This is Info file /home/pdm/tmp/Python-1.5.2p1/Doc/tut/python-tut.info,
produced by Makeinfo version 1.68 from the input file tut.texi.

   July 6, 1999			1.5.2


File: python-tut.info,  Node: Exceptions,  Next: Handling Exceptions,  Prev: Syntax Errors,  Up: Errors and Exceptions

Exceptions
==========

   Even if a statement or expression is syntactically correct, it may
cause an error when an attempt is made to execute it.  Errors detected
during execution are called *exceptions* and are not unconditionally
fatal: you will soon learn how to handle them in Python programs.  Most
exceptions are not handled by programs, however, and result in error
messages as shown here:

     >>> 10 * (1/0)
     Traceback (innermost last):
       File "<stdin>", line 1
     ZeroDivisionError: integer division or modulo
     >>> 4 + spam*3
     Traceback (innermost last):
       File "<stdin>", line 1
     NameError: spam
     >>> '2' + 2
     Traceback (innermost last):
       File "<stdin>", line 1
     TypeError: illegal argument type for built-in operation

   The last line of the error message indicates what happened.
Exceptions come in different types, and the type is printed as part of
the message: the types in the example are `ZeroDivisionError',
`NameError' and `TypeError'.  The string printed as the exception type
is the name of the built-in name for the exception that occurred.  This
is true for all built-in exceptions, but need not be true for
user-defined exceptions (although it is a useful convention).  Standard
exception names are built-in identifiers (not reserved keywords).

   The rest of the line is a detail whose interpretation depends on the
exception type; its meaning is dependent on the exception type.

   The preceding part of the error message shows the context where the
exception happened, in the form of a stack backtrace.  In general it
contains a stack backtrace listing source lines; however, it will not
display lines read from standard input.

   The Library Reference lists the built-in exceptions and their
meanings.


File: python-tut.info,  Node: Handling Exceptions,  Next: Raising Exceptions,  Prev: Exceptions,  Up: Errors and Exceptions

Handling Exceptions
===================

   It is possible to write programs that handle selected exceptions.
Look at the following example, which prints a table of inverses of some
floating point numbers:

     >>> numbers = [0.3333, 2.5, 0, 10]
     >>> for x in numbers:
     ...     print x,
     ...     try:
     ...         print 1.0 / x
     ...     except ZeroDivisionError:
     ...         print '*** has no inverse ***'
     ...
     0.3333 3.00030003
     2.5 0.4
     0 *** has no inverse ***
     10 0.1

   The `try' statement works as follows.
   * First, the *try clause* (the statement(s) between the `try' and
     `except' keywords) is executed.

   * If no exception occurs, the *except clause* is skipped and
     execution of the `try' statement is finished.

   * If an exception occurs during execution of the try clause, the
     rest of the clause is skipped.  Then if its type matches the
     exception named after the `except' keyword, the rest of the try
     clause is skipped, the except clause is executed, and then
     execution continues after the `try' statement.

   * If an exception occurs which does not match the exception named in
     the except clause, it is passed on to outer `try' statements; if
     no handler is found, it is an *unhandled exception* and execution
     stops with a message as shown above.

   A `try' statement may have more than one except clause, to specify
handlers for different exceptions.  At most one handler will be
executed.  Handlers only handle exceptions that occur in the
corresponding try clause, not in other handlers of the same `try'
statement.  An except clause may name multiple exceptions as a
parenthesized list, e.g.:

     ... except (RuntimeError, TypeError, NameError):
     ...     pass

   The last except clause may omit the exception name(s), to serve as a
wildcard.  Use this with extreme caution, since it is easy to mask a
real programming error in this way!

   The `try' ... `except' statement has an optional *else clause*,
which must follow all except clauses.  It is useful to place code that
must be executed if the try clause does not raise an exception.  For
example:

     for arg in sys.argv[1:]:
         try:
             f = open(arg, 'r')
         except IOError:
             print 'cannot open', arg
         else:
             print arg, 'has', len(f.readlines()), 'lines'
             f.close()

   When an exception occurs, it may have an associated value, also
known as the exceptions's *argument*.  The presence and type of the
argument depend on the exception type.  For exception types which have
an argument, the except clause may specify a variable after the
exception name (or list) to receive the argument's value, as follows:

     >>> try:
     ...     spam()
     ... except NameError, x:
     ...     print 'name', x, 'undefined'
     ...
     name spam undefined

   If an exception has an argument, it is printed as the last part
(`detail') of the message for unhandled exceptions.

   Exception handlers don't just handle exceptions if they occur
immediately in the try clause, but also if they occur inside functions
that are called (even indirectly) in the try clause.  For example:

     >>> def this_fails():
     ...     x = 1/0
     ...
     >>> try:
     ...     this_fails()
     ... except ZeroDivisionError, detail:
     ...     print 'Handling run-time error:', detail
     ...
     Handling run-time error: integer division or modulo


File: python-tut.info,  Node: Raising Exceptions,  Next: User-defined Exceptions,  Prev: Handling Exceptions,  Up: Errors and Exceptions

Raising Exceptions
==================

   The `raise' statement allows the programmer to force a specified
exception to occur.  For example:

     >>> raise NameError, 'HiThere'
     Traceback (innermost last):
       File "<stdin>", line 1
     NameError: HiThere

   The first argument to `raise' names the exception to be raised.  The
optional second argument specifies the exception's argument.


File: python-tut.info,  Node: User-defined Exceptions,  Next: Defining Clean-up Actions,  Prev: Raising Exceptions,  Up: Errors and Exceptions

User-defined Exceptions
=======================

   Programs may name their own exceptions by assigning a string to a
variable.  For example:

     >>> my_exc = 'my_exc'
     >>> try:
     ...     raise my_exc, 2*2
     ... except my_exc, val:
     ...     print 'My exception occurred, value:', val
     ...
     My exception occurred, value: 4
     >>> raise my_exc, 1
     Traceback (innermost last):
       File "<stdin>", line 1
     my_exc: 1

   Many standard modules use this to report errors that may occur in
functions they define.


File: python-tut.info,  Node: Defining Clean-up Actions,  Prev: User-defined Exceptions,  Up: Errors and Exceptions

Defining Clean-up Actions
=========================

   The `try' statement has another optional clause which is intended to
define clean-up actions that must be executed under all circumstances.
For example:

     >>> try:
     ...     raise KeyboardInterrupt
     ... finally:
     ...     print 'Goodbye, world!'
     ...
     Goodbye, world!
     Traceback (innermost last):
       File "<stdin>", line 2
     KeyboardInterrupt

   A *finally clause* is executed whether or not an exception has
occurred in the try clause.  When an exception has occurred, it is
re-raised after the finally clause is executed.  The finally clause is
also executed "on the way out" when the `try' statement is left via a
`break' or `return' statement.

   A `try' statement must either have one or more except clauses or one
finally clause, but not both.


File: python-tut.info,  Node: Classes,  Next: What Now?,  Prev: Errors and Exceptions,  Up: Top

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.  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.  But, just like in C++ or
Modula-3, built-in types cannot 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.

* Menu:

* A Word About Terminology::
* Python Scopes and Name Spaces::
* A First Look at Classes::
* Random Remarks::
* Inheritance::
* Private Variables::
* Odds and Ends::


File: python-tut.info,  Node: A Word About Terminology,  Next: Python Scopes and Name Spaces,  Prev: Classes,  Up: Classes

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.)

   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 obviates
the need for two different argument passing mechanisms as in Pascal.


File: python-tut.info,  Node: Python Scopes and Name Spaces,  Next: A First Look at Classes,  Prev: A Word About Terminology,  Up: Classes

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
name spaces, and you need to know how scopes and name spaces 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 *name space* is a mapping from names to objects.  Most name spaces
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 name spaces 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 name space.  The
important thing to know about name spaces is that there is absolutely
no relation between names in different name spaces; 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 name space!(1)

   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, e.g.  `del modname.the_answer'.

   Name spaces are created at different moments and have different
lifetimes.  The name space containing the built-in names is created
when the Python interpreter starts up, and is never deleted.  The
global name space for a module is created when the module definition is
read in; normally, module name spaces 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 name space.  (The built-in names actually also live in a module;
this is called `__builtin__'.)

   The local name space 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 name space.

   A *scope* is a textual region of a Python program where a name space
is directly accessible.  "Directly accessible" here means that an
unqualified reference to a name attempts to find the name in the name
space.

   Although scopes are determined statically, they are used dynamically.
At any time during execution, exactly three nested scopes are in use
(i.e., exactly three name spaces are directly accessible): the
innermost scope, which is searched first, contains the local names, the
middle scope, searched next, contains the current module's global
names, and the outermost scope (searched last) is the name space
containing built-in names.

   Usually, the local scope references the local names of the
(textually) current function.  Outside of functions, the local scope
references the same name space as the global scope: the module's name
space.  Class definitions place yet another name space 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 name
space, 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 name space 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.)

   ---------- Footnotes ----------

   (1)  Except for one thing.  Module objects have a secret read-only
attribute called `__dict__' which returns the dictionary used to
implement the module's name space; the name `__dict__' is an attribute
but not a global name. Obviously, using this violates the abstraction
of name space implementation, and should be restricted to things like
post-mortem debuggers.


File: python-tut.info,  Node: A First Look at Classes,  Next: Random Remarks,  Prev: Python Scopes and Name Spaces,  Up: Classes

A First Look at Classes
=======================

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

* Menu:

* Class Definition Syntax::
* Class Objects::
* Instance Objects::
* Method Objects::


File: python-tut.info,  Node: Class Definition Syntax,  Next: Class Objects,  Prev: A First Look at Classes,  Up: A First Look at Classes

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 name space is created, and
used as the local scope -- thus, all assignments to local variables go
into this new name space.  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 name space 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).


File: python-tut.info,  Node: Class Objects,  Next: Instance Objects,  Prev: Class Definition Syntax,  Up: A First Look at Classes

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 name space when the class object
was created.  So, if the class definition looked like this:

     class MyClass:
         "A simple example class"
         i = 12345
         def f(x):
             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 that's
read-only, 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):

     x = MyClass()

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


File: python-tut.info,  Node: Instance Objects,  Next: Method Objects,  Prev: Class Objects,  Up: A First Look at Classes

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.

   The first I'll call *data attributes*.  These 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 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, e.g., 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.)

   Valid method names of an instance object depend on its class.  By
definition, all attributes of a class that are (user-defined) 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.


File: python-tut.info,  Node: Method Objects,  Prev: Instance Objects,  Up: A First Look at Classes

Method Objects
--------------

   Usually, a method is called immediately, e.g.:

     x.f()

   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:

     xf = x.f
     while 1:
         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.


File: python-tut.info,  Node: Random Remarks,  Next: Inheritance,  Prev: A First Look at Classes,  Up: Classes

Random Remarks
==============

   [These should perhaps be placed more carefully...]

   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, e.g., capitalize method names,
prefix data attribute names with a small unique string (perhaps just an
underscore), or use 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:

     # 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, e.g.:

     class Bag:
         def empty(self):
             self.data = []
         def add(self, x):
             self.data.append(x)
         def addtwice(self, x):
             self.add(x)
             self.add(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:

         def __init__(self):
             self.empty()

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

     x = Bag()

   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)

   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!


File: python-tut.info,  Node: Inheritance,  Next: Private Variables,  Prev: Random Remarks,  Up: Classes

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 as follows:

     class DerivedClassName(BaseClassName):
         <statement-1>
         .
         .
         .
         <statement-N>

   The name `BaseClassName' must be defined in a scope containing the
derived class definition.  Instead of a base class name, an expression
is also allowed.  This is useful when the base class is defined in
another module, e.g.,

     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, it is searched 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 in fact end up calling a method of a
derived class that overrides it.  (For C++ programmers: all methods in
Python are "virtual functions".)

   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.)

* Menu:

* Multiple Inheritance::


File: python-tut.info,  Node: Multiple Inheritance,  Prev: Inheritance,  Up: Inheritance

Multiple Inheritance
--------------------

   Python supports a limited form of multiple inheritance as well.  A
class definition with multiple base classes looks as follows:

     class DerivedClassName(Base1, Base2, Base3):
         <statement-1>
         .
         .
         .
         <statement-N>

   The only rule necessary to explain the semantics is the resolution
rule used for class attribute references.  This 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'.)

   It is clear that indiscriminate use of multiple inheritance is a
maintenance nightmare, given the reliance in Python on conventions to
avoid accidental name conflicts.  A well-known problem with multiple
inheritance is a class derived from two classes that happen to have a
common base class.  While it is easy enough to figure out what happens
in this case (the instance will have a single copy of "instance
variables" or data attributes used by the common base class), it is not
clear that these semantics are in any way useful.


File: python-tut.info,  Node: Private Variables,  Next: Odds and Ends,  Prev: Inheritance,  Up: Classes

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 now textually replaced with
`_classname__spam', where `classname' is the current class name with
leading underscore(s) stripped.  This mangling is done without regard
of the syntactic position of the identifier, so it can be used to
define class-private instance and class variables, methods, as well as
globals, and even to store instance variables 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, e.g. for 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 `evalfile()' 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.

   Here's an example of a class that implements its own `__getattr__'
and `__setattr__' methods and stores all attributes in a private
variable, in a way that works in Python 1.4 as well as in previous
versions:

     class VirtualAttributes:
         __vdict = None
         __vdict_name = locals().keys()[0]
     
         def __init__(self):
             self.__dict__[self.__vdict_name] = {}
     
         def __getattr__(self, name):
             return self.__vdict[name]
     
         def __setattr__(self, name, value):
             self.__vdict[name] = value


File: python-tut.info,  Node: Odds and Ends,  Prev: Private Variables,  Up: Classes

Odds and Ends
=============

   Sometimes it is useful to have a data type similar to the Pascal
"record" or C "struct", bundling together a couple of named data items.
An empty class definition will do nicely, e.g.:

     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 gets the data from a string buffer instead, and pass
it as an argument.

   Instance method objects have attributes, too: `m.im_self' is the
object of which the method is an instance, and `m.im_func' is the
function object corresponding to the method.

* Menu:

* Exceptions Can Be Classes::


File: python-tut.info,  Node: Exceptions Can Be Classes,  Prev: Odds and Ends,  Up: Odds and Ends

Exceptions Can Be Classes
-------------------------

   User-defined exceptions are no longer limited to being string objects
-- they can be 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

   An except clause may list classes as well as string objects.  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 which is
a class, the class name is printed, then a colon and a space, and
finally the instance converted to a string using the built-in function
`str()'.


File: python-tut.info,  Node: What Now?,  Next: Interactive Input Editing and History Substitution,  Prev: Classes,  Up: Top

What Now?
*********

   Hopefully reading this tutorial has reinforced your interest in using
Python.  Now what should you do?

   You should read, or at least page through, the Library Reference,
which gives complete (though terse) reference material about types,
functions, and modules that can save you a lot of time when writing
Python programs.  The standard Python distribution includes a *lot* of
code in both C and Python; there are modules to read UNIX mailboxes,
retrieve documents via HTTP, generate random numbers, parse
command-line options, write CGI programs, compress data, and a lot
more; skimming through the Library Reference will give you an idea of
what's available.

   The major Python Web site is `http://www.python.org'; it contains
code, documentation, and pointers to Python-related pages around the
Web.  This web site is mirrored in various places around the world,
such as Europe, Japan, and Australia; a mirror may be faster than the
main site, depending on your geographical location.  A more informal
site is `http://starship.python.net/', which contains a bunch of
Python-related personal home pages; many people have downloadable
software there.

   For Python-related questions and problem reports, you can post to the
newsgroup `comp.lang.python', or send them to the mailing list at
<python-list@cwi.nl>.  The newsgroup and mailing list are gatewayed, so
messages posted to one will automatically be forwarded to the other.
There are around 35-45 postings a day, asking (and answering)
questions, suggesting new features, and announcing new modules.  Before
posting, be sure to check the list of Frequently Asked Questions (also
called the FAQ), at `http://www.python.org/doc/FAQ.html', or look for
it in the `Misc/' directory of the Python source distribution.  The FAQ
answers many of the questions that come up again and again, and may
already contain the solution for your problem.

   You can support the Python community by joining the Python Software
Activity, which runs the python.org web, ftp and email servers, and
organizes Python workshops.  See `http://www.python.org/psa/' for
information on how to join.


File: python-tut.info,  Node: Interactive Input Editing and History Substitution,  Next: Module Index,  Prev: What Now?,  Up: Top

Interactive Input Editing and History Substitution
**************************************************

   Some versions of the Python interpreter support editing of the
current input line and history substitution, similar to facilities
found in the Korn shell and the GNU Bash shell.  This is implemented
using the *GNU Readline* library, which supports Emacs-style and
vi-style editing.  This library has its own documentation which I won't
duplicate here; however, the basics are easily explained.  The
interactive editing and history described here are optionally available
in the UNIX and CygWin versions of the interpreter.

   This chapter does *not* document the editing facilities of Mark
Hammond's PythonWin package or the Tk-based environment, IDLE,
distributed with Python.  The command line history recall which
operates within DOS boxes on NT and some other DOS and Windows flavors
is yet another beast.

* Menu:

* Line Editing::
* History Substitution::
* Key Bindings::
* Commentary::


File: python-tut.info,  Node: Line Editing,  Next: History Substitution,  Prev: Interactive Input Editing and History Substitution,  Up: Interactive Input Editing and History Substitution

Line Editing
============

   If supported, input line editing is active whenever the interpreter
prints a primary or secondary prompt.  The current line can be edited
using the conventional Emacs control characters.  The most important of
these are: C-A (Control-A) moves the cursor to the beginning of the
line, C-E to the end, C-B moves it one position to the left, C-F to the
right.  Backspace erases the character to the left of the cursor, C-D
the character to its right.  C-K kills (erases) the rest of the line to
the right of the cursor, C-Y yanks back the last killed string.
C-underscore undoes the last change you made; it can be repeated for
cumulative effect.


File: python-tut.info,  Node: History Substitution,  Next: Key Bindings,  Prev: Line Editing,  Up: Interactive Input Editing and History Substitution

History Substitution
====================

   History substitution works as follows.  All non-empty input lines
issued are saved in a history buffer, and when a new prompt is given
you are positioned on a new line at the bottom of this buffer.  C-P
moves one line up (back) in the history buffer, C-N moves one down.
Any line in the history buffer can be edited; an asterisk appears in
front of the prompt to mark a line as modified.  Pressing the Return
key passes the current line to the interpreter.  C-R starts an
incremental reverse search; C-S starts a forward search.


File: python-tut.info,  Node: Key Bindings,  Next: Commentary,  Prev: History Substitution,  Up: Interactive Input Editing and History Substitution

Key Bindings
============

   The key bindings and some other parameters of the Readline library
can be customized by placing commands in an initialization file called
`$HOME/.inputrc'.  Key bindings have the form

     key-name: function-name

   or

     "string": function-name

   and options can be set with

     set option-name value

   For example:

     # I prefer vi-style editing:
     set editing-mode vi
     # Edit using a single line:
     set horizontal-scroll-mode On
     # Rebind some keys:
     Meta-h: backward-kill-word
     "\C-u": universal-argument
     "\C-x\C-r": re-read-init-file

   Note that the default binding for TAB in Python is to insert a TAB
instead of Readline's default filename completion function.  If you
insist, you can override this by putting

     TAB: complete

   in your `$HOME/.inputrc'.  (Of course, this makes it hard to type
indented continuation lines...)

   Automatic completion of variable and module names is optionally
available.  To enable it in the interpreter's interactive mode, add the
following to your `$HOME/.pythonrc.py' file:

     import rlcompleter, readline
     readline.parse_and_bind('tab: complete')

   This binds the TAB key to the completion function, so hitting the TAB
key twice suggests completions; it looks at Python statement names, the
current local variables, and the available module names.  For dotted
expressions such as `string.a', it will evaluate the the expression up
to the final `.' and then suggest completions from the attributes of
the resulting object.  Note that this may execute application-defined
code if an object with a `__getattr__()' method is part of the
expression.


File: python-tut.info,  Node: Commentary,  Prev: Key Bindings,  Up: Interactive Input Editing and History Substitution

Commentary
==========

   This facility is an enormous step forward compared to previous
versions of the interpreter; however, some wishes are left: It would be
nice if the proper indentation were suggested on continuation lines
(the parser knows if an indent token is required next).  The completion
mechanism might use the interpreter's symbol table.  A command to check
(or even suggest) matching parentheses, quotes etc.  would also be
useful.


File: python-tut.info,  Node: Module Index,  Next: Class-Exception-Object Index,  Prev: Interactive Input Editing and History Substitution,  Up: Top

Module Index
************

* Menu:

* __builtin__:                           dir Function.
* compileall:                            Compiled Python files.
* pickle:                                pickle Module.
* readline:                              Key Bindings.
* rlcompleter:                           Key Bindings.
* string:                                Fancier Output Formatting.
* sys:                                   Standard Modules.


File: python-tut.info,  Node: Class-Exception-Object Index,  Next: Function-Method-Variable Index,  Prev: Module Index,  Up: Top

Class, Exception, and Object Index
**********************************

* Menu:

* file:                                  Reading and Writing Files.
* method:                                Instance Objects.


File: python-tut.info,  Node: Function-Method-Variable Index,  Next: Miscellaneous Index,  Prev: Class-Exception-Object Index,  Up: Top

Function, Method, and Variable Index
************************************

* Menu:

* open:                                  Reading and Writing Files.


File: python-tut.info,  Node: Miscellaneous Index,  Prev: Function-Method-Variable Index,  Up: Top

Miscellaneous Index
*******************

* Menu:

* .pythonrc.py file <1>:                 Key Bindings.
* .pythonrc.py file:                     Interactive Startup File.
* for:                                   for Statements.
* module search path:                    Module Search Path.