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Classes
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.
A D V E R T I S E M E N T
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, and 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.
A Word About Terminology
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.)
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.
Python Scopes and Name Spaces
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.
By the way, I use the word attribute for any name following a dot --
for example, in the expression z.real, real is an
attribute of the object z. Strictly speaking, references to names
in modules are attribute references: in the expression modname.funcname,
modname is a module object and funcname is an
attribute of it. In this case there happens to be a straightforward mapping
between the module's attributes and the global names defined in the module: they
share the same namespace!
Attributes may be read-only or writable. In the latter case, assignment to
attributes is possible. Module attributes are writable: you can write "modname.the_answer
= 42". Writable attributes may also be deleted with the
del statement. For example, "del modname.the_answer"
will remove the attribute the_answer from the object
named by modname.
Name spaces are created at different moments and have different lifetimes.
The namespace containing the built-in names is created when the Python
interpreter starts up, and is never deleted. The global namespace for a module
is created when the module definition is read in; normally, module namespaces
also last until the interpreter quits. The statements executed by the top-level
invocation of the interpreter, either read from a script file or interactively,
are considered part of a module called __main__, so they
have their own global namespace. (The built-in names actually also live in a
module; this is called __builtin__.)
The local namespace for a function is created when the function is called,
and deleted when the function returns or raises an exception that is not handled
within the function. (Actually, forgetting would be a better way to describe
what actually happens.) Of course, recursive invocations each have their own
local namespace.
A scope is a textual region of a Python program where a namespace is
directly accessible. ``Directly accessible'' here means that an unqualified
reference to a name attempts to find the name in the namespace.
Although scopes are determined statically, they are used dynamically. At any
time during execution, there are at least three nested scopes whose namespaces
are directly accessible: the innermost scope, which is searched first, contains
the local names; the namespaces of any enclosing functions, which are searched
starting with the nearest enclosing scope; the middle scope, searched next,
contains the current module's global names; and the outermost scope (searched
last) is the namespace containing built-in names.
If a name is declared global, then all references and assignments go directly
to the middle scope containing the module's global names. Otherwise, all
variables found outside of the innermost scope are read-only (an attempt to
write to such a variable will simply create a new local variable in the
innermost scope, leaving the identically named outer variable unchanged).
Usually, the local scope references the local names of the (textually)
current function. Outside functions, the local scope references the same
namespace as the global scope: the module's namespace. Class definitions place
yet another namespace in the local scope.
It is important to realize that scopes are determined textually: the global
scope of a function defined in a module is that module's namespace, no matter
from where or by what alias the function is called. On the other hand, the
actual search for names is done dynamically, at run time -- however, the
language definition is evolving towards static name resolution, at ``compile''
time, so don't rely on dynamic name resolution! (In fact, local variables are
already determined statically.)
A special quirk of Python is that assignments always go into the innermost
scope. Assignments do not copy data -- they just bind names to objects. The same
is true for deletions: the statement "del x" removes the
binding of x from the namespace referenced by the local scope. In
fact, all operations that introduce new names use the local scope: in
particular, import statements and function definitions bind the module or
function name in the local scope. (The global statement
can be used to indicate that particular variables live in the global scope.)
A First Look at Classes
Classes introduce a little bit of new syntax, three new object types, and
some new semantics.
Class Definition Syntax
The simplest form of class definition looks like this:
class ClassName:
<statement-1>
.
.
.
<statement-N>
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
definition 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).
Class Objects
Class objects support two kinds of operations: attribute references and
instantiation.
Attribute references use the standard syntax used for all attribute
references in Python: obj.name. Valid attribute names are all the
names that were in the class's namespace when the class object was created. So,
if the class definition looked like this:
class MyClass:
"A simple example class"
i = 12345
def f(self):
return 'hello world'
then MyClass.i and MyClass.f are valid attribute
references, returning an integer and a function 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 with instances customized to a
specific initial state. Therefore a class may define a special method named
__init__(), like this:
def __init__(self):
self.data = []
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,
>>> class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)
Instance Objects
Now what can we do with instance objects? The only operations understood by
instance objects are attribute references. There are two kinds of valid
attribute names, data attributes and methods.
data attributes correspond to ``instance variables'' in Smalltalk,
and to ``data members'' in C++. Data attributes need not be declared; like local
variables, they spring into existence when they are first assigned to. For
example, if x is the instance of MyClass
created above, the following piece of code will print the value 16,
without leaving a trace:
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print x.counter
del x.counter
The other kind of instance attribute reference is a method. 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, in the following discussion, we'll use the term method exclusively
to mean methods of class instance objects, unless explicitly stated otherwise.)
Valid method names of an instance object depend on its class. By definition,
all attributes of a class that are function objects define corresponding methods
of its instances. So in our example, x.f is a valid method
reference, since MyClass.f is a function, but x.i is
not, since MyClass.i is not. But x.f is not the same
thing as MyClass.f -- it is a
method object, not a function object.
Method Objects
Usually, a method is called right after it is bound:
In the MyClass 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:
xf = x.f
while True:
print xf()
will continue to print "hello world" until 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.
Random Remarks
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.
Often, the first argument of a method is 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 to other Python programmers, and it is also
conceivable that a class browser program might be written that 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:
# Function defined outside the class
def f1(self, x, y):
return min(x, x+y)
class C:
f = f1
def g(self):
return 'hello world'
h = g
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 exactly equivalent to g. Note that this practice usually only
serves to confuse the reader of a program.
Methods may call other methods by using method attributes of the self
argument:
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
self.add(x)
self.add(x)
Methods may reference global names in the same way as ordinary functions. The
global scope associated with a method is the module containing the class
definition. (The class itself is never used as a global scope!) While one rarely
encounters a good reason for using global data in a method, there are many
legitimate uses of the global scope: for one thing, functions and modules
imported into the global scope can be used by methods, as well as functions and
classes defined in it. Usually, the class containing the method is itself
defined in this global scope, and in the next section we'll find some good
reasons why a method would want to reference its own class!
Inheritance
Of course, a language feature would not be worthy of the name ``class''
without supporting inheritance. The syntax for a derived class definition looks
like this:
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
The name BaseClassName must be defined in a scope
containing the derived class definition. In place of a base class name, other
arbitrary expressions are also allowed. This can be useful, for example, when
the base class is defined in another module:
class DerivedClassName(modname.BaseClassName):
Execution of a derived class definition proceeds the same as for a base
class. When the class object is constructed, the base class is remembered. This
is used for resolving attribute references: if a requested attribute is not
found in the class, the search proceeds to look in the base class. This rule is
applied recursively if the base class itself is derived from some other class.
There's nothing special about instantiation of derived classes:
DerivedClassName() creates a new instance of the class. Method references
are resolved as follows: the corresponding class attribute is searched,
descending down the chain of base classes if necessary, and the method reference
is valid if this yields a function object.
Derived classes may override methods of their base classes. Because methods
have no special privileges when calling other methods of the same object, a
method of a base class that calls another method defined in the same base class
may end up calling a method of a derived class that overrides it. (For C++
programmers: all methods in Python are effectively virtual.)
An overriding method in a derived class may in fact want to extend rather
than simply replace the base class method of the same name. There is a simple
way to call the base class method directly: just call "BaseClassName.methodname(self,
arguments)". This is occasionally useful to clients as well. (Note that
this only works if the base class is defined or imported directly in the global
scope.)
Multiple Inheritance
Python supports a limited form of multiple inheritance as well. A class
definition with multiple base classes looks like this:
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
.
.
.
<statement-N>
For old-style classes, the only rule is depth-first, left-to-right. Thus, if
an attribute is not found in DerivedClassName, it is
searched in Base1, then (recursively) in the base classes
of Base1, and only if it is not found there, it is
searched in Base2, and so on.
(To some people breadth first -- searching Base2 and
Base3 before the base classes of Base1
-- looks more natural. However, this would require you to know whether a
particular attribute of Base1 is actually defined in
Base1 or in one of its base classes before you can figure
out the consequences of a name conflict with an attribute of
Base2. The depth-first rule makes no differences between direct and
inherited attributes of Base1.)
For new-style classes, the method resolution order changes dynamically to
support cooperative calls to super(). This approach is
known in some other multiple-inheritance languages as call-next-method and is
more powerful than the super call found in single-inheritance languages.
With new-style classes, dynamic ordering is necessary because all cases of
multiple inheritance exhibit one or more diamond relationships (where one at
least one of the parent classes can be accessed through multiple paths from the
bottommost class). For example, all new-style classes inherit from
object, so any case of multiple inheritance provides more
than one path to reach object. To keep the base classes
from being accessed more than once, the dynamic algorithm linearizes the search
order in a way that preserves the left-to-right ordering specified in each
class, that calls each parent only once, and that is monotonic (meaning that a
class can be subclassed without affecting the precedence order of its parents).
Taken together, these properties make it possible to design reliable and
extensible classes with multiple inheritance.
Private Variables
There is limited support for class-private identifiers. Any identifier of the
form __spam (at least two leading underscores, at most one trailing
underscore) is textually replaced with _classname__spam, where
classname is the current class name with leading underscore(s)
stripped. This mangling is done without regard to the syntactic position of the
identifier, so it can be used to define class-private instance and class
variables, methods, variables stored in globals, and even variables stored in
instances. private to this class on instances of other classes.
Truncation may occur when the mangled name would be longer than 255 characters.
Outside classes, or when the class name consists of only underscores, no
mangling occurs.
Name mangling is intended to give classes an easy way to define ``private''
instance variables and methods, without having to worry about instance variables
defined by derived classes, or mucking with instance variables by code outside
the class. Note that the mangling rules are designed mostly to avoid accidents;
it still is possible for a determined soul to access or modify a variable that
is considered private. This can even be useful in special circumstances, such as
in the debugger, and that's one reason why this loophole is not closed. (Buglet:
derivation of a class with the same name as the base class makes use of private
variables of the base class possible.)
Notice that code passed to exec, eval() or
execfile() does not consider the classname of the invoking class to be
the current class; this is similar to the effect of the global
statement, the effect of which is likewise restricted to code that is
byte-compiled together. The same restriction applies to getattr(),
setattr() and delattr(), as well as when referencing
__dict__ directly.
Odds and Ends
Sometimes it is useful to have a data type similar to the Pascal ``record''
or C ``struct'', bundling together a few named data items. An empty class
definition will do nicely:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
A piece of Python code that expects a particular abstract data type can often
be passed a class that emulates the methods of that data type instead. For
instance, if you have a function that formats some data from a file object, you
can define a class with methods read() and
readline() that get the data from a string buffer
instead, and pass it as an argument.
Instance method objects have attributes, too: m.im_self is the
instance object with the method m, and m.im_func
is the function object corresponding to the method.
Exceptions Are Classes Too
User-defined exceptions are identified by classes as well. Using this
mechanism it is possible to create extensible hierarchies of exceptions.
There are two new valid (semantic) forms for the raise statement:
raise Class, instance
raise instance
In the first form, instance must be an instance of
Class or of a class derived from it. The second form is a
shorthand for:
raise instance.__class__, instance
A class in an except clause is compatible with an exception if it is the same
class or a base class thereof (but not the other way around -- an except clause
listing a derived class is not compatible with a base class). For example, the
following code will print B, C, D in that order:
class B:
pass
class C(B):
pass
class D(C):
pass
for c in [B, C, D]:
try:
raise c()
except D:
print "D"
except C:
print "C"
except B:
print "B"
Note that if the except clauses were reversed (with "except
B" first), it would have printed B, B, B -- the first matching except
clause is triggered.
When an error message is printed for an unhandled exception, the exception's
class name is printed, then a colon and a space, and finally the instance
converted to a string using the built-in function str().
Iterators
By now you have probably noticed that most container objects can be looped
over using a for statement:
for element in [1, 2, 3]:
print element
for element in (1, 2, 3):
print element
for key in {'one':1, 'two':2}:
print key
for char in "123":
print char
for line in open("myfile.txt"):
print line
This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the for
statement calls iter() on the container object. The
function returns an iterator object that defines the method
next() which accesses elements in the container one at a time. When there
are no more elements, next() raises a
StopIteration exception which tells the
for loop to terminate. This example shows how it all
works:
>>> s = 'abc'
>>> it = iter(s)
>>> it
<iterator object at 0x00A1DB50>
>>> it.next()
'a'
>>> it.next()
'b'
>>> it.next()
'c'
>>> it.next()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
it.next()
StopIteration
Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define a __iter__()
method which returns an object with a next() method. If
the class defines next(), then __iter__()
can just return self:
class Reverse:
"Iterator for looping over a sequence backwards"
def __init__(self, data):
self.data = data
self.index = len(data)
def __iter__(self):
return self
def next(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]
>>> for char in Reverse('spam'):
... print char
...
m
a
p
s
Generators
Generators are a simple and powerful tool for creating iterators. They are
written like regular functions but use the yield
statement whenever they want to return data. Each time next()
is called, the generator resumes where it left-off (it remembers all the data
values and which statement was last executed). An example shows that generators
can be trivially easy to create:
def reverse(data):
for index in range(len(data)-1, -1, -1):
yield data[index]
>>> for char in reverse('golf'):
... print char
...
f
l
o
g
Anything that can be done with generators can also be done with class based
iterators as described in the previous section. What makes generators so compact
is that the __iter__() and next()
methods are created automatically.
Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and
much more clear than an approach using instance variables like self.index
and self.data.
In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise
StopIteration. In combination, these features make it easy to create
iterators with no more effort than writing a regular function.
Generator Expressions
Some simple generators can be coded succinctly as expressions using a syntax
similar to list comprehensions but with parentheses instead of brackets. These
expressions are designed for situations where the generator is used right away
by an enclosing function. Generator expressions are more compact but less
versatile than full generator definitions and tend to be more memory friendly
than equivalent list comprehensions.
Examples:
>>> sum(i*i for i in range(10)) # sum of squares
285
>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260
>>> from math import pi, sin
>>> sine_table = dict((x, sin(x*pi/180)) for x in range(0, 91))
>>> unique_words = set(word for line in page for word in line.split())
>>> valedictorian = max((student.gpa, student.name) for student in graduates)
>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1,-1,-1))
['f', 'l', 'o', 'g']
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