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 <div class="section" id="classes">
<h1>9. Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h1>
Compared with other programming languages, Python&#8217;s class mechanism adds classes
with a minimum of new syntax and semantics. It is a mixture of the class
mechanisms found in C++ and Modula-3. Python classes provide all the standard
features of Object Oriented Programming: 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 arbitrary amounts and kinds of data. As is true for
modules, classes partake of the dynamic nature of Python: they are created at
runtime, and can be modified further after creation.
In C++ terminology, normally class members (including the data members) are
public (except see below <a class="reference internal" href="#tut-private">Private Variables and Class-local References</a>), and all member functions are
virtual. As in Modula-3, there are no shorthands for referencing the object&#8217;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. 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++, 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.)
<div class="section" id="a-word-about-names-and-objects">
<h2>9.1. A Word About Names and Objects<a class="headerlink" href="#a-word-about-names-and-objects" title="Permalink to this headline">¶</a></h2>
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 a possibly surprising effect on the semantics of Python code
involving mutable objects such as lists, dictionaries, and most other types.
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 &#8212; this eliminates the
need for two different argument passing mechanisms as in Pascal.
</div>
<div class="section" id="python-scopes-and-namespaces">
<h2>9.2. Python Scopes and Namespaces<a class="headerlink" href="#python-scopes-and-namespaces" title="Permalink to this headline">¶</a></h2>
Before introducing classes, I first have to tell you something about Python&#8217;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&#8217;s going on.
Incidentally, knowledge about this subject is useful for any advanced Python
programmer.
Let&#8217;s begin with some definitions.
A namespace is a mapping from names to objects. Most namespaces are currently
implemented as Python dictionaries, but that&#8217;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 (containing functions such as <a class="reference internal" href="../library/functions.html#abs" title="abs"><tt class="xref py py-func docutils literal">abs()</tt></a>, 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 <tt class="docutils literal">maximize</tt> without confusion &#8212;
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 &#8212; for
example, in the expression <tt class="docutils literal">z.real</tt>, <tt class="docutils literal">real</tt> is an attribute of the object
<tt class="docutils literal">z</tt>. Strictly speaking, references to names in modules are attribute
references: in the expression <tt class="docutils literal">modname.funcname</tt>, <tt class="docutils literal">modname</tt> is a module
object and <tt class="docutils literal">funcname</tt> is an attribute of it. In this case there happens to be
a straightforward mapping between the module&#8217;s attributes and the global names
defined in the module: they share the same namespace! <a class="footnote-reference" href="#id2" id="id1">[1]</a>
Attributes may be read-only or writable. In the latter case, assignment to
attributes is possible. Module attributes are writable: you can write
<tt class="docutils literal">modname.the_answer = 42</tt>. Writable attributes may also be deleted with the
<a class="reference internal" href="../reference/simple_stmts.html#del"><tt class="xref std std-keyword docutils literal">del</tt></a> statement. For example, <tt class="docutils literal">del modname.the_answer</tt> will remove
the attribute <tt class="xref py py-attr docutils literal">the_answer</tt> from the object named by <tt class="docutils literal">modname</tt>.
Namespaces 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 <a class="reference internal" href="../library/__main__.html#module-__main__" title="__main__: The environment where the top-level script is run."><tt class="xref py py-mod docutils literal">__main__</tt></a>, so they have their own
global namespace. (The built-in names actually also live in a module; this is
called <a class="reference internal" href="../library/__builtin__.html#module-__builtin__" title="__builtin__: The module that provides the built-in namespace."><tt class="xref py py-mod docutils literal">__builtin__</tt></a>.)
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. &#8220;Directly accessible&#8221; 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:
<ul class="simple">
<li>the innermost scope, which is searched first, contains the local names</li>
<li>the scopes of any enclosing functions, which are searched starting with the
nearest enclosing scope, contains non-local, but also non-global names</li>
<li>the next-to-last scope contains the current module&#8217;s global names</li>
<li>the outermost scope (searched last) is the namespace containing built-in names</li>
</ul>
If a name is declared global, then all references and assignments go directly to
the middle scope containing the module&#8217;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&#8217;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&#8217;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 &#8212; however, the
language definition is evolving towards static name resolution, at &#8220;compile&#8221;
time, so don&#8217;t rely on dynamic name resolution! (In fact, local variables are
already determined statically.)
A special quirk of Python is that &#8211; if no <a class="reference internal" href="../reference/simple_stmts.html#global"><tt class="xref std std-keyword docutils literal">global</tt></a> statement is in
effect &#8211; assignments to names always go into the innermost scope. Assignments
do not copy data &#8212; they just bind names to objects. The same is true for
deletions: the statement <tt class="docutils literal">del x</tt> removes the binding of <tt class="docutils literal">x</tt> from the
namespace referenced by the local scope. In fact, all operations that introduce
new names use the local scope: in particular, <a class="reference internal" href="../reference/simple_stmts.html#import"><tt class="xref std std-keyword docutils literal">import</tt></a> statements and
function definitions bind the module or function name in the local scope. (The
<a class="reference internal" href="../reference/simple_stmts.html#global"><tt class="xref std std-keyword docutils literal">global</tt></a> statement can be used to indicate that particular variables
live in the global scope.)
</div>
<div class="section" id="a-first-look-at-classes">
<h2>9.3. A First Look at Classes<a class="headerlink" href="#a-first-look-at-classes" title="Permalink to this headline">¶</a></h2>
Classes introduce a little bit of new syntax, three new object types, and some
new semantics.
<div class="section" id="class-definition-syntax">
<h3>9.3.1. Class Definition Syntax<a class="headerlink" href="#class-definition-syntax" title="Permalink to this headline">¶</a></h3>
The simplest form of class definition looks like this:
<div class="highlight-python"><pre>class ClassName:
 &lt;statement-1&gt;
 .
 .
 .
 &lt;statement-N&gt;</pre>
</div>
Class definitions, like function definitions (<a class="reference internal" href="../reference/compound_stmts.html#def"><tt class="xref std std-keyword docutils literal">def</tt></a> statements) must be
executed before they have any effect. (You could conceivably place a class
definition in a branch of an <a class="reference internal" href="../reference/compound_stmts.html#if"><tt class="xref std std-keyword docutils literal">if</tt></a> 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 &#8212; we&#8217;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 &#8212; again, this is explained later.
When a class definition is entered, a new namespace is created, and used as the
local scope &#8212; 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&#8217;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 (<tt class="xref py py-class docutils literal">ClassName</tt> in the
example).
</div>
<div class="section" id="class-objects">
<h3>9.3.2. Class Objects<a class="headerlink" href="#class-objects" title="Permalink to this headline">¶</a></h3>
Class objects support two kinds of operations: attribute references and
instantiation.
Attribute references use the standard syntax used for all attribute references
in Python: <tt class="docutils literal">obj.name</tt>. Valid attribute names are all the names that were in
the class&#8217;s namespace when the class object was created. So, if the class
definition looked like this:
<div class="highlight-python"><div class="highlight"><pre>class MyClass:
 &quot;&quot;&quot;A simple example class&quot;&quot;&quot;
 i = 12345
 def f(self):
 return &#39;hello world&#39;
</pre></div>
</div>
then <tt class="docutils literal">MyClass.i</tt> and <tt class="docutils literal">MyClass.f</tt> 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 <tt class="docutils literal">MyClass.i</tt> by assignment.
<tt class="xref py py-attr docutils literal">__doc__</tt> is also a valid attribute, returning the docstring belonging to
the class: <tt class="docutils literal">&quot;A simple example class&quot;</tt>.
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):
<div class="highlight-python"><div class="highlight"><pre>x = MyClass()
</pre></div>
</div>
creates a new instance of the class and assigns this object to the local
variable <tt class="docutils literal">x</tt>.
The instantiation operation (&#8220;calling&#8221; 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
<a class="reference internal" href="../reference/datamodel.html#object.__init__" title="object.__init__"><tt class="xref py py-meth docutils literal">__init__()</tt></a>, like this:
<div class="highlight-python"><div class="highlight"><pre>def __init__(self):
 self.data = []
</pre></div>
</div>
When a class defines an <a class="reference internal" href="../reference/datamodel.html#object.__init__" title="object.__init__"><tt class="xref py py-meth docutils literal">__init__()</tt></a> method, class instantiation
automatically invokes <a class="reference internal" href="../reference/datamodel.html#object.__init__" title="object.__init__"><tt class="xref py py-meth docutils literal">__init__()</tt></a> for the newly-created class instance. So
in this example, a new, initialized instance can be obtained by:
<div class="highlight-python"><div class="highlight"><pre>x = MyClass()
</pre></div>
</div>
Of course, the <a class="reference internal" href="../reference/datamodel.html#object.__init__" title="object.__init__"><tt class="xref py py-meth docutils literal">__init__()</tt></a> method may have arguments for greater
flexibility. In that case, arguments given to the class instantiation operator
are passed on to <a class="reference internal" href="../reference/datamodel.html#object.__init__" title="object.__init__"><tt class="xref py py-meth docutils literal">__init__()</tt></a>. For example,
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
...
&gt;&gt;&gt; x = Complex(3.0, -4.5)
&gt;&gt;&gt; x.r, x.i
(3.0, -4.5)
</pre></div>
</div>
</div>
<div class="section" id="instance-objects">
<h3>9.3.3. Instance Objects<a class="headerlink" href="#instance-objects" title="Permalink to this headline">¶</a></h3>
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 &#8220;instance variables&#8221; in Smalltalk, and to &#8220;data
members&#8221; in C++. Data attributes need not be declared; like local variables,
they spring into existence when they are first assigned to. For example, if
<tt class="docutils literal">x</tt> is the instance of <tt class="xref py py-class docutils literal">MyClass</tt> created above, the following piece of
code will print the value <tt class="docutils literal">16</tt>, without leaving a trace:
<div class="highlight-python"><div class="highlight"><pre>x.counter = 1
while x.counter &lt; 10:
 x.counter = x.counter * 2
print x.counter
del x.counter
</pre></div>
</div>
The other kind of instance attribute reference is a method. A method is a
function that &#8220;belongs to&#8221; 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&#8217;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, <tt class="docutils literal">x.f</tt> is a valid method
reference, since <tt class="docutils literal">MyClass.f</tt> is a function, but <tt class="docutils literal">x.i</tt> is not, since
<tt class="docutils literal">MyClass.i</tt> is not. But <tt class="docutils literal">x.f</tt> is not the same thing as <tt class="docutils literal">MyClass.f</tt> &#8212; it
is a method object, not a function object.
</div>
<div class="section" id="method-objects">
<h3>9.3.4. Method Objects<a class="headerlink" href="#method-objects" title="Permalink to this headline">¶</a></h3>
Usually, a method is called right after it is bound:
<div class="highlight-python"><div class="highlight"><pre>x.f()
</pre></div>
</div>
In the <tt class="xref py py-class docutils literal">MyClass</tt> example, this will return the string <tt class="docutils literal">'hello world'</tt>.
However, it is not necessary to call a method right away: <tt class="docutils literal">x.f</tt> is a method
object, and can be stored away and called at a later time. For example:
<div class="highlight-python"><div class="highlight"><pre>xf = x.f
while True:
 print xf()
</pre></div>
</div>
will continue to print <tt class="docutils literal">hello world</tt> until the end of time.
What exactly happens when a method is called? You may have noticed that
<tt class="docutils literal">x.f()</tt> was called without an argument above, even though the function
definition for <tt class="xref py py-meth docutils literal">f()</tt> specified an argument. What happened to the argument?
Surely Python raises an exception when a function that requires an argument is
called without any &#8212; even if the argument isn&#8217;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 <tt class="docutils literal">x.f()</tt> is exactly equivalent to <tt class="docutils literal">MyClass.f(x)</tt>. 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&#8217;s object before the first argument.
If you still don&#8217;t understand how methods work, a look at the implementation can
perhaps clarify matters. When an instance attribute is referenced that isn&#8217;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, a new argument list is constructed from the instance
object and the argument list, and the function object is called with this new
argument list.
</div>
</div>
<div class="section" id="random-remarks">
<h2>9.4. Random Remarks<a class="headerlink" href="#random-remarks" title="Permalink to this headline">¶</a></h2>
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
(&#8220;clients&#8221;) 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 &#8212; 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 &#8212; 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 &#8212;
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 <tt class="docutils literal">self</tt>. This is nothing more
than a convention: the name <tt class="docutils literal">self</tt> 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:
<div class="highlight-python"><div class="highlight"><pre># Function defined outside the class
def f1(self, x, y):
 return min(x, x+y)

class C:
 f = f1
 def g(self):
 return &#39;hello world&#39;
 h = g
</pre></div>
</div>
Now <tt class="docutils literal">f</tt>, <tt class="docutils literal">g</tt> and <tt class="docutils literal">h</tt> are all attributes of class <tt class="xref py py-class docutils literal">C</tt> that refer to
function objects, and consequently they are all methods of instances of
<tt class="xref py py-class docutils literal">C</tt> &#8212; <tt class="docutils literal">h</tt> being exactly equivalent to <tt class="docutils literal">g</tt>. 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 <tt class="docutils literal">self</tt>
argument:
<div class="highlight-python"><div class="highlight"><pre>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)
</pre></div>
</div>
Methods may reference global names in the same way as ordinary functions. The
global scope associated with a method is the module containing its
definition. (A class 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&#8217;ll find some good
reasons why a method would want to reference its own class.
Each value is an object, and therefore has a class (also called its type).
It is stored as <tt class="docutils literal">object.__class__</tt>.
</div>
<div class="section" id="inheritance">
<h2>9.5. Inheritance<a class="headerlink" href="#inheritance" title="Permalink to this headline">¶</a></h2>
Of course, a language feature would not be worthy of the name &#8220;class&#8221; without
supporting inheritance. The syntax for a derived class definition looks like
this:
<div class="highlight-python"><pre>class DerivedClassName(BaseClassName):
 &lt;statement-1&gt;
 .
 .
 .
 &lt;statement-N&gt;</pre>
</div>
The name <tt class="xref py py-class docutils literal">BaseClassName</tt> 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:
<div class="highlight-python"><pre>class DerivedClassName(modname.BaseClassName):</pre>
</div>
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&#8217;s nothing special about instantiation of derived classes:
<tt class="docutils literal">DerivedClassName()</tt> 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 <tt class="docutils literal">virtual</tt>.)
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 <tt class="docutils literal">BaseClassName.methodname(self,
arguments)</tt>. This is occasionally useful to clients as well. (Note that this
only works if the base class is accessible as <tt class="docutils literal">BaseClassName</tt> in the global
scope.)
Python has two built-in functions that work with inheritance:
<ul class="simple">
<li>Use <a class="reference internal" href="../library/functions.html#isinstance" title="isinstance"><tt class="xref py py-func docutils literal">isinstance()</tt></a> to check an instance&#8217;s type: <tt class="docutils literal">isinstance(obj, int)</tt>
will be <tt class="docutils literal">True</tt> only if <tt class="docutils literal">obj.__class__</tt> is <a class="reference internal" href="../library/functions.html#int" title="int"><tt class="xref py py-class docutils literal">int</tt></a> or some class
derived from <a class="reference internal" href="../library/functions.html#int" title="int"><tt class="xref py py-class docutils literal">int</tt></a>.</li>
<li>Use <a class="reference internal" href="../library/functions.html#issubclass" title="issubclass"><tt class="xref py py-func docutils literal">issubclass()</tt></a> to check class inheritance: <tt class="docutils literal">issubclass(bool, int)</tt>
is <tt class="docutils literal">True</tt> since <a class="reference internal" href="../library/functions.html#bool" title="bool"><tt class="xref py py-class docutils literal">bool</tt></a> is a subclass of <a class="reference internal" href="../library/functions.html#int" title="int"><tt class="xref py py-class docutils literal">int</tt></a>. However,
<tt class="docutils literal">issubclass(unicode, str)</tt> is <tt class="docutils literal">False</tt> since <a class="reference internal" href="../library/functions.html#unicode" title="unicode"><tt class="xref py py-class docutils literal">unicode</tt></a> is not a
subclass of <a class="reference internal" href="../library/functions.html#str" title="str"><tt class="xref py py-class docutils literal">str</tt></a> (they only share a common ancestor,
<a class="reference internal" href="../library/functions.html#basestring" title="basestring"><tt class="xref py py-class docutils literal">basestring</tt></a>).</li>
</ul>
<div class="section" id="multiple-inheritance">
<h3>9.5.1. Multiple Inheritance<a class="headerlink" href="#multiple-inheritance" title="Permalink to this headline">¶</a></h3>
Python supports a limited form of multiple inheritance as well. A class
definition with multiple base classes looks like this:
<div class="highlight-python"><pre>class DerivedClassName(Base1, Base2, Base3):
 &lt;statement-1&gt;
 .
 .
 .
 &lt;statement-N&gt;</pre>
</div>
For old-style classes, the only rule is depth-first, left-to-right. Thus, if an
attribute is not found in <tt class="xref py py-class docutils literal">DerivedClassName</tt>, it is searched in
<tt class="xref py py-class docutils literal">Base1</tt>, then (recursively) in the base classes of <tt class="xref py py-class docutils literal">Base1</tt>, and
only if it is not found there, it is searched in <tt class="xref py py-class docutils literal">Base2</tt>, and so on.
(To some people breadth first &#8212; searching <tt class="xref py py-class docutils literal">Base2</tt> and <tt class="xref py py-class docutils literal">Base3</tt>
before the base classes of <tt class="xref py py-class docutils literal">Base1</tt> &#8212; looks more natural. However, this
would require you to know whether a particular attribute of <tt class="xref py py-class docutils literal">Base1</tt> is
actually defined in <tt class="xref py py-class docutils literal">Base1</tt> or in one of its base classes before you can
figure out the consequences of a name conflict with an attribute of
<tt class="xref py py-class docutils literal">Base2</tt>. The depth-first rule makes no differences between direct and
inherited attributes of <tt class="xref py py-class docutils literal">Base1</tt>.)
For <a class="reference internal" href="../glossary.html#term-new-style-class">new-style class</a>es, the method resolution order changes dynamically
to support cooperative calls to <a class="reference internal" href="../library/functions.html#super" title="super"><tt class="xref py py-func docutils literal">super()</tt></a>. 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 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
<a class="reference internal" href="../library/functions.html#object" title="object"><tt class="xref py py-class docutils literal">object</tt></a>, so any case of multiple inheritance provides more than one path
to reach <a class="reference internal" href="../library/functions.html#object" title="object"><tt class="xref py py-class docutils literal">object</tt></a>. 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. For more detail, see
<a class="reference external" href="http://www.python.org/download/releases/2.3/mro/">http://www.python.org/download/releases/2.3/mro/</a>.
</div>
</div>
<div class="section" id="private-variables-and-class-local-references">
<h2>9.6. Private Variables and Class-local References<a class="headerlink" href="#private-variables-and-class-local-references" title="Permalink to this headline">¶</a></h2>
&#8220;Private&#8221; instance variables that cannot be accessed except from inside an
object don&#8217;t exist in Python. However, there is a convention that is followed
by most Python code: a name prefixed with an underscore (e.g. <tt class="docutils literal">_spam</tt>) should
be treated as a non-public part of the API (whether it is a function, a method
or a data member). It should be considered an implementation detail and subject
to change without notice.
Since there is a valid use-case for class-private members (namely to avoid name
clashes of names with names defined by subclasses), there is limited support for
such a mechanism, called name mangling. Any identifier of the form
<tt class="docutils literal">__spam</tt> (at least two leading underscores, at most one trailing underscore)
is textually replaced with <tt class="docutils literal">_classname__spam</tt>, where <tt class="docutils literal">classname</tt> is the
current class name with leading underscore(s) stripped. This mangling is done
without regard to the syntactic position of the identifier, as long as it
occurs within the definition of a class.
Name mangling is helpful for letting subclasses override methods without
breaking intraclass method calls. For example:
<div class="highlight-python"><div class="highlight"><pre>class Mapping:
 def __init__(self, iterable):
 self.items_list = []
 self.__update(iterable)

def update(self, iterable):
 for item in iterable:
 self.items_list.append(item)

__update = update # private copy of original update() method

class MappingSubclass(Mapping):

def update(self, keys, values):
 # provides new signature for update()
 # but does not break __init__()
 for item in zip(keys, values):
 self.items_list.append(item)
</pre></div>
</div>
Note that the mangling rules are designed mostly to avoid accidents; it still is
possible to access or modify a variable that is considered private. This can
even be useful in special circumstances, such as in the debugger.
Notice that code passed to <tt class="docutils literal">exec</tt>, <tt class="docutils literal">eval()</tt> or <tt class="docutils literal">execfile()</tt> does not
consider the classname of the invoking class to be the current class; this is
similar to the effect of the <tt class="docutils literal">global</tt> statement, the effect of which is
likewise restricted to code that is byte-compiled together. The same
restriction applies to <tt class="docutils literal">getattr()</tt>, <tt class="docutils literal">setattr()</tt> and <tt class="docutils literal">delattr()</tt>, as well
as when referencing <tt class="docutils literal">__dict__</tt> directly.
</div>
<div class="section" id="odds-and-ends">
<h2>9.7. Odds and Ends<a class="headerlink" href="#odds-and-ends" title="Permalink to this headline">¶</a></h2>
Sometimes it is useful to have a data type similar to the Pascal &#8220;record&#8221; or C
&#8220;struct&#8221;, bundling together a few named data items. An empty class definition
will do nicely:
<div class="highlight-python"><div class="highlight"><pre>class Employee:
 pass

john = Employee() # Create an empty employee record

# Fill the fields of the record
john.name = &#39;John Doe&#39;
john.dept = &#39;computer lab&#39;
john.salary = 1000
</pre></div>
</div>
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 <tt class="xref py py-meth docutils literal">read()</tt> and <a class="reference internal" href="../library/readline.html#module-readline" title="readline: GNU readline support for Python. (Unix)"><tt class="xref py py-meth docutils literal">readline()</tt></a> that get the
data from a string buffer instead, and pass it as an argument.
Instance method objects have attributes, too: <tt class="docutils literal">m.im_self</tt> is the instance
object with the method <tt class="xref py py-meth docutils literal">m()</tt>, and <tt class="docutils literal">m.im_func</tt> is the function object
corresponding to the method.
</div>
<div class="section" id="exceptions-are-classes-too">
<h2>9.8. Exceptions Are Classes Too<a class="headerlink" href="#exceptions-are-classes-too" title="Permalink to this headline">¶</a></h2>
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 <a class="reference internal" href="../reference/simple_stmts.html#raise"><tt class="xref std std-keyword docutils literal">raise</tt></a> statement:
<div class="highlight-python"><div class="highlight"><pre>raise Class, instance

raise instance
</pre></div>
</div>
In the first form, <tt class="docutils literal">instance</tt> must be an instance of <tt class="xref py py-class docutils literal">Class</tt> or of a
class derived from it. The second form is a shorthand for:
<div class="highlight-python"><div class="highlight"><pre>raise instance.__class__, instance
</pre></div>
</div>
A class in an <a class="reference internal" href="../reference/compound_stmts.html#except"><tt class="xref std std-keyword docutils literal">except</tt></a> clause is compatible with an exception if it is
the same class or a base class thereof (but not the other way around &#8212; 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:
<div class="highlight-python"><div class="highlight"><pre>class B:
 pass
class C(B):
 pass
class D(C):
 pass

for c in [B, C, D]:
 try:
 raise c()
 except D:
 print &quot;D&quot;
 except C:
 print &quot;C&quot;
 except B:
 print &quot;B&quot;
</pre></div>
</div>
Note that if the except clauses were reversed (with <tt class="docutils literal">except B</tt> first), it
would have printed B, B, B &#8212; the first matching except clause is triggered.
When an error message is printed for an unhandled exception, the exception&#8217;s
class name is printed, then a colon and a space, and finally the instance
converted to a string using the built-in function <a class="reference internal" href="../library/functions.html#str" title="str"><tt class="xref py py-func docutils literal">str()</tt></a>.
</div>
<div class="section" id="iterators">
<h2>9.9. Iterators<a class="headerlink" href="#iterators" title="Permalink to this headline">¶</a></h2>
By now you have probably noticed that most container objects can be looped over
using a <a class="reference internal" href="../reference/compound_stmts.html#for"><tt class="xref std std-keyword docutils literal">for</tt></a> statement:
<div class="highlight-python"><div class="highlight"><pre>for element in [1, 2, 3]:
 print element
for element in (1, 2, 3):
 print element
for key in {&#39;one&#39;:1, &#39;two&#39;:2}:
 print key
for char in &quot;123&quot;:
 print char
for line in open(&quot;myfile.txt&quot;):
 print line,
</pre></div>
</div>
This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the <a class="reference internal" href="../reference/compound_stmts.html#for"><tt class="xref std std-keyword docutils literal">for</tt></a> statement
calls <a class="reference internal" href="../library/functions.html#iter" title="iter"><tt class="xref py py-func docutils literal">iter()</tt></a> on the container object. The function returns an iterator
object that defines the method <a class="reference internal" href="../library/functions.html#next" title="next"><tt class="xref py py-meth docutils literal">next()</tt></a> which accesses elements in the
container one at a time. When there are no more elements, <a class="reference internal" href="../library/functions.html#next" title="next"><tt class="xref py py-meth docutils literal">next()</tt></a> raises a
<a class="reference internal" href="../library/exceptions.html#exceptions.StopIteration" title="exceptions.StopIteration"><tt class="xref py py-exc docutils literal">StopIteration</tt></a> exception which tells the <a class="reference internal" href="../reference/compound_stmts.html#for"><tt class="xref std std-keyword docutils literal">for</tt></a> loop to terminate.
This example shows how it all works:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; s = &#39;abc&#39;
&gt;&gt;&gt; it = iter(s)
&gt;&gt;&gt; it
&lt;iterator object at 0x00A1DB50&gt;
&gt;&gt;&gt; it.next()
&#39;a&#39;
&gt;&gt;&gt; it.next()
&#39;b&#39;
&gt;&gt;&gt; it.next()
&#39;c&#39;
&gt;&gt;&gt; it.next()
Traceback (most recent call last):
 File &quot;&lt;stdin&gt;&quot;, line 1, in ?
 it.next()
StopIteration
</pre></div>
</div>
Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define an <a class="reference internal" href="../reference/datamodel.html#object.__iter__" title="object.__iter__"><tt class="xref py py-meth docutils literal">__iter__()</tt></a> method which
returns an object with a <a class="reference internal" href="../library/functions.html#next" title="next"><tt class="xref py py-meth docutils literal">next()</tt></a> method. If the class defines
<a class="reference internal" href="../library/functions.html#next" title="next"><tt class="xref py py-meth docutils literal">next()</tt></a>, then <a class="reference internal" href="../reference/datamodel.html#object.__iter__" title="object.__iter__"><tt class="xref py py-meth docutils literal">__iter__()</tt></a> can just return <tt class="docutils literal">self</tt>:
<div class="highlight-python"><div class="highlight"><pre>class Reverse:
 &quot;&quot;&quot;Iterator for looping over a sequence backwards.&quot;&quot;&quot;
 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]
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; rev = Reverse(&#39;spam&#39;)
&gt;&gt;&gt; iter(rev)
&lt;__main__.Reverse object at 0x00A1DB50&gt;
&gt;&gt;&gt; for char in rev:
... print char
...
m
a
p
s
</pre></div>
</div>
</div>
<div class="section" id="generators">
<h2>9.10. Generators<a class="headerlink" href="#generators" title="Permalink to this headline">¶</a></h2>
<a class="reference internal" href="../glossary.html#term-generator">Generator</a>s are a simple and powerful tool for creating iterators. They
are written like regular functions but use the <a class="reference internal" href="../reference/simple_stmts.html#yield"><tt class="xref std std-keyword docutils literal">yield</tt></a> statement
whenever they want to return data. Each time <a class="reference internal" href="../library/functions.html#next" title="next"><tt class="xref py py-meth docutils literal">next()</tt></a> 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:
<div class="highlight-python"><div class="highlight"><pre>def reverse(data):
 for index in range(len(data)-1, -1, -1):
 yield data[index]
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; for char in reverse(&#39;golf&#39;):
... print char
...
f
l
o
g
</pre></div>
</div>
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 <a class="reference internal" href="../reference/datamodel.html#object.__iter__" title="object.__iter__"><tt class="xref py py-meth docutils literal">__iter__()</tt></a> and <a class="reference internal" href="../library/functions.html#next" title="next"><tt class="xref py py-meth docutils literal">next()</tt></a> 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 <tt class="docutils literal">self.index</tt>
and <tt class="docutils literal">self.data</tt>.
In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise <a class="reference internal" href="../library/exceptions.html#exceptions.StopIteration" title="exceptions.StopIteration"><tt class="xref py py-exc docutils literal">StopIteration</tt></a>. In
combination, these features make it easy to create iterators with no more effort
than writing a regular function.
</div>
<div class="section" id="generator-expressions">
<h2>9.11. Generator Expressions<a class="headerlink" href="#generator-expressions" title="Permalink to this headline">¶</a></h2>
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:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; sum(i*i for i in range(10)) # sum of squares
285

&gt;&gt;&gt; xvec = [10, 20, 30]
&gt;&gt;&gt; yvec = [7, 5, 3]
&gt;&gt;&gt; sum(x*y for x,y in zip(xvec, yvec)) # dot product
260

&gt;&gt;&gt; from math import pi, sin
&gt;&gt;&gt; sine_table = dict((x, sin(x*pi/180)) for x in range(0, 91))

&gt;&gt;&gt; unique_words = set(word for line in page for word in line.split())

&gt;&gt;&gt; valedictorian = max((student.gpa, student.name) for student in graduates)

&gt;&gt;&gt; data = &#39;golf&#39;
&gt;&gt;&gt; list(data[i] for i in range(len(data)-1,-1,-1))
[&#39;f&#39;, &#39;l&#39;, &#39;o&#39;, &#39;g&#39;]
</pre></div>
</div>
Footnotes
<table class="docutils footnote" frame="void" id="id2" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id1">[1]</a></td><td>Except for one thing. Module objects have a secret read-only attribute called
<tt class="xref py py-attr docutils literal">__dict__</tt> which returns the dictionary used to implement the module&#8217;s
namespace; the name <tt class="xref py py-attr docutils literal">__dict__</tt> is an attribute but not a global name.
Obviously, using this violates the abstraction of namespace implementation, and
should be restricted to things like post-mortem debuggers.</td></tr>
</tbody>
</table>
</div>
</div>

</div>
 </div>
 </div>
 <div class="sphinxsidebar">
 <div class="sphinxsidebarwrapper">
 <h3><a href="../contents.html">Table Of Contents</a></h3>
 <ul>
<li><a class="reference internal" href="#">9. Classes</a><ul>
<li><a class="reference internal" href="#a-word-about-names-and-objects">9.1. A Word About Names and Objects</a></li>
<li><a class="reference internal" href="#python-scopes-and-namespaces">9.2. Python Scopes and Namespaces</a></li>
<li><a class="reference internal" href="#a-first-look-at-classes">9.3. A First Look at Classes</a><ul>
<li><a class="reference internal" href="#class-definition-syntax">9.3.1. Class Definition Syntax</a></li>
<li><a class="reference internal" href="#class-objects">9.3.2. Class Objects</a></li>
<li><a class="reference internal" href="#instance-objects">9.3.3. Instance Objects</a></li>
<li><a class="reference internal" href="#method-objects">9.3.4. Method Objects</a></li>
</ul>
</li>
<li><a class="reference internal" href="#random-remarks">9.4. Random Remarks</a></li>
<li><a class="reference internal" href="#inheritance">9.5. Inheritance</a><ul>
<li><a class="reference internal" href="#multiple-inheritance">9.5.1. Multiple Inheritance</a></li>
</ul>
</li>
<li><a class="reference internal" href="#private-variables-and-class-local-references">9.6. Private Variables and Class-local References</a></li>
<li><a class="reference internal" href="#odds-and-ends">9.7. Odds and Ends</a></li>
<li><a class="reference internal" href="#exceptions-are-classes-too">9.8. Exceptions Are Classes Too</a></li>
<li><a class="reference internal" href="#iterators">9.9. Iterators</a></li>
<li><a class="reference internal" href="#generators">9.10. Generators</a></li>
<li><a class="reference internal" href="#generator-expressions">9.11. Generator Expressions</a></li>
</ul>
</li>
</ul>

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 title="previous chapter">8. Errors and Exceptions</a>
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