If you have only the one class and you only read the variable, you won't see a lot of difference.

If you take into account the possibility of subclasses that override class_var, then self.class_var will look in the current class first while Foo.class_var will continue to refer concretely to that particular value. The best practice depends on your intent but since using classes in a language that makes them optional implies at least some interest in polymorphism, self is probably more generally useful.

Also, if you want to set the value, self.whatever = will set an instance variable and Foo.whatever = will set the class variable which could then affect any other instance as well.

Arguments are passed by assignment. The rationale behind this is twofold:

1. the parameter passed in is actually a reference to an object (but the reference is passed by value)
2. some data types are mutable, but others aren't

So:

• If you pass a mutable object into a method, the method gets a reference to that same object and you can mutate it to your heart's delight, but if you rebind the reference in the method, the outer scope will know nothing about it, and after you're done, the outer reference will still point at the original object.

• If you pass an immutable object to a method, you still can't rebind the outer reference, and you can't even mutate the object.

To make it even more clear, let's have some examples.

List - a mutable type

Let's try to modify the list that was passed to a method:

def try_to_change_list_contents(the_list):
    print('got', the_list)
    the_list.append('four')
    print('changed to', the_list)

outer_list = ['one', 'two', 'three']

print('before, outer_list =', outer_list)
try_to_change_list_contents(outer_list)
print('after, outer_list =', outer_list)

Output:

before, outer_list = ['one', 'two', 'three']
got ['one', 'two', 'three']
changed to ['one', 'two', 'three', 'four']
after, outer_list = ['one', 'two', 'three', 'four']

Since the parameter passed in is a reference to outer_list, not a copy of it, we can use the mutating list methods to change it and have the changes reflected in the outer scope.

Now let's see what happens when we try to change the reference that was passed in as a parameter:

def try_to_change_list_reference(the_list):
    print('got', the_list)
    the_list = ['and', 'we', 'can', 'not', 'lie']
    print('set to', the_list)

outer_list = ['we', 'like', 'proper', 'English']

print('before, outer_list =', outer_list)
try_to_change_list_reference(outer_list)
print('after, outer_list =', outer_list)

Output:

before, outer_list = ['we', 'like', 'proper', 'English']
got ['we', 'like', 'proper', 'English']
set to ['and', 'we', 'can', 'not', 'lie']
after, outer_list = ['we', 'like', 'proper', 'English']

Since the the_list parameter was passed by value, assigning a new list to it had no effect that the code outside the method could see. The the_list was a copy of the outer_list reference, and we had the_list point to a new list, but there was no way to change where outer_list pointed.

String - an immutable type

It's immutable, so there's nothing we can do to change the contents of the string

Now, let's try to change the reference

def try_to_change_string_reference(the_string):
    print('got', the_string)
    the_string = 'In a kingdom by the sea'
    print('set to', the_string)

outer_string = 'It was many and many a year ago'

print('before, outer_string =', outer_string)
try_to_change_string_reference(outer_string)
print('after, outer_string =', outer_string)

Output:

before, outer_string = It was many and many a year ago
got It was many and many a year ago
set to In a kingdom by the sea
after, outer_string = It was many and many a year ago

Again, since the the_string parameter was passed by value, assigning a new string to it had no effect that the code outside the method could see. The the_string was a copy of the outer_string reference, and we had the_string point to a new string, but there was no way to change where outer_string pointed.

I hope this clears things up a little.

EDIT: It's been noted that this doesn't answer the question that @David originally asked, "Is there something I can do to pass the variable by actual reference?". Let's work on that.

How do we get around this?

As @Andrea's answer shows, you could return the new value. This doesn't change the way things are passed in, but does let you get the information you want back out:

def return_a_whole_new_string(the_string):
    new_string = something_to_do_with_the_old_string(the_string)
    return new_string

# then you could call it like
my_string = return_a_whole_new_string(my_string)

If you really wanted to avoid using a return value, you could create a class to hold your value and pass it into the function or use an existing class, like a list:

def use_a_wrapper_to_simulate_pass_by_reference(stuff_to_change):
    new_string = something_to_do_with_the_old_string(stuff_to_change[0])
    stuff_to_change[0] = new_string

# then you could call it like
wrapper = [my_string]
use_a_wrapper_to_simulate_pass_by_reference(wrapper)

do_something_with(wrapper[0])

Although this seems a little cumbersome.

You need to use the full classname to set class variables. cls in double_x and tripple_x will refer to subclasses (ObjectOne and ObjectTwo, respectively), and setting attributes on those subclasses will store new variables, not alter the class variable BaseObject.x. You can only alter base class variables by directly accessing them.

Using your code, we get:

>>> obj_1 = ObjectOne()
cls.initialized = False
>>> obj_1.double_x()
2
>>> obj_2 = ObjectTwo()
cls.initialized = False
>>> obj_2.triple_x()
3
>>> BaseObject.x
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: type object 'BaseObject' has no attribute 'x'
>>> BaseObject.initialized, ObjectOne.initialized, ObjectOne.x, ObjectTwo.initialized, ObjectTwo.x
(False, True, 2, True, 3)

What happened is that in _initialize(), cls was set to ObjectOne or ObjectTwo, depending on what instance you created, and each subclass got their own copies of the variables initialized and x.

Using BaseObject._initialize() (to ensure that BaseObject is initialized, and not the subclasses) gives:

>>> obj_1 = ObjectOne()
cls.initialized = False
>>> obj_1.double_x()
2
>>> obj_2 = ObjectTwo()
cls.initialized = True
>>> obj_2.triple_x()
3
>>> BaseObject.x, ObjectOne.x, ObjectTwo.x
(1, 2, 3)
>>> BaseObject.initialized
True
>>> 'x' in ObjectOne.__dict__
True
>>> 'initialized' in ObjectOne.__dict__
False
>>> 'initialized' in ObjectTwo.__dict__
False

So now _initialize() used BaseObject as the target to set initialized and the initial value for x, but double_x and triple_x still used their own subclasses to set the new value of x and are not sharing that value through BaseObject.

The only option you have to set class variables on a specific base class is to refer to it directly in all class methods:

class BaseObject(object):
    initialized = False
    def __init__(self):
        BaseObject._initialize()

    @classmethod
    def _initialize(cls):
        print "cls.initialized = "+str(cls.initialized)
        if not cls.initialized:
            cls.x = 1
            cls.initialized = True
class ObjectOne(BaseObject):
    @classmethod
    def double_x(cls):
        BaseObject.x = BaseObject.x * 2
        print cls.x

class ObjectTwo(BaseObject):
    @classmethod
    def triple_x(cls):
        BaseObject.x = BaseObject.x * 3
        print cls.x

which would give:

>>> obj_1 = ObjectOne()
cls.initialized = False
>>> obj_1.double_x()
2
>>> obj_2 = ObjectTwo()
cls.initialized = True
>>> obj_2.triple_x()
6

Note that I called BaseObject._initialize() to make sure that cls is BasObject and not a subclass. Then, when setting x the double_x and triple_x methods still refer directly to BaseObject to ensure that the variable is set directly on the base class. When reading the value of x the above example still uses cls, which uses the class MRO to find x on the base class when not set locally.

One approach would be a class attribute of pre-defined positions, then when Mouse.move_mouse is called it either tries to retrieve the pos from that dictionary, assuming it's a key, or uses it directly if it isn't a key:

class Mouse:

    POSITIONS = {'home': (0, 0), ...}

    def move_mouse(self, pos):
        """move the mouse to the specified coordinates"""
        x, y = self.POSITIONS.get(pos, pos)
        ...

Now both mouse.move_mouse('home') and mouse.move_mouse((0, 0)) would have the same effect.

Class body in python is an executable context, not like Java that only contains declaration. What this ultimately means is that sequence of execution is important within a class definition.

To quote the documentation:

class definition is an executable statement.

...

The class’s suite is then executed in a new execution frame (see Naming and binding), using a newly created local namespace and the original global namespace. (Usually, the suite contains mostly function definitions.) When the class’s suite finishes execution, its execution frame is discarded but its local namespace is saved. [4] A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace.

Some more lengthier explanations.

If you want to call a function to define a class variable, you can do it with one of these ways:

1. use staticmethod:

   class MyClass:
       def _run_instance_method():
           return "ran instance method"
       run_instance_method = staticmethod(_run_instance_method)
   
       class_var_1 = "a"
       class_var_2 = _run_instance_method() # or run_instance_method.__func__()
   

2. or define it as a standalone function:

   def run_method():
       return "ran method"
   
   class MyClass:
       class_var_1 = "a"
       class_var_2 = run_method()
   
       # optional
       run_method = staticmethod(run_method)
   

3. or access the original function with __func__ and provide a dummy cls value:

   class MyClass:
       @classmethod
       def run_class_method(cls):
           return "ran class method"
   
       class_var_1 = "a"
       class_var_2 = run_class_method.__func__(object())
   

4. or set the class variables after class creation:

   class MyClass:
       @classmethod
       def run_class_method(cls):
           return "ran class method"
   
       class_var_1 = "a"
   
   MyClass.class_var_2 = MyClass.run_class_method()
   

No, you cannot. As other answer point out, you can (ab?)use aliasing of mutable objects to achieve a similar effect. However, that's not the same thing as C++ references, and I want to explain what actually happens to avoid any misconceptions.

You see, in C++ (and other languages), a variable (and object fields, and entries in collections, etc.) is a storage location and you write a value (for instance, an integer, an object, or a pointer) to that location. In this model, references are an alias for a storage location (of any kind) - when you assign to a non-reference variable, you copy a value (even if it's just a pointer, it's still a value) to the storage location; when you assign to a reference, you copy to a storage location somewhere else. Note that you cannot change a reference itself - once it is bound (and it has to as soon as you create one) all assignments to it alter not the reference but whatever is referred to.

In Python (and other languages), a variable (and object fields, and entries in collections, etc.) is a just a name. Values are somewhere else (e.g. sprinkled all over the heap), and a variable refers (not in the sense of C++ references, more like a pointer minus the pointer arithmetic) to a value. Multiple names can refer to the same value (which is generally a good thing). Python (and other languages) calls whatever is needed to refer to a value a reference, despite being pretty unrelated to things like C++ references and pass-by-reference. Assigning to a variable (or object field, or ...) simply makes it refer to another value. The whole model of storage locations does not apply to Python, the programmer never handles storage locations for values. All he stores and shuffles around are Python references, and those are not values in Python, so they cannot be target of other Python references.

All of this is independent of mutability of the value - it's the same for ints and lists, for instance. You cannot take a variable that refers to either, and overwrite the object it points to. You can only tell the object to modify parts of itself - say, change some reference it contains.

Is this a more restrictive model? Perhaps, but it's powerful enough most of the time. And when it isn't you can work around it, either with a custom class like the one given below, or (equivalent, but less obvious) a single-element collection.

class Reference:
    def __init__(self, val):
        self._value = val # just refers to val, no copy

    def get(self):
        return self._value

    def set(self, val):
        self._value = val

That still won't allow you to alias a "regular" variable or object field, but you can have multiple variables referring to the same Reference object (ditto for the mutable-singleton-collection alternative). You just have to be careful to always use .get()/.set() (or [0]).