The first reason you're usually given for using getters and setters is "for encapsulation: consumers of your class shouldn't even know that it has an attribute x, much less be able to change it willy-nilly." 

As an argument for getters and setters, this is nonsense. (And the people who write the C++ standard agree, but they can't stop people from writing misleading textbooks or tutorials.) Consumers of your class do know that you have an attribute x, and can change it willy-nilly, because you have a method named set_x. That's the conventional and idiomatic name for a setter for the x attribute, and it would be highly confusing to have a method with that name that did anything else.

The point of encapsulation is that the class's interface should be based on what the class does, not on what its state is. If what it does is just represent a point with x and y values, then the x and y attributes themselves are meaningful, and a getter and setter don't make them any more meaningful. If what it does is fetch a URL, parse the resulting JSON, fetch all referenced documents, and index them, then the HTTP connection pool is not meaningful, and a getter and setter don't make it any more meaningful. Adding a getter and setter almost never improves encapsulation in any meaningful way.

"Almost always" isn't "always". Say what your class does is represent a point more abstractly, with x, y, r, and theta values. Maybe x and y are attributes, maybe they're computed on the fly from r and theta. Or maybe they're sometimes stored as attributes, sometimes computed, depending on how you constructed or most recently set the instance. 

In that case, you obviously do need getters and setters—not to hide the x and y attributes, but to hide the fact that there may not even be any x and y attributes. 

That being said, is this really part of the interface, or just an implementation artifact? Often it's the latter. In that case, in Python (as in some C++ derived languages, like C#, but not in C++ itself), you can, and often should, still present them as attributes even if they really aren't, by using @property:

    class Point:

        @property

        def x(self):

            if self._x is None:

                self._x, self._y = Point._polar_to_rect(self._r, self._theta)

            return self.x

        @x.setter

        def x(set, value):

            if self._x is None:

            self._x = x

            self._r, self._theta = None, None

        # etc.

Sometimes, at least in C++ and traditional ObjC, even the "dumb" version of encapsulation idea isn't actually wrong, just oversimplified. Preventing people from setting your attribute by giving them a method to set your attribute is silly—but in C++, direct access to an attribute allows you to do more than just set the attribute, it allows you to store a reference to it. And, since C++ isn't garbage-collected, this means that there's nothing stopping some code from keeping that reference around after your instance goes out of scope, at which point they've now got a reference to garbage. In fact, this can be a major source of bugs in C++ code.

If you instead provide a getter or setter, you can ensure that consumers can only get a copy of the attribute. Or, if you need to provide a reference, you can hold the object by smart pointer and return a new smart pointer to the object. (Of course this means that people who—usually as a misguided attempt at optimization—write getters that return a const reference, or that return objects that aren't copy-safe, like raw pointers, are defeating the entire purpose of having getters and setters in the first place…)

This rationale, which makes perfect sense in C++, is irrelevant to Python. Python is garbage collected. And Python doesn't provide any way to take references to attributes in the first place; the only thing you can do is get a new (properly-GC-tracked) reference to the value of the attribute. If your instance goes away, but someone else is still referencing one of the values you had in an attribute, that value is still alive and perfectly usable.

Maybe today, you're storing x and y as attributes, but what if you want to change your implementation to compute them on the fly?

In some languages, like C++, if you're worried about that, the only option you have is to create useless getters and setters today, so that if you change the implementation tomorrow, your interface doesn't have to change.

In Python, just expose the attribute; if you change the implementation tomorrow, change the attribute to a @property, and your interface doesn't have to change.

Interface inheritance

Design by contract

Sometimes, you want to expose an attribute, but make it read-only. In many languages, like C++, there's no way to do that but with a getter (and no corresponding setter). In Python, again, the answer is @property.

C++, Java, etc. also give you a way to create class-wide constants. Python doesn't really have constants, so some people try to simulate this by adding a getter. Again, though, the answer is @property. Or, consider whether you really need to enforce the fact that it's a constant. Why would someone try to change it? What would happen if they did?

C++ and most of its descendants provide three levels of access control—public is the interface to everyone, protected is additional interface for subclasses, and private is only usable by the class's own methods and its friends. Sometimes you might want to make an attribute read-only for your subclasses but writable for your own methods (or read-only for your consumers but writable for your subclasses). The only way to do this is to make the attribute private (or protected) and add a protected (or public) getter.

First, almost any OO design where this makes sense is probably not idiomatic for Python. (In fact, it's probably not even idiomatic for modern C++, but a lot of people are still programming 90s-style C++, and at any rate, it's much more idiomatic in Java. However, nobody is writing 90s-style OO in Python.) Often you can flatten out and simplify your hierarchy by passing around closures or methods, or by duck typing (that is, implicitly subtyping by just implementing the right methods, instead of subclassing); if you really do need subclassing, often you want to refactor your classes into small ABCs and/or mixins.

It's also worth noting that access control doesn't provide any actual security against malicious subclasses or consumers. (Except in Java; see below.) In ObjC, they can just ask the runtime to inspect your ivars and change them through pointers. In C++, they can't do that—but everyone using your class has to be able to see all of your members, even if they're private, as part of the header, and if they really want to force your class to do something it wouldn't normally do by changing its private members, there's nothing stopping them from reinterpret_cast<>ing their way to any part of that structure. 

At any rate, in Python, even if you wanted to control access this way, you can't do it.

Some people teach that _x is Python's equivalent of protected, and __x its equivalent of private, but that's very misleading.

The single underscore has only a conventional meaning: don't count on this being part of the useful and/or stable interface. Many introspection tools (e.g., tab completion in the interactive interface) will skip over underscore-prefixed names by default, but nothing stops a consumer from writing spam._eggs to access the value.

The double underscore mangles the name—inside your own methods, the attribute is named __x, but from anywhere else, it's named _MyClass__x. But this is not there to add any more protection—after all, _MyClass__x will still show up in dir(my_instance), and someone can still write my_instance._MyClass__x = 42. What it's there for is to prevent subclasses from accidentally shadowing your attributes or methods. (This is primarily important when the base classes and subclasses are implemented independently—you wouldn't want to add a new _spam attribute to your library and accidentally break any app that subclasses your library and adds a _spam attribute.)

Java was designed to allow components that don't trust each other to run securely. That means that in some cases, access control does actually provide security. (Of course if you're just going to hide your x behind a public set_x method, that isn't adding anything…) That's great for Java, but it's irrelevant to Python, or any language without a secure class loader, etc.

As far as I know, this one is really only relevant to C++ , because most other languages' generics were designed around offering most of the useful features of C++ templates without all of the mess, while C++'s templates were designed before anyone knew what they wanted to do with generics.

There are many cases where it's hard to specify a type for an attribute in a class template. C++ has much more limited type inference for classes and objects (before C++11, there was none at all) than for functions and types. Also, a class or class template can have methods that are themselves templates, but there are no "object templates", so you have to simulate them with either traits classes or function templates—that is, templated getters and setters. And there are also cases where it's hard to specify a constant or initializer value for an attribute, so again you have to simulate them with either traits classes or function templates.

Needless to say, none of these applies at all in duck-typed Python.

In some languages, there are libraries for reflection or serialization, observer-notification frameworks, code-generating wizards for UIs or network protocols, tools like IDEs and refactoring assistants, etc., that expect you to use getters and setters. This is particularly true in Java, and to a lesser extent C# and ObjC. Obviously you don't want to fight against these tools.

The tools and libraries for Python are, of course, designed around the idea that your attributes are attributes, not hidden behind getters and setters. But if you're, say, using PyObjC to write Python code that's bound to a NIB both at runtime and in InterfaceBuilder, you have to do things the way InterfaceBuilder wants you to.

In some debuggers, it's impossible to place a watchpoint on a variable, or at least much harder or much less efficient than placing a breakpoint on a setter. If you expect this to be a problem, and you need to be able to debug code in the field without editing it, you might want to hide some attributes behind @property for easier debugging. But this doesn't come up very often.

So, what if you're a Python programmer and you have to write some code in Java or D? Just as you shouldn't make your Python code look like Java, you shouldn't make your Java code look like Python. This means you'll probably want a lot more getters and setters than you're used to.

In Java, C#, D, etc. many of the same motivations (both good and bad) for getters and setters apply the same as in C++. In some cases, some of the motivations don't apply (e.g., C# has properties, just like Python; Java generics don't work like C++ templates). So there are a few cases where there are additional reasons for getters and setters.

But, more importantly, Java (and, to a lesser extent, C#, ObjC, etc.) has a much stronger culture than C++ of idiomatically requiring getters and setters even when there's no objectively good reason. There are wizards that generate them for you, linters that warn if you don't use them, IDEs that expect them to exist, coworkers or customers that complain… And the fact that it's idiomatic is in itself a good reason to follow the idiom, even if there's no objective basis for it.