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where to declare object variables

Which of the following cases is the best practice way of declaring an instance variable in python. Is there a typical preference, and what are the justifications for this?
Option 1 - Declare within __init__
class MyObject:
def __init__(self, arg):
self.variable_1 = self.method_1(arg)
def method_1(self, arg):
return(arg)
Option 2 - Declare in other methods
class MyObject:
def __init__(self, arg):
self.method_1(arg)
def method_1(self, arg):
self.variable_1 = arg
This is purely to understand if there is a best practice way of doing this that other developers would prefer to see when reviewing and extending code.

This is obviously not exact science, but it generally makes more sense to set all attributes (as possible) in the constructor so that you can follow up on them.
You can, of course, change them later as necessary in other methods.
Setting constructor level variables everywhere in the class makes it very hard to understand where things are coming from.

Option 1 is best practice to declare instance variable in Python.
Instance variables are for data that is actually part of the instance so it would be better if you define in constructor.

Your Option 2 is basically a Setter-/Getter-Paradigm. Python uses properties for these use-cases. There's a nice SO-answer for a similar question.
In general you initialize all your Instance-variables in the __init__-method, that's its reason to exist. If you need a getter-/setter use properties. And use the "least-astonishment" principle. Do not surprise another reader, or your later self with overly clever and/or complicated solutions. (aka KISS principle)

It depends. Defining all the attributes inside __init__ itself generally makes the code more readable, but if the class has a lot of attributes and you can easily divide them into logical groups then it makes sense to initialise each group of attributes in its own initialising method. You may wish to indicate that such methods are private by giving them a name that commences with a single underscore.
Note that if the class is derived from one or more other classes (apart from object) then you will have to call super.__init__ to initialise the attributes inherited from the parent class(es).
The bottom line is that all instance attributes should exist by the time that __init__ finishes executing. If it's not possible to set a proper value for some attribute in __init__ then it should be set to an appropriate default value, eg an empty string, list, etc, None, or a sentinel value like object().
Of course, the above doesn't apply to #property attributes, but even those will generally have an underlying "private" attribute that should be set in __init__.
For more info about properties, please see Raymond Hettinger's excellent Descriptor HowTo Guide in the Python docs.
As juanpa.arrivillaga mentions in the question comments, we don't actually declare variables in Python. That's basically because the Python data model doesn't really have variables like C and many other languages do. For a succinct explanation with nice diagrams please see Other languages have "variables", Python has "names". Also see Facts and myths about Python names and values, which was written by SO veteran Ned Batchelder.

What's the best way to extend the functionality of factory-produced classes outside of the module in python?

I've been reading lots of previous SO discussions of factory functions, etc. and still don't know what the best (pythonic) approach is to this particular situation. I'll admit up front that i am imposing a somewhat artificial constraint on the problem in that i want my solution to work without modifying the module i am trying to extend: i could make modifications to it, but let's assume that it must remain as-is because i'm trying to understand best practice in this situation.
I'm working with the http://pypi.python.org/pypi/icalendar module, which handles parsing from and serializing to the Icalendar spec (hereafter ical). It parses the text into a hierarchy of dictionary-like "component" objects, where every "component" is an instance of a trivial derived class implementing the different valid ical types (VCALENDAR, VEVENT, etc.) and they are all spit out by a recursive factory from the common parent class:
class Component(...):
#classmethod
def from_ical(cls, ...)
I have created a 'CalendarFile' class that extends the ical 'Calendar' class, including in it generator function of its own:
class CalendarFile(Calendar):
#classmethod
def from_file(cls, ics):
which opens a file (ics) and passes it on:
instance = cls.from_ical(f.read())
It initializes and modifies some other things in instance and then returns it. The problem is that instance ends up being a Calendar object instead of a CalendarFile object, in spite of cls being CalendarFile. Short of going into the factory function of the ical module and fiddling around in there, is there any way to essentially "recast" that object as a 'CalendarFile'?
The alternatives (again without modifying the original module) that I have considered are:make the CalendarFile class a has-a Calendar class (each instance creates its own internal instance of a Calendar object), but that seems methodically stilted.
fiddle with the returned object to give it the methods it needs (i know there's a term for creating a customized object but it escapes me).
make the additional methods into functions and just have them work with instances of Calendar.
or perhaps the answer is that i shouldn't be trying to subclass from a module in the first place, and this type of code belongs in the module itself.
Again i'm trying to understand what the "best" approach is and also learn if i'm missing any alternatives. Thanks.

Normally, I would expect an alternative constructor defined as a classmethod to simply call the class's standard constructor, transforming the arguments that it receives into valid arguments to the standard constructor.
>>> class Toy(object):
... def __init__(self, x):
... self.x = abs(x)
... def __repr__(self):
... return 'Toy({})'.format(self.x)
... #classmethod
... def from_string(cls, s):
... return cls(int(s))
...
>>> Toy.from_string('5')
Toy(5)
In most cases, I would strongly recommend something like this approach; this is the gold standard for alternative constructors.
But this is a special case.
I've now looked over the source, and I think the best way to add a new class is to edit the module directly; otherwise, scrap inheritance and take option one (your "has-a" option). The different classes are all slightly differentiated versions of the same container class -- they shouldn't really even be separate classes. But if you want to add a new class in the idiom of the code as it it is written, you have to add a new class to the module itself. Furthermore, from_iter is deceptively named; it's not really a constructor at all. I think it should be a standalone function. It builds a whole tree of components linked together, and the code that builds the individual components is buried in a chain of calls to various factory functions that also should be standalone functions but aren't. IMO much of that code ought to live in __init__ where it would be useful to you for subclassing, but it doesn't.
Indeed, none of the subclasses of Component even add any methods. By adding methods to your subclass of Calendar, you're completely disregarding the actual idiom of the code. I don't like its idiom very much but by disregarding that idiom, you're making it even worse. If you don't want to modify the original module, then forget about inheritance here and give your object a has-a relationship to Calendar objects. Don't modify __class__; establish your own OO structure that follows standard OO practices.

How dangerous is setting self.__class__ to something else?

Say I have a class, which has a number of subclasses.
I can instantiate the class. I can then set its __class__ attribute to one of the subclasses. I have effectively changed the class type to the type of its subclass, on a live object. I can call methods on it which invoke the subclass's version of those methods.
So, how dangerous is doing this? It seems weird, but is it wrong to do such a thing? Despite the ability to change type at run-time, is this a feature of the language that should completely be avoided? Why or why not?
(Depending on responses, I'll post a more-specific question about what I would like to do, and if there are better alternatives).

Here's a list of things I can think of that make this dangerous, in rough order from worst to least bad:
It's likely to be confusing to someone reading or debugging your code.
You won't have gotten the right __init__ method, so you probably won't have all of the instance variables initialized properly (or even at all).
The differences between 2.x and 3.x are significant enough that it may be painful to port.
There are some edge cases with classmethods, hand-coded descriptors, hooks to the method resolution order, etc., and they're different between classic and new-style classes (and, again, between 2.x and 3.x).
If you use __slots__, all of the classes must have identical slots. (And if you have the compatible but different slots, it may appear to work at first but do horrible things…)
Special method definitions in new-style classes may not change. (In fact, this will work in practice with all current Python implementations, but it's not documented to work, so…)
If you use __new__, things will not work the way you naively expected.
If the classes have different metaclasses, things will get even more confusing.
Meanwhile, in many cases where you'd think this is necessary, there are better options:
Use a factory to create an instance of the appropriate class dynamically, instead of creating a base instance and then munging it into a derived one.
Use __new__ or other mechanisms to hook the construction.
Redesign things so you have a single class with some data-driven behavior, instead of abusing inheritance.
As a very most common specific case of the last one, just put all of the "variable methods" into classes whose instances are kept as a data member of the "parent", rather than into subclasses. Instead of changing self.__class__ = OtherSubclass, just do self.member = OtherSubclass(self). If you really need methods to magically change, automatic forwarding (e.g., via __getattr__) is a much more common and pythonic idiom than changing classes on the fly.

Assigning the __class__ attribute is useful if you have a long time running application and you need to replace an old version of some object by a newer version of the same class without loss of data, e.g. after some reload(mymodule) and without reload of unchanged modules. Other example is if you implement persistency - something similar to pickle.load.
All other usage is discouraged, especially if you can write the complete code before starting the application.

On arbitrary classes, this is extremely unlikely to work, and is very fragile even if it does. It's basically the same thing as pulling the underlying function objects out of the methods of one class, and calling them on objects which are not instances of the original class. Whether or not that will work depends on internal implementation details, and is a form of very tight coupling.
That said, changing the __class__ of objects amongst a set of classes that were particularly designed to be used this way could be perfectly fine. I've been aware that you can do this for a long time, but I've never yet found a use for this technique where a better solution didn't spring to mind at the same time. So if you think you have a use case, go for it. Just be clear in your comments/documentation what is going on. In particular it means that the implementation of all the classes involved have to respect all of their invariants/assumptions/etc, rather than being able to consider each class in isolation, so you'd want to make sure that anyone who works on any of the code involved is aware of this!

Well, not discounting the problems cautioned about at the start. But it can be useful in certain cases.
First of all, the reason I am looking this post up is because I did just this and __slots__ doesn't like it. (yes, my code is a valid use case for slots, this is pure memory optimization) and I was trying to get around a slots issue.
I first saw this in Alex Martelli's Python Cookbook (1st ed). In the 3rd ed, it's recipe 8.19 "Implementing Stateful Objects or State Machine Problems". A fairly knowledgeable source, Python-wise.
Suppose you have an ActiveEnemy object that has different behavior from an InactiveEnemy and you need to switch back and forth quickly between them. Maybe even a DeadEnemy.
If InactiveEnemy was a subclass or a sibling, you could switch class attributes. More exactly, the exact ancestry matters less than the methods and attributes being consistent to code calling it. Think Java interface or, as several people have mentioned, your classes need to be designed with this use in mind.
Now, you still have to manage state transition rules and all sorts of other things. And, yes, if your client code is not expecting this behavior and your instances switch behavior, things will hit the fan.
But I've used this quite successfully on Python 2.x and never had any unusual problems with it. Best done with a common parent and small behavioral differences on subclasses with the same method signatures.
No problems, until my __slots__ issue that's blocking it just now. But slots are a pain in the neck in general.
I would not do this to patch live code. I would also privilege using a factory method to create instances.
But to manage very specific conditions known in advance? Like a state machine that the clients are expected to understand thoroughly? Then it is pretty darn close to magic, with all the risk that comes with it. It's quite elegant.
Python 3 concerns? Test it to see if it works but the Cookbook uses Python 3 print(x) syntax in its example, FWIW.

The other answers have done a good job of discussing the question of why just changing __class__ is likely not an optimal decision.
Below is one example of a way to avoid changing __class__ after instance creation, using __new__. I'm not recommending it, just showing how it could be done, for the sake of completeness. However it is probably best to do this using a boring old factory rather than shoe-horning inheritance into a job for which it was not intended.
class ChildDispatcher:
_subclasses = dict()
def __new__(cls, *args, dispatch_arg, **kwargs):
# dispatch to a registered child class
subcls = cls.getsubcls(dispatch_arg)
return super(ChildDispatcher, subcls).__new__(subcls)
def __init_subclass__(subcls, **kwargs):
super(ChildDispatcher, subcls).__init_subclass__(**kwargs)
# add __new__ contructor to child class based on default first dispatch argument
def __new__(cls, *args, dispatch_arg = subcls.__qualname__, **kwargs):
return super(ChildDispatcher,cls).__new__(cls, *args, **kwargs)
subcls.__new__ = __new__
ChildDispatcher.register_subclass(subcls)
#classmethod
def getsubcls(cls, key):
name = cls.__qualname__
if cls is not ChildDispatcher:
raise AttributeError(f"type object {name!r} has no attribute 'getsubcls'")
try:
return ChildDispatcher._subclasses[key]
except KeyError:
raise KeyError(f"No child class key {key!r} in the "
f"{cls.__qualname__} subclasses registry")
#classmethod
def register_subclass(cls, subcls):
name = subcls.__qualname__
if cls is not ChildDispatcher:
raise AttributeError(f"type object {name!r} has no attribute "
f"'register_subclass'")
if name not in ChildDispatcher._subclasses:
ChildDispatcher._subclasses[name] = subcls
else:
raise KeyError(f"{name} subclass already exists")
class Child(ChildDispatcher): pass
c1 = ChildDispatcher(dispatch_arg = "Child")
assert isinstance(c1, Child)
c2 = Child()
assert isinstance(c2, Child)

How "dangerous" it is depends primarily on what the subclass would have done when initializing the object. It's entirely possible that it would not be properly initialized, having only run the base class's __init__(), and something would fail later because of, say, an uninitialized instance attribute.
Even without that, it seems like bad practice for most use cases. Easier to just instantiate the desired class in the first place.

Here's an example of one way you could do the same thing without changing __class__. Quoting #unutbu in the comments to the question:
Suppose you were modeling cellular automata. Suppose each cell could be in one of say 5 Stages. You could define 5 classes Stage1, Stage2, etc. Suppose each Stage class has multiple methods.
class Stage1(object):
…
class Stage2(object):
…
…
class Cell(object):
def __init__(self):
self.current_stage = Stage1()
def goToStage2(self):
self.current_stage = Stage2()
def __getattr__(self, attr):
return getattr(self.current_stage, attr)
If you allow changing __class__ you could instantly give a cell all the methods of a new stage (same names, but different behavior).
Same for changing current_stage, but this is a perfectly normal and pythonic thing to do, that won't confuse anyone.
Plus, it allows you to not change certain special methods you don't want changed, just by overriding them in Cell.
Plus, it works for data members, class methods, static methods, etc., in ways every intermediate Python programmer already understands.
If you refuse to change __class__, then you might have to include a stage attribute, and use a lot of if statements, or reassign a lot of attributes pointing to different stage's functions
Yes, I've used a stage attribute, but that's not a downside—it's the obvious visible way to keep track of what the current stage is, better for debugging and for readability.
And there's not a single if statement or any attribute reassignment except for the stage attribute.
And this is just one of multiple different ways of doing this without changing __class__.

In the comments I proposed modeling cellular automata as a possible use case for dynamic __class__s. Let's try to flesh out the idea a bit:
Using dynamic __class__:
class Stage(object):
def __init__(self, x, y):
self.x = x
self.y = y
class Stage1(Stage):
def step(self):
if ...:
self.__class__ = Stage2
class Stage2(Stage):
def step(self):
if ...:
self.__class__ = Stage3
cells = [Stage1(x,y) for x in range(rows) for y in range(cols)]
def step(cells):
for cell in cells:
cell.step()
yield cells
For lack of a better term, I'm going to call this
The traditional way: (mainly abarnert's code)
class Stage1(object):
def step(self, cell):
...
if ...:
cell.goToStage2()
class Stage2(object):
def step(self, cell):
...
if ...:
cell.goToStage3()
class Cell(object):
def __init__(self, x, y):
self.x = x
self.y = y
self.current_stage = Stage1()
def goToStage2(self):
self.current_stage = Stage2()
def __getattr__(self, attr):
return getattr(self.current_stage, attr)
cells = [Cell(x,y) for x in range(rows) for y in range(cols)]
def step(cells):
for cell in cells:
cell.step(cell)
yield cells
Comparison:
The traditional way creates a list of Cell instances each with a
current stage attribute.
The dynamic __class__ way creates a list of instances which are
subclasses of Stage. There is no need for a current stage
attribute since __class__ already serves this purpose.
The traditional way uses goToStage2, goToStage3, ... methods to
switch stages.
The dynamic __class__ way requires no such methods. You just
reassign __class__.
The traditional way uses the special method __getattr__ to delegate
some method calls to the appropriate stage instance held in the
self.current_stage attribute.
The dynamic __class__ way does not require any such delegation. The
instances in cells are already the objects you want.
The traditional way needs to pass the cell as an argument to
Stage.step. This is so cell.goToStageN can be called.
The dynamic __class__ way does not need to pass anything. The
object we are dealing with has everything we need.
Conclusion:
Both ways can be made to work. To the extent that I can envision how these two implementations would pan-out, it seems to me the dynamic __class__ implementation will be
simpler (no Cell class),
more elegant (no ugly goToStage2 methods, no brain-teasers like why
you need to write cell.step(cell) instead of cell.step()),
and easier to understand (no __getattr__, no additional level of
indirection)

Adding convenience static / class methods without removing or breaking existing implementation

I am getting the hang of the OOP paradigm, and the art of making expandable and reusable code is something I want to improve at. Let's say in theory that I have a Python library of utility classes that has been widely used. I want to add some convenience static methods with the same code to a particular class for ease of use, but I don't want to break my existing use of the library. What is the recommended way to implement and name the new class methods? I have a couple of guesses as follows:
1) With an overloaded method name to maintain clarity? Python's not really a good example, but in other languages such as Java? For example:
class Cat(object):
def __init__(self, name):
self.name = name
def meow(self):
meow(self.name)
#staticmethod
def meow(name): # <-- Granted, Python doesn't overload method names
print "{} meowed cutely!".format(name)
2) With a different, perhaps less semantic static method name? The old name cannot be changed, so... This seems to me this could get out of hand for huge projects, with a bunch of non semantic names for just a static version of the same method. Example:
class Cat(object):
def __init__(self, name):
self.name = name
def meow(self):
meowFrom(self.name)
#staticmethod
def meowFrom(name): # Different, possibly less semantic name
print "{} meowed cutely!".format(name)
I assume duplicating the code outright is a bad idea. Which is the way to go? Or is there some better design pattern? Something specific for Python I am unaware of? I want to make sure my code isn't worthless in the future; and make some personal libraries that are expansive for future projects.

You can add optional parameters and keyword parameters to a function without breaking existing uses of it. For example, you could add a protocol=old argument to the end to choose which behavior to use, with the old behavior being default in the case of no explicit parameter. This is the best option if you want to keep the same function name, but it can quickly get unwieldy if you do it multiple times.

Class usage in Python

I write a lot of scripts in Python to analyze and plot experimental data as well as write simple simulations to test how theories fit the data. The scripts tend to be very procedural; calculate some property, calculate some other property, plot properties, analyze plot...
Rather than just writing a procedure, would there be an benefits of using a Class? I can bury the actual analysis into functions so I can pass the data to the function and let it do it's thing but the functions are not contained in a Class.
What sort of drawbacks would a Class over come and what would be the purpose of using a Class if it can be written procedurally?
If this has been posted before my apologies, just point me in that direction.

By using Object Oriented Programming, you will have objects, that have associated functions, that are (should) be the only way to modify its properties (internal variables).
It was common to have functions called trim_string(string), while with a string class you could do string.trim(). The difference is noticeable mainly when doing big complex modules, where you need to do all you can to minify the coupling between individual components.
There are other concepts that encompass OOP, like inheritance, but the real important thing to know, is that OOP is about making you think about objects that have operations and message passing (methods/verbs), instead of thinking in term of operations (functions/verbs) and basic elements (variables)
The importance of the object oriented paradigm is not as much in the language mechanism as it is in the thinking and design process.
Also take a look at this question.
There is nothing inherently wrong about Structured Programming, it's just that some problems map better to an Object Oriented design.
For example you could have in a SP language:
#Pseudocode!!!
function talk(dog):
if dog is aDog:
print "bark!"
raise "IS NOT A SUPPORTED ANIMAL!!!"
>>var dog as aDog
>>talk(dog)
"bark!"
>>var cat as aCat
>>talk(cat)
EXCEPTION: IS NOT A SUPPORTED ANIMAL!!!
# Lets add the cat
function talk(animal):
if animal is aDog:
print "bark!"
if animal is aCat:
print "miau!"
raise "IS NOT A SUPPORTED ANIMAL!!!"
While on an OOP you'd have:
class Animal:
def __init__(self, name="skippy"):
self.name = name
def talk(self):
raise "MUTE ANIMAL"
class Dog(Animal):
def talk(self):
print "bark!"
class Cat(Animal):
def talk(self):
print "miau!"
>>dog = new Dog()
>>dog.talk()
"bark!"
>>cat = new Cat()
>>cat.talk()
"miau!"
You can see that with SP, every animal that you add, you'd have to add another if to talk, add another variable to store the name of the animal, touch potentially every function in the module, while on OOP, you can consider your class as independent to the rest. When there is a global change, you change the Animal, when it's a narrow change, you just have to look at the class definition.
For simple, sequential, and possibly throwaway code, it's ok to use structured programming.

You don't need to use classes in Python - it doesn't force you to do OOP. If you're more comfortable with the functional style, that's fine. I use classes when I want to model some abstraction which has variations, and I want to model those variations using classes. As the word "class" implies, they're useful mainly when the stuff you are working with falls naturally into various classes. When just manipulating large datasets, I've not found an overarching need to follow an OOP paradigm just for the sake of it.

"but the functions are not contained in a Class."
They could be.
class Linear( object ):
a= 2.
b= 3.
def calculate( self, somePoint ):
somePoint['line']= b + somePoint['x']*a
class Exponential( object ):
a = 1.05
b = 3.2
def calculate( self, somePoint ):
somePoint['exp']= b * somePoint['x']**a
class Mapping( object ):
def __init__( self ):
self.funcs = ( Linear(), Exponential() )
def apply( self, someData ):
for row in someData:
for f in self.funcs:
f.calculate( row )
Now your calculations are wrapped in classes. You can use design patterns like Delegation, Composition and Command to simplify your scripts.

OOP lends itself well to complex programs. It's great for capturing the state and behavior of real world concepts and orchestrating the interplay between them. Good OO code is easy to read/understand, protects your data's integrity, and maximizes code reuse. I'd say code reuse is one big advantage to keeping your frequently used calculations in a class.

Object-oriented programming isn't the solution to every coding problem.
In Python, functions are objects. You can mix as many objects and functions as you want.
Modules with functions are already objects with properties.
If you find yourself passing a lot of the same variables around — state — an object is probably better suited. If you have a lot of classes with class methods, or methods that don't use self very much, then functions are probably better.