8.2 Exceptions

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# 8.2 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:
```text
>>> 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.
---
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8.3 Handling Exceptions

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# 8.3 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:
```text
>>> 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.:
```text
... 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:
```text
for arg in sys.argv:
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:
```text
>>> 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:
```text
>>> 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
```
---
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8.4 Raising Exceptions

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# 8.4 Raising Exceptions
The raise statement allows the programmer to force a
specified exception to occur.
For example:
```text
>>> 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.
---
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8.5 User-defined Exceptions

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# 8.5 User-defined Exceptions
Programs may name their own exceptions by assigning a string to a
variable.
For example:
```text
>>> 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.
---
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8.6 Defining Clean-up Actions

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# 8.6 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:
```text
>>> 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.
---
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9 Classes

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# 9 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(es), 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 members.
---
- 9.1 A word about terminology (node57.html#SECTION0010100000000000000000)
9.2 Python scopes and name spaces (node58.html#SECTION0010200000000000000000)
9.3 A first look at classes (node59.html#SECTION0010300000000000000000)
- Class definition syntax (node60.html#SECTION0010310000000000000000)
Class objects (node61.html#SECTION0010320000000000000000)
Instance objects (node62.html#SECTION0010330000000000000000)
Method objects (node63.html#SECTION0010340000000000000000)
9.4 Random remarks (node64.html#SECTION0010400000000000000000)
9.5 Inheritance (node65.html#SECTION0010500000000000000000)
- Multiple inheritance (node66.html#SECTION0010510000000000000000)
9.6 Private variables through name mangling (node67.html#SECTION0010600000000000000000)
9.7 Odds and ends (node68.html#SECTION0010700000000000000000)
- Exceptions Can Be Classes (node69.html#SECTION0010710000000000000000)
---
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9.1 A word about terminology

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# 9.1 A word about terminology
Lacking universally accepted terminology to talk about classes, I'll
make occasional use of Smalltalk and C++ terms. (I'd 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 aren't, 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.
---
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9.2 Python scopes and name spaces

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# 9.2 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!footnode.html#719
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.)
---
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9.3 A first look at classes

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# 9.3 A first look at classes
Classes introduce a little bit of new syntax, three new object types,
and some new semantics.
---
- Class definition syntax (node60.html#SECTION0010310000000000000000)
Class objects (node61.html#SECTION0010320000000000000000)
Instance objects (node62.html#SECTION0010330000000000000000)
Method objects (node63.html#SECTION0010340000000000000000)
---
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2.1 Invoking the Interpreter

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# 2.1 Invoking the Interpreter
The Python interpreter is usually installed as `/usr/local/bin/python'
on those machines where it is available; putting `/usr/local/bin' in
your Unix shell's search path makes it possible to start it by
typing the command
```text
python
```
to the shell. Since the choice of the directory where the interpreter
lives is an installation option, other places are possible; check with
your local Python guru or system administrator. (E.g.,
`/usr/local/python' is a popular alternative location.)
Typing an EOF character (Control-D on Unix, Control-Z or F6 on DOS
or Windows) at the primary prompt causes the interpreter to exit with
a zero exit status. If that doesn't work, you can exit the
interpreter by typing the following commands: `import sys;
sys.exit()'.
The interpreter's line-editing features usually aren't very
sophisticated. On Unix, whoever installed the interpreter may have
enabled support for the GNU readline library, which adds more
elaborate interactive editing and history features. Perhaps the
quickest check to see whether command line editing is supported is
typing Control-P to the first Python prompt you get. If it beeps, you
have command line editing; see Appendix A for an introduction to the
keys. If nothing appears to happen, or if P is echoed,
command line editing isn't available; you'll only be able to use
backspace to remove characters from the current line.
The interpreter operates somewhat like the Unix shell: when called
with standard input connected to a tty device, it reads and executes
commands interactively; when called with a file name argument or with
a file as standard input, it reads and executes a script from
that file.
A third way of starting the interpreter is
`python -c command [arg] ...', which
executes the statement(s) in command, analogous to the shell's
-c option. Since Python statements often contain spaces or other
characters that are special to the shell, it is best to quote
command in its entirety with double quotes.
Note that there is a difference between `python file' and
`python <file'. In the latter case, input requests from the
program, such as calls to input() and raw_input(), are
satisfied from file. Since this file has already been read
until the end by the parser before the program starts executing, the
program will encounter EOF immediately. In the former case (which is
usually what you want) they are satisfied from whatever file or device
is connected to standard input of the Python interpreter.
When a script file is used, it is sometimes useful to be able to run
the script and enter interactive mode afterwards. This can be done by
passing -i before the script. (This does not work if the script
is read from standard input, for the same reason as explained in the
previous paragraph.)
---
- Argument Passing (node7.html#SECTION003110000000000000000)
Interactive Mode (node8.html#SECTION003120000000000000000)
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Class definition syntax

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## Class definition syntax
The simplest form of class definition looks like this:
```text
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 class name given in
the class definition header (ClassName in the example).
---
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Class objects

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## 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:
```text
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):
```text
x = MyClass()
```
creates a new instance of the class and assigns this object to
the local variable x.
---
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Instance objects

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## 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:
```text
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.
---
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Method objects

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## Method objects
Usually, a method is called immediately, e.g.:
```text
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
moment, for example:
```text
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.
---
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9.4 Random remarks

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# 9.4 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:
```text
# 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.:
```text
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:
```text
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:
```text
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,
```text
>>> 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!
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9.5 Inheritance

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# 9.5 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:
```text
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.,
```text
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.)
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Multiple inheritance

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## Multiple inheritance
Python supports a limited form of multiple inheritance as well. A
class definition with multiple base classes looks as follows:
```text
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.
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# 9.6 Private variables through name mangling
There is now 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:
```text
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
```
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9.7 Odds and ends

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# 9.7 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.:
```text
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. (Unfortunately, this
technique has its limitations: a class can't define operations that
are accessed by special syntax such as sequence subscripting or
arithmetic operators, and assigning such a ``pseudo-file'' to
sys.stdin will not cause the interpreter to read further input
from it.)
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.
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Exceptions Can Be Classes

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## 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:
```text
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
```text
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:
```text
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().
In this release, the built-in exceptions are still strings.
---
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Argument Passing

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## Argument Passing
When known to the interpreter, the script name and additional
arguments thereafter are passed to the script in the variable
sys.argv, which is a list of strings. Its length is at least
one; when no script and no arguments are given, sys.argv[0] is
an empty string. When the script name is given as '-' (meaning
standard input), sys.argv[0] is set to '-'. When -c
command is used, sys.argv[0] is set to '-c'. Options
found after -c command are not consumed by the Python
interpreter's option processing but left in sys.argv for the
command to handle.
---
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10 What Now?

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# 10 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. www.python.org 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.skyport.net, which contains a
bunch of Python-related personal home pages; many people have
downloadable software here.
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 20-30 postings a day, asking (and
answering) questions, suggesting new features, and announcing new
modules. But 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.
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11 Recent Additions as of Release 1.1

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# 11 Recent Additions as of Release 1.1
---
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---
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11.1 Lambda Forms

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# 11.1 Lambda Forms
By popular demand, a few features commonly found in functional
programming languages and Lisp have been added to Python. With the
lambda keyword, small anonymous functions can be created.
Here's a function that returns the sum of its two arguments:
`lambda a, b: a+b'. Lambda forms can be used wherever function
objects are required. They are syntactically restricted to a single
expression. Semantically, they are just syntactic sugar for a normal
function definition. Like nested function definitions, lambda forms
cannot reference variables from the containing scope, but this can be
overcome through the judicious use of default argument values, e.g.
```text
def make_incrementor(n):
return lambda x, incr=n: x+incr
```
---
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11.2 Documentation Strings

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# 11.2 Documentation Strings
There are emerging conventions about the content and formatting of
documentation strings.
The first line should always be a short, concise summary of the
object's purpose. For brevity, it should not explicitly state the
object's name or type, since these are available by other means
(except if the name happens to be a verb describing a function's
operation). This line should begin with a capital letter and end with
a period.
If there are more lines in the documentation string, the second line
should be blank, visually separating the summary from the rest of the
description. The following lines should be one of more of paragraphs
describing the objects calling conventions, its side effects, etc.
Some people like to copy the Emacs convention of using UPPER CASE for
function parameters -- this often saves a few words or lines.
The Python parser does not strip indentation from multi-line string
literals in Python, so tools that process documentation have to strip
indentation. This is done using the following convention. The first
non-blank line after the first line of the string determines the
amount of indentation for the entire documentation string. (We can't
use the first line since it is generally adjacent to the string's
opening quotes so its indentation is not apparent in the string
literal.) Whitespace ``equivalent'' to this indentation is then
stripped from the start of all lines of the string. Lines that are
indented less should not occur, but if they occur all their leading
whitespace should be stripped. Equivalence of whitespace should be
tested after expansion of tabs (to 8 spaces, normally).
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# A 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.
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# A.1 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.
---
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A.2 History Substitution

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# A.2 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.
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A.3 Key Bindings

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# A.3 Key Bindings
The key bindings and some other parameters of the Readline library can
be customized by placing commands in an initialization file called
`$HOME/.inputrc'. Key bindings have the form
```text
key-name: function-name
```
or
```text
"string": function-name
```
and options can be set with
```text
set option-name value
```
For example:
```text
# I prefer vi-style editing:
set editing-mode vi
# Edit using a single line:
set horizontal-scroll-mode On
# Rebind some keys:
Meta-h: backward-kill-word
"\C-u": universal-argument
"\C-x\C-r": re-read-init-file
```
Note that the default binding for TAB in Python is to insert a TAB
instead of Readline's default filename completion function. If you
insist, you can override this by putting
```text
TAB: complete
```
in your `$HOME/.inputrc'. (Of course, this makes it hard to type
indented continuation lines...)
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A.4 Commentary

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# A.4 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.
---
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About this document ...

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# About this document ...
Python Tutorial
This document was generated using the LaTeX2HTML (http://www-dsed.llnl.gov/files/programs/unix/latex2html/manual/) translator Version 96.1 (Feb 5, 1996) Copyright © 1993, 1994, 1995, 1996, Nikos Drakos (http://cbl.leeds.ac.uk/nikos/personal.html), Computer Based Learning Unit, University of Leeds.
The command line arguments were:
latex2html -address guido@python.org tut.tex.
The translation was initiated by Fred L. Drake on Tue Feb 17 10:28:17 EST 1998
---
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Interactive Mode

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## Interactive Mode
When commands are read from a tty, the interpreter is said to be in
interactive mode. In this mode it prompts for the next command
with the primary prompt, usually three greater-than signs
(`>>> '); for continuation lines it prompts with the
secondary prompt,
by default three dots (`... ').
The interpreter prints a welcome message stating its version number
and a copyright notice before printing the first prompt, e.g.:
```text
python
Python 1.5b1 (#1, Dec 3 1997, 00:02:06) [GCC 2.7.2.2] on sunos5
Copyright 1991-1995 Stichting Mathematisch Centrum, Amsterdam
>>>
```
---
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2.2 The Interpreter and its Environment

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# 2.2 The Interpreter and its Environment
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Python Tutorial

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# Python Tutorial
Guido van Rossum
February 17, 1998
Copyright © 1991-1995 by Stichting Mathematisch Centrum,
Amsterdam, The Netherlands.
All Rights Reserved
Permission to use, copy, modify, and distribute this software and its
documentation for any purpose and without fee is hereby granted,
provided that the above copyright notice appear in all copies and that
both that copyright notice and this permission notice appear in
supporting documentation, and that the names of Stichting Mathematisch
Centrum or CWI or Corporation for National Research Initiatives or
CNRI not be used in advertising or publicity pertaining to
distribution of the software without specific, written prior
permission.
While CWI is the initial source for this software, a modified version
is made available by the Corporation for National Research Initiatives
(CNRI) at the Internet address ftp://ftp.python.org.
STICHTING MATHEMATISCH CENTRUM AND CNRI DISCLAIM ALL WARRANTIES WITH
REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH
CENTRUM OR CNRI BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
PERFORMANCE OF THIS SOFTWARE.
### Abstract:
Python is an easy to learn, powerful programming language. It has
efficient high-level data structures and a simple but effective
approach to object-oriented programming. Python's elegant syntax and
dynamic typing, together with its interpreted nature, make it an ideal
language for scripting and rapid application development in many areas
on most platforms.
The Python interpreter and the extensive standard library are freely
available in source or binary form for all major platforms from the
Python web site, http://www.python.org, and can be freely
distributed. The same site also contains distributions of and
pointers to many free third party Python modules, programs and tools,
and additional documentation.
The Python interpreter is easily extended with new functions and data
types implemented in C or C++ (or other languages callable from C).
Python is also suitable as an extension language for customizable
applications.
This tutorial introduces the reader informally to the basic concepts
and features of the Python language and system. It helps to have a
Python interpreter handy for hands-on experience, but all examples are
self-contained, so the tutorial can be read off-line as well.
For a description of standard objects and modules, see the
Python Library Reference document. The Python Reference
Manual gives a more formal definition of the language. To write
extensions in C or C++, read the Extending and Embedding and
Python/C API manuals. There are also several books covering
Python in depth.
This tutorial does not attempt to be comprehensive and cover every
single feature, or even every commonly used feature. Instead, it
introduces many of Python's most noteworthy features, and will give
you a good idea of the language's flavor and style. After reading it,
you will be able to read and write Python modules and programs, and
you will be ready to learn more about the various Python library
modules described in the Python Library Reference.
---
- Contents (node1.html#SECTION001000000000000000000)
1 Whetting Your Appetite (node2.html#SECTION002000000000000000000)
- 1.1 Introduction (node3.html#SECTION002100000000000000000)
1.2 Where From Here (node4.html#SECTION002200000000000000000)
2 Using the Python Interpreter (node5.html#SECTION003000000000000000000)
- 2.1 Invoking the Interpreter (node6.html#SECTION003100000000000000000)
- Argument Passing (node7.html#SECTION003110000000000000000)
Interactive Mode (node8.html#SECTION003120000000000000000)
2.2 The Interpreter and its Environment (node9.html#SECTION003200000000000000000)
- Error Handling (node10.html#SECTION003210000000000000000)
Executable Python scripts (node11.html#SECTION003220000000000000000)
The Interactive Startup File (node12.html#SECTION003230000000000000000)
3 An Informal Introduction to Python (node13.html#SECTION004000000000000000000)
- 3.1 Using Python as a Calculator (node14.html#SECTION004100000000000000000)
- Numbers (node15.html#SECTION004110000000000000000)
Strings (node16.html#SECTION004120000000000000000)
Lists (node17.html#SECTION004130000000000000000)
3.2 First Steps Towards Programming (node18.html#SECTION004200000000000000000)
4 More Control Flow Tools (node19.html#SECTION005000000000000000000)
- 4.1 If Statements (node20.html#SECTION005100000000000000000)
4.2 For Statements (node21.html#SECTION005200000000000000000)
4.3 The range() Function (node22.html#SECTION005300000000000000000)
4.4 Break and Continue Statements, and Else Clauses on Loops (node23.html#SECTION005400000000000000000)
4.5 Pass Statements (node24.html#SECTION005500000000000000000)
4.6 Defining Functions (node25.html#SECTION005600000000000000000)
4.7 More on Defining Functions (node26.html#SECTION005700000000000000000)
- Default Argument Values (node27.html#SECTION005710000000000000000)
Keyword Arguments (node28.html#SECTION005720000000000000000)
Arbitrary Argument Lists (node29.html#SECTION005730000000000000000)
5 Data Structures (node30.html#SECTION006000000000000000000)
- 5.1 More on Lists (node31.html#SECTION006100000000000000000)
- Functional Programming Tools (node32.html#SECTION006110000000000000000)
5.2 The del statement (node33.html#SECTION006200000000000000000)
5.3 Tuples and Sequences (node34.html#SECTION006300000000000000000)
5.4 Dictionaries (node35.html#SECTION006400000000000000000)
5.5 More on Conditions (node36.html#SECTION006500000000000000000)
5.6 Comparing Sequences and Other Types (node37.html#SECTION006600000000000000000)
6 Modules (node38.html#SECTION007000000000000000000)
- 6.1 More on Modules (node39.html#SECTION007100000000000000000)
- The Module Search Path (node40.html#SECTION007110000000000000000)
``Compiled'' Python files (node41.html#SECTION007120000000000000000)
6.2 Standard Modules (node42.html#SECTION007200000000000000000)
6.3 The dir() function (node43.html#SECTION007300000000000000000)
7 Input and Output (node44.html#SECTION008000000000000000000)
- 7.1 Fancier Output Formatting (node45.html#SECTION008100000000000000000)
7.2 Reading and Writing Files (node46.html#SECTION008200000000000000000)
- Methods of file objects (node47.html#SECTION008210000000000000000)
The pickle module (node48.html#SECTION008220000000000000000)
8 Errors and Exceptions (node49.html#SECTION009000000000000000000)
- 8.1 Syntax Errors (node50.html#SECTION009100000000000000000)
8.2 Exceptions (node51.html#SECTION009200000000000000000)
8.3 Handling Exceptions (node52.html#SECTION009300000000000000000)
8.4 Raising Exceptions (node53.html#SECTION009400000000000000000)
8.5 User-defined Exceptions (node54.html#SECTION009500000000000000000)
8.6 Defining Clean-up Actions (node55.html#SECTION009600000000000000000)
9 Classes (node56.html#SECTION0010000000000000000000)
- 9.1 A word about terminology (node57.html#SECTION0010100000000000000000)
9.2 Python scopes and name spaces (node58.html#SECTION0010200000000000000000)
9.3 A first look at classes (node59.html#SECTION0010300000000000000000)
- Class definition syntax (node60.html#SECTION0010310000000000000000)
Class objects (node61.html#SECTION0010320000000000000000)
Instance objects (node62.html#SECTION0010330000000000000000)
Method objects (node63.html#SECTION0010340000000000000000)
9.4 Random remarks (node64.html#SECTION0010400000000000000000)
9.5 Inheritance (node65.html#SECTION0010500000000000000000)
- Multiple inheritance (node66.html#SECTION0010510000000000000000)
9.6 Private variables through name mangling (node67.html#SECTION0010600000000000000000)
9.7 Odds and ends (node68.html#SECTION0010700000000000000000)
- Exceptions Can Be Classes (node69.html#SECTION0010710000000000000000)
10 What Now? (node70.html#SECTION0011000000000000000000)
11 Recent Additions as of Release 1.1 (node71.html#SECTION0012000000000000000000)
- 11.1 Lambda Forms (node72.html#SECTION0012100000000000000000)
11.2 Documentation Strings (node73.html#SECTION0012200000000000000000)
A Interactive Input Editing and History Substitution (node74.html#SECTION0013000000000000000000)
- A.1 Line Editing (node75.html#SECTION0013100000000000000000)
A.2 History Substitution (node76.html#SECTION0013200000000000000000)
A.3 Key Bindings (node77.html#SECTION0013300000000000000000)
A.4 Commentary (node78.html#SECTION0013400000000000000000)
About this document ... (node79.html#SECTION0014000000000000000000)
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