location). So, the first object, which was the most recent object allocated, is immediately freed - it isn't too surprising that the next object allocated will end up in the same spot (although this potentially also depends on details of how the interpreter was compiled).
If you are relying on several objects having distinct ids, you might keep them around - say, in a list - so that their lifetimes overlap. Otherwise, you might implement a class-specific id that has different guarantees - eg:
class SomeClass:
next_id = 0
def __init__(self):
self.id = SomeClass.nextid
SomeClass.nextid += 1

If you read the documentation for id, it says:
Return the “identity” of an object. This is an integer which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id() value.
And that's exactly what's happening: you have two objects with non-overlapping lifetimes, because the first one is already out of scope before the second one is ever created.
But don't trust that this will always happen, either. Especially if you need to deal with other Python implementations, or with more complicated classes. All that the language says is that these two objects may have the same id() value, not that they will. And the fact that they do depends on two implementation details:
The garbage collector has to clean up the first object before your code even starts to allocate the second object—which is guaranteed to happen with CPython or any other ref-counting implementation (when there are no circular references), but pretty unlikely with a generational garbage collector as in Jython or IronPython.
The allocator under the covers have to have a very strong preference for reusing recently-freed objects of the same type. This is true in CPython, which has multiple layers of fancy allocators on top of basic C malloc, but most of the other implementations leave a lot more to the underlying virtual machine.
One last thing: The fact that the object.__repr__ happens to contain a substring that happens to be the same as the id as a hexadecimal number is just an implementation artifact of CPython that isn't guaranteed anywhere. According to the docs:
If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form <...some useful description…> should be returned.
The fact that CPython's object happens to put hex(id(self)) (actually, I believe it's doing the equivalent of sprintf-ing its pointer through %p, but since CPython's id just returns the same pointer cast to a long that ends up being the same) isn't guaranteed anywhere. Even if it has been true since… before object even existed in the early 2.x days. You're safe to rely on it for this kind of simple "what's going on here" debugging at the interactive prompt, but don't try to use it beyond that.

I sense a deeper problem here. You should not be relying on id to track unique instances over the lifetime of your program. You should simply see it as a non-guaranteed memory location indicator for the duration of each object instance. If you immediately create and release instances then you may very well create consecutive instances in the same memory location.
Perhaps what you need to do is track a class static counter that assigns each new instance with a unique id, and increments the class static counter for the next instance.

It's releasing the first instance since it wasn't retained, then since nothing has happened to the memory in the meantime, it instantiates a second time to the same location.

Try this, try calling the following:
a = someClass()
for i in range(0,44):
print(someClass())
print(a)
You'll see something different. Why? Cause the memory that was released by the first object in the "foo" loop was reused. On the other hand a is not reused since it's retained.

A example where the memory location (and id) is not released is:
print([someClass() for i in range(10)])
Now the ids are all unique.