Use a Metaclass

I would recommend Method #2, but you're better off using a metaclass than a base class. Here is a sample implementation:

class Singleton(type):
    _instances = {}
    def __call__(cls, *args, **kwargs):
        if cls not in cls._instances:
            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
        return cls._instances[cls]
        
class Logger(object):
    __metaclass__ = Singleton

Or in Python3

class Logger(metaclass=Singleton):
    pass

If you want to run __init__ every time the class is called, add

        else:
            cls._instances[cls].__init__(*args, **kwargs)

to the if statement in Singleton.__call__.

A few words about metaclasses. A metaclass is the class of a class; that is, a class is an instance of its metaclass. You find the metaclass of an object in Python with type(obj). Normal new-style classes are of type type. Logger in the code above will be of type class 'your_module.Singleton', just as the (only) instance of Logger will be of type class 'your_module.Logger'. When you call logger with Logger(), Python first asks the metaclass of Logger, Singleton, what to do, allowing instance creation to be pre-empted. This process is the same as Python asking a class what to do by calling __getattr__ when you reference one of it's attributes by doing myclass.attribute.

A metaclass essentially decides what the definition of a class means and how to implement that definition. See for example http://code.activestate.com/recipes/498149/, which essentially recreates C-style structs in Python using metaclasses. The thread What are some (concrete) use-cases for metaclasses? also provides some examples, they generally seem to be related to declarative programming, especially as used in ORMs.

In this situation, if you use your Method #2, and a subclass defines a __new__ method, it will be executed every time you call SubClassOfSingleton() -- because it is responsible for calling the method that returns the stored instance. With a metaclass, it will only be called once, when the only instance is created. You want to customize what it means to call the class, which is decided by it's type.

In general, it makes sense to use a metaclass to implement a singleton. A singleton is special because is created only once, and a metaclass is the way you customize the creation of a class. Using a metaclass gives you more control in case you need to customize the singleton class definitions in other ways.

Your singletons won't need multiple inheritance (because the metaclass is not a base class), but for subclasses of the created class that use multiple inheritance, you need to make sure the singleton class is the first / leftmost one with a metaclass that redefines __call__ This is very unlikely to be an issue. The instance dict is not in the instance's namespace so it won't accidentally overwrite it.

You will also hear that the singleton pattern violates the "Single Responsibility Principle" -- each class should do only one thing. That way you don't have to worry about messing up one thing the code does if you need to change another, because they are separate and encapsulated. The metaclass implementation passes this test. The metaclass is responsible for enforcing the pattern and the created class and subclasses need not be aware that they are singletons. Method #1 fails this test, as you noted with "MyClass itself is a a function, not a class, so you cannot call class methods from it."

Python 2 and 3 Compatible Version

Writing something that works in both Python2 and 3 requires using a slightly more complicated scheme. Since metaclasses are usually subclasses of type type, it's possible to use one to dynamically create an intermediary base class at run time with it as its metaclass and then use that as the baseclass of the public Singleton base class. It's harder to explain than to do, as illustrated next:

# works in Python 2 & 3
class _Singleton(type):
    """ A metaclass that creates a Singleton base class when called. """
    _instances = {}
    def __call__(cls, *args, **kwargs):
        if cls not in cls._instances:
            cls._instances[cls] = super(_Singleton, cls).__call__(*args, **kwargs)
        return cls._instances[cls]

class Singleton(_Singleton('SingletonMeta', (object,), {})): pass

class Logger(Singleton):
    pass

An ironic aspect of this approach is that it's using subclassing to implement a metaclass. One possible advantage is that, unlike with a pure metaclass, isinstance(inst, Singleton) will return True.

Corrections

On another topic, you've probably already noticed this, but the base class implementation in your original post is wrong. _instances needs to be referenced on the class, you need to use super() or you're recursing, and __new__ is actually a static method that you have to pass the class to, not a class method, as the actual class hasn't been created yet when it is called. All of these things will be true for a metaclass implementation as well.

class Singleton(object):
  _instances = {}
  def __new__(class_, *args, **kwargs):
    if class_ not in class_._instances:
        class_._instances[class_] = super(Singleton, class_).__new__(class_, *args, **kwargs)
    return class_._instances[class_]

class MyClass(Singleton):
  pass

c = MyClass()

Decorator Returning A Class

I originally was writing a comment but it was too long, so I'll add this here. Method #4 is better than the other decorator version, but it's more code than needed for a singleton, and it's not as clear what it does.

The main problems stem from the class being it's own base class. First, isn't it weird to have a class be a subclass of a nearly identical class with the same name that exists only in its __class__ attribute? This also means that you can't define any methods that call the method of the same name on their base class with super() because they will recurse. This means your class can't customize __new__, and can't derive from any classes that need __init__ called on them.

When to use the singleton pattern

Your use case is one of the better examples of wanting to use a singleton. You say in one of the comments "To me logging has always seemed a natural candidate for Singletons." You're absolutely right.

When people say singletons are bad, the most common reason is they are implicit shared state. While with global variables and top-level module imports are explicit shared state, other objects that are passed around are generally instantiated. This is a good point, with two exceptions.

The first, and one that gets mentioned in various places, is when the singletons are constant. Use of global constants, especially enums, is widely accepted, and considered sane because no matter what, none of the users can mess them up for any other user. This is equally true for a constant singleton.

The second exception, which get mentioned less, is the opposite -- when the singleton is only a data sink, not a data source (directly or indirectly). This is why loggers feel like a "natural" use for singletons. As the various users are not changing the loggers in ways other users will care about, there is not really shared state. This negates the primary argument against the singleton pattern, and makes them a reasonable choice because of their ease of use for the task.

Here is a quote from http://googletesting.blogspot.com/2008/08/root-cause-of-singletons.html:

Now, there is one kind of Singleton which is OK. That is a singleton where all of the reachable objects are immutable. If all objects are immutable than Singleton has no global state, as everything is constant. But it is so easy to turn this kind of singleton into mutable one, it is very slippery slope. Therefore, I am against these Singletons too, not because they are bad, but because it is very easy for them to go bad. (As a side note Java enumeration are just these kind of singletons. As long as you don't put state into your enumeration you are OK, so please don't.)

The other kind of Singletons, which are semi-acceptable are those which don't effect the execution of your code, They have no "side effects". Logging is perfect example. It is loaded with Singletons and global state. It is acceptable (as in it will not hurt you) because your application does not behave any different whether or not a given logger is enabled. The information here flows one way: From your application into the logger. Even thought loggers are global state since no information flows from loggers into your application, loggers are acceptable. You should still inject your logger if you want your test to assert that something is getting logged, but in general Loggers are not harmful despite being full of state.

Script vs. Module

Here's an explanation. The short version is that there is a big difference between directly running a Python file, and importing that file from somewhere else. Just knowing what directory a file is in does not determine what package Python thinks it is in. That depends, additionally, on how you load the file into Python (by running or by importing).

There are two ways to load a Python file: as the top-level script, or as a module. A file is loaded as the top-level script if you execute it directly, for instance by typing python myfile.py on the command line. It is loaded as a module when an import statement is encountered inside some other file. There can only be one top-level script at a time; the top-level script is the Python file you ran to start things off.

Naming

When a file is loaded, it is given a name (which is stored in its __name__ attribute). If it was loaded as the top-level script, its name is __main__. If it was loaded as a module, its name is the filename, preceded by the names of any packages/subpackages of which it is a part, separated by dots.

So for instance in your example:

package/
    __init__.py
    subpackage1/
        __init__.py
        moduleX.py
    moduleA.py

if you imported moduleX (note: imported, not directly executed), its name would be package.subpackage1.moduleX. If you imported moduleA, its name would be package.moduleA. However, if you directly run moduleX from the command line, its name will instead be __main__, and if you directly run moduleA from the command line, its name will be __main__. When a module is run as the top-level script, it loses its normal name and its name is instead __main__.

Accessing a module NOT through its containing package

There is an additional wrinkle: the module's name depends on whether it was imported "directly" from the directory it is in or imported via a package. This only makes a difference if you run Python in a directory, and try to import a file in that same directory (or a subdirectory of it). For instance, if you start the Python interpreter in the directory package/subpackage1 and then do import moduleX, the name of moduleX will just be moduleX, and not package.subpackage1.moduleX. This is because Python adds the current directory to its search path when the interpreter is entered interactively; if it finds the to-be-imported module in the current directory, it will not know that that directory is part of a package, and the package information will not become part of the module's name.

A special case is if you run the interpreter interactively (e.g., just type python and start entering Python code on the fly). In this case, the name of that interactive session is __main__.

Now here is the crucial thing for your error message: if a module's name has no dots, it is not considered to be part of a package. It doesn't matter where the file actually is on disk. All that matters is what its name is, and its name depends on how you loaded it.

Now look at the quote you included in your question:

Relative imports use a module's name attribute to determine that module's position in the package hierarchy. If the module's name does not contain any package information (e.g. it is set to 'main') then relative imports are resolved as if the module were a top-level module, regardless of where the module is actually located on the file system.

Relative imports...

Relative imports use the module's name to determine where it is in a package. When you use a relative import like from .. import foo, the dots indicate to step up some number of levels in the package hierarchy. For instance, if your current module's name is package.subpackage1.moduleX, then ..moduleA would mean package.moduleA. For a from .. import to work, the module's name must have at least as many dots as there are in the import statement.

... are only relative in a package

However, if your module's name is __main__, it is not considered to be in a package. Its name has no dots, and therefore you cannot use from .. import statements inside it. If you try to do so, you will get the "relative-import in non-package" error.

Scripts can't import relative

What you probably did is you tried to run moduleX or the like from the command line. When you did this, its name was set to __main__, which means that relative imports within it will fail, because its name does not reveal that it is in a package. Note that this will also happen if you run Python from the same directory where a module is, and then try to import that module, because, as described above, Python will find the module in the current directory "too early" without realizing it is part of a package.

Also remember that when you run the interactive interpreter, the "name" of that interactive session is always __main__. Thus you cannot do relative imports directly from an interactive session. Relative imports are only for use within module files.

Two solutions:

1. If you really do want to run moduleX directly, but you still want it to be considered part of a package, you can do python -m package.subpackage1.moduleX. The -m tells Python to load it as a module, not as the top-level script.

2. Or perhaps you don't actually want to run moduleX, you just want to run some other script, say myfile.py, that uses functions inside moduleX. If that is the case, put myfile.py somewhere else – not inside the package directory – and run it. If inside myfile.py you do things like from package.moduleA import spam, it will work fine.

Notes

• For either of these solutions, the package directory (package in your example) must be accessible from the Python module search path (sys.path). If it is not, you will not be able to use anything in the package reliably at all.

• Since Python 2.6, the module's "name" for package-resolution purposes is determined not just by its __name__ attributes but also by the __package__ attribute. That's why I'm avoiding using the explicit symbol __name__ to refer to the module's "name". Since Python 2.6 a module's "name" is effectively __package__ + '.' + __name__, or just __name__ if __package__ is None.)