Python's class mechanism adds classes to the language with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. As is true for modules, classes in Python do not put an absolute barrier between definition and user, but rather rely on the politeness of the user not to ``break into the definition.'' The most important features of classes are retained with full power, however: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, a method can call the method of a base class with the same name. Objects can contain an arbitrary amount of private data.

In C++ terminology, all class members (including the data members) are public, and all member functions are virtual. There are no special constructors or destructors. As in Modula-3, there are no shorthands for referencing the object's members from its methods: the method function is declared with an explicit first argument representing the object, which is provided implicitly by the call. As in Smalltalk, classes themselves are objects, albeit in the wider sense of the word: in Python, all data types are objects. This provides semantics for importing and renaming. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++ but unlike in Modula-3, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.

Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. (I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)

I also have to warn you that there's a terminological pitfall for object-oriented readers: the word ``object'' in Python does not necessarily mean a class instance. Like C++ and Modula-3, and unlike Smalltalk, not all types in Python are classes: the basic built-in types like integers and lists are not, and even somewhat more exotic types like files aren't. However, all Python types share a little bit of common semantics that is best described by using the word object.

Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has an (intended!) effect on the semantics of Python code involving mutable objects such as lists, dictionaries, and most types representing entities outside the program (files, windows, etc.). This is usually used to the benefit of the program, since aliases behave like pointers in some respects. For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, the caller will see the change -- this eliminates the need for two different argument passing mechanisms as in Pascal.

Before introducing classes, I first have to tell you something about Python's scope rules. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what's going on. Incidentally, knowledge about this subject is useful for any advanced Python programmer.

Let's begin with some definitions.

A namespace is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that's normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are: the set of built-in names (functions such as abs(), and built-in exception names); the global names in a module; and the local names in a function invocation. In a sense the set of attributes of an object also form a namespace. The important thing to know about namespaces is that there is absolutely no relation between names in different namespaces; for instance, two different modules may both define a function ``maximize'' without confusion -- users of the modules must prefix it with the module name.

Classes introduce a little bit of new syntax, three new object types, and some new semantics.

Class definitions, like function definitions (def statements) must be executed before they have any effect. (You could conceivably place a class definition in a branch of an if statement, or inside a function.)

In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful -- we'll come back to this later. The function definitions inside a class normally have a peculiar form of argument list, dictated by the calling conventions for methods -- again, this is explained later.

When a class definition is entered, a new namespace is created, and used as the local scope -- thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.

When a class definition is left normally (via the end), a class object is created. This is basically a wrapper around the contents of the namespace created by the class definition; we'll learn more about class objects in the next section. The original local scope (the one in effect just before the class definitions was entered) is reinstated, and the class object is bound here to the class name given in the class definition header (ClassName in the example).

then MyClass.i and MyClass.f are valid attribute references, returning an integer and a method object, respectively. Class attributes can also be assigned to, so you can change the value of MyClass.i by assignment. __doc__ is also a valid attribute, returning the docstring belonging to the class: "A simple example class.

Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):

creates a new instance of the class and assigns this object to the local variable x.

The instantiation operation (``calling'' a class object) creates an empty object. Many classes like to create objects in a known initial state. Therefore a class may define a special method named __init__(), like this:

When a class defines an __init__() method, class instantiation automatically invokes __init__() for the newly-created class instance. So in this example, a new, initialized instance can be obtained by:

Of course, the __init__() method may have arguments for greater flexibility. In that case, arguments given to the class instantiation operator are passed on to __init__(). For example,

The second kind of attribute references understood by instance objects are methods. A method is a function that ``belongs to'' an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on. However, below, we'll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)

In our example, this will return the string 'hello world'. However, it is not necessary to call a method right away: x.f is a method object, and can be stored away and called at a later time. For example:

will continue to print "hello worlduntil the end of time.

What exactly happens when a method is called? You may have noticed that x.f() was called without an argument above, even though the function definition for f specified an argument. What happened to the argument? Surely Python raises an exception when a function that requires an argument is called without any -- even if the argument isn't actually used...

Actually, you may have guessed the answer: the special thing about methods is that the object is passed as the first argument of the function. In our example, the call x.f() is exactly equivalent to MyClass.f(x). In general, calling a method with a list of n arguments is equivalent to calling the corresponding function with an argument list that is created by inserting the method's object before the first argument.

If you still don't understand how methods work, a look at the implementation can perhaps clarify matters. When an instance attribute is referenced that isn't a data attribute, its class is searched. If the name denotes a valid class attribute that is a function object, a method object is created by packing (pointers to) the instance object and the function object just found together in an abstract object: this is the method object. When the method object is called with an argument list, it is unpacked again, a new argument list is constructed from the instance object and the original argument list, and the function object is called with this new argument list.

Data attributes override method attributes with the same name; to avoid accidental name conflicts, which may cause hard-to-find bugs in large programs, it is wise to use some kind of convention that minimizes the chance of conflicts. Possible conventions include capitalizing method names, prefixing data attribute names with a small unique string (perhaps just an underscore), or using verbs for methods and nouns for data attributes.

Data attributes may be referenced by methods as well as by ordinary users (``clients'') of an object. In other words, classes are not usable to implement pure abstract data types. In fact, nothing in Python makes it possible to enforce data hiding -- it is all based upon convention. (On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.)

Clients should use data attributes with care -- clients may mess up invariants maintained by the methods by stamping on their data attributes. Note that clients may add data attributes of their own to an instance object without affecting the validity of the methods, as long as name conflicts are avoided -- again, a naming convention can save a lot of headaches here.

There is no shorthand for referencing data attributes (or other methods!) from within methods. I find that this actually increases the readability of methods: there is no chance of confusing local variables and instance variables when glancing through a method.

Conventionally, the first argument of methods is often called self. This is nothing more than a convention: the name self has absolutely no special meaning to Python. (Note, however, that by not following the convention your code may be less readable by other Python programmers, and it is also conceivable that a class browser program be written which relies upon such a convention.)

Any function object that is a class attribute defines a method for instances of that class. It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok. For example:

Now f, g and h are all attributes of class C that refer to function objects, and consequently they are all methods of instances of C -- h being