mirrors/cpython
1
0
Fork 0
mirror of https://github.com/python/cpython.git synced 2025-07-24 11:44:31 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 8

Move the 3k reST doc tree in place.

Browse source
This commit is contained in:
Georg Brandl 2007-08-15 14:28:22 +00:00
parent 739c01d47b
commit 116aa62bf5
423 changed files with 131199 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

89
Doc/tutorial/appetite.rst Normal file
View file

@ -0,0 +1,89 @@
.. _tut-intro:
**********************
Whetting Your Appetite
**********************
If you do much work on computers, eventually you find that there's some task
you'd like to automate. For example, you may wish to perform a
search-and-replace over a large number of text files, or rename and rearrange a
bunch of photo files in a complicated way. Perhaps you'd like to write a small
custom database, or a specialized GUI application, or a simple game.
If you're a professional software developer, you may have to work with several
C/C++/Java libraries but find the usual write/compile/test/re-compile cycle is
too slow. Perhaps you're writing a test suite for such a library and find
writing the testing code a tedious task. Or maybe you've written a program that
could use an extension language, and you don't want to design and implement a
whole new language for your application.
Python is just the language for you.
You could write a Unix shell script or Windows batch files for some of these
tasks, but shell scripts are best at moving around files and changing text data,
not well-suited for GUI applications or games. You could write a C/C++/Java
program, but it can take a lot of development time to get even a first-draft
program. Python is simpler to use, available on Windows, MacOS X, and Unix
operating systems, and will help you get the job done more quickly.
Python is simple to use, but it is a real programming language, offering much
more structure and support for large programs than shell scripts or batch files
can offer. On the other hand, Python also offers much more error checking than
C, and, being a *very-high-level language*, it has high-level data types built
in, such as flexible arrays and dictionaries. Because of its more general data
types Python is applicable to a much larger problem domain than Awk or even
Perl, yet many things are at least as easy in Python as in those languages.
Python allows you to split your program into modules that can be reused in other
Python programs. It comes with a large collection of standard modules that you
can use as the basis of your programs --- or as examples to start learning to
program in Python. Some of these modules provide things like file I/O, system
calls, sockets, and even interfaces to graphical user interface toolkits like
Tk.
Python is an interpreted language, which can save you considerable time during
program development because no compilation and linking is necessary. The
interpreter can be used interactively, which makes it easy to experiment with
features of the language, to write throw-away programs, or to test functions
during bottom-up program development. It is also a handy desk calculator.
Python enables programs to be written compactly and readably. Programs written
in Python are typically much shorter than equivalent C, C++, or Java programs,
for several reasons:
* the high-level data types allow you to express complex operations in a single
statement;
* statement grouping is done by indentation instead of beginning and ending
brackets;
* no variable or argument declarations are necessary.
Python is *extensible*: if you know how to program in C it is easy to add a new
built-in function or module to the interpreter, either to perform critical
operations at maximum speed, or to link Python programs to libraries that may
only be available in binary form (such as a vendor-specific graphics library).
Once you are really hooked, you can link the Python interpreter into an
application written in C and use it as an extension or command language for that
application.
By the way, the language is named after the BBC show "Monty Python's Flying
Circus" and has nothing to do with nasty reptiles. Making references to Monty
Python skits in documentation is not only allowed, it is encouraged!
Now that you are all excited about Python, you'll want to examine it in some
more detail. Since the best way to learn a language is to use it, the tutorial
invites you to play with the Python interpreter as you read.
.. % \section{Where From Here \label{where}}
In the next chapter, the mechanics of using the interpreter are explained. This
is rather mundane information, but essential for trying out the examples shown
later.
The rest of the tutorial introduces various features of the Python language and
system through examples, beginning with simple expressions, statements and data
types, through functions and modules, and finally touching upon advanced
concepts like exceptions and user-defined classes.

792
Doc/tutorial/classes.rst Normal file
View file

@ -0,0 +1,792 @@
.. _tut-classes:
*******
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, and a method can call the method of a base class with the same
name. Objects can contain an arbitrary amount of private data.
In C++ terminology, all class members (including the data members) are *public*,
and all member functions are *virtual*. There are no special constructors or
destructors. As in Modula-3, there are no shorthands for referencing the
object's members from its methods: the method function is declared with an
explicit first argument representing the object, which is provided implicitly by
the call. As in Smalltalk, classes themselves are objects, albeit in the wider
sense of the word: in Python, all data types are objects. This provides
semantics for importing and renaming. Unlike C++ and Modula-3, built-in types
can be used as base classes for extension by the user. Also, like in C++ but
unlike in Modula-3, most built-in operators with special syntax (arithmetic
operators, subscripting etc.) can be redefined for class instances.
.. _tut-terminology:
A Word About Terminology
========================
Lacking universally accepted terminology to talk about classes, I will make
occasional use of Smalltalk and C++ terms. (I would use Modula-3 terms, since
its object-oriented semantics are closer to those of Python than C++, but I
expect that few readers have heard of it.)
Objects have individuality, and multiple names (in multiple scopes) can be bound
to the same object. This is known as aliasing in other languages. This is
usually not appreciated on a first glance at Python, and can be safely ignored
when dealing with immutable basic types (numbers, strings, tuples). However,
aliasing has an (intended!) effect on the semantics of Python code involving
mutable objects such as lists, dictionaries, and most types representing
entities outside the program (files, windows, etc.). This is usually used to
the benefit of the program, since aliases behave like pointers in some respects.
For example, passing an object is cheap since only a pointer is passed by the
implementation; and if a function modifies an object passed as an argument, the
caller will see the change --- this eliminates the need for two different
argument passing mechanisms as in Pascal.
.. _tut-scopes:
Python Scopes and Name Spaces
=============================
Before introducing classes, I first have to tell you something about Python's
scope rules. Class definitions play some neat tricks with namespaces, and you
need to know how scopes and namespaces work to fully understand what's going on.
Incidentally, knowledge about this subject is useful for any advanced Python
programmer.
Let's begin with some definitions.
A *namespace* is a mapping from names to objects. Most namespaces are currently
implemented as Python dictionaries, but that's normally not noticeable in any
way (except for performance), and it may change in the future. Examples of
namespaces are: the set of built-in names (functions such as :func:`abs`, and
built-in exception names); the global names in a module; and the local names in
a function invocation. In a sense the set of attributes of an object also form
a namespace. The important thing to know about namespaces is that there is
absolutely no relation between names in different namespaces; for instance, two
different modules may both define a function "maximize" without confusion ---
users of the modules must prefix it with the module name.
By the way, I use the word *attribute* for any name following a dot --- for
example, in the expression ``z.real``, ``real`` is an attribute of the object
``z``. Strictly speaking, references to names in modules are attribute
references: in the expression ``modname.funcname``, ``modname`` is a module
object and ``funcname`` is an attribute of it. In this case there happens to be
a straightforward mapping between the module's attributes and the global names
defined in the module: they share the same namespace! [#]_
Attributes may be read-only or writable. In the latter case, assignment to
attributes is possible. Module attributes are writable: you can write
``modname.the_answer = 42``. Writable attributes may also be deleted with the
:keyword:`del` statement. For example, ``del modname.the_answer`` will remove
the attribute :attr:`the_answer` from the object named by ``modname``.
Name spaces are created at different moments and have different lifetimes. The
namespace containing the built-in names is created when the Python interpreter
starts up, and is never deleted. The global namespace for a module is created
when the module definition is read in; normally, module namespaces also last
until the interpreter quits. The statements executed by the top-level
invocation of the interpreter, either read from a script file or interactively,
are considered part of a module called :mod:`__main__`, so they have their own
global namespace. (The built-in names actually also live in a module; this is
called :mod:`__builtin__`.)
The local namespace for a function is created when the function is called, and
deleted when the function returns or raises an exception that is not handled
within the function. (Actually, forgetting would be a better way to describe
what actually happens.) Of course, recursive invocations each have their own
local namespace.
A *scope* is a textual region of a Python program where a namespace is directly
accessible. "Directly accessible" here means that an unqualified reference to a
name attempts to find the name in the namespace.
Although scopes are determined statically, they are used dynamically. At any
time during execution, there are at least three nested scopes whose namespaces
are directly accessible: the innermost scope, which is searched first, contains
the local names; the namespaces of any enclosing functions, which are searched
starting with the nearest enclosing scope; the middle scope, searched next,
contains the current module's global names; and the outermost scope (searched
last) is the namespace containing built-in names.
If a name is declared global, then all references and assignments go directly to
the middle scope containing the module's global names. Otherwise, all variables
found outside of the innermost scope are read-only (an attempt to write to such
a variable will simply create a *new* local variable in the innermost scope,
leaving the identically named outer variable unchanged).
Usually, the local scope references the local names of the (textually) current
function. Outside functions, the local scope references the same namespace as
the global scope: the module's namespace. Class definitions place yet another
namespace in the local scope.
It is important to realize that scopes are determined textually: the global
scope of a function defined in a module is that module's namespace, no matter
from where or by what alias the function is called. On the other hand, the
actual search for names is done dynamically, at run time --- however, the
language definition is evolving towards static name resolution, at "compile"
time, so don't rely on dynamic name resolution! (In fact, local variables are
already determined statically.)
A special quirk of Python is that assignments always go into the innermost
scope. Assignments do not copy data --- they just bind names to objects. The
same is true for deletions: the statement ``del x`` removes the binding of ``x``
from the namespace referenced by the local scope. In fact, all operations that
introduce new names use the local scope: in particular, import statements and
function definitions bind the module or function name in the local scope. (The
:keyword:`global` statement can be used to indicate that particular variables
live in the global scope.)
.. _tut-firstclasses:
A First Look at Classes
=======================
Classes introduce a little bit of new syntax, three new object types, and some
new semantics.
.. _tut-classdefinition:
Class Definition Syntax
-----------------------
The simplest form of class definition looks like this::
class ClassName:
<statement-1>
.
.
.
<statement-N>
Class definitions, like function definitions (:keyword:`def` statements) must be
executed before they have any effect. (You could conceivably place a class
definition in a branch of an :keyword:`if` statement, or inside a function.)
In practice, the statements inside a class definition will usually be function
definitions, but other statements are allowed, and sometimes useful --- we'll
come back to this later. The function definitions inside a class normally have
a peculiar form of argument list, dictated by the calling conventions for
methods --- again, this is explained later.
When a class definition is entered, a new namespace is created, and used as the
local scope --- thus, all assignments to local variables go into this new
namespace. In particular, function definitions bind the name of the new
function here.
When a class definition is left normally (via the end), a *class object* is
created. This is basically a wrapper around the contents of the namespace
created by the class definition; we'll learn more about class objects in the
next section. The original local scope (the one in effect just before the class
definition was entered) is reinstated, and the class object is bound here to the
class name given in the class definition header (:class:`ClassName` in the
example).
.. _tut-classobjects:
Class Objects
-------------
Class objects support two kinds of operations: attribute references and
instantiation.
*Attribute references* use the standard syntax used for all attribute references
in Python: ``obj.name``. Valid attribute names are all the names that were in
the class's namespace when the class object was created. So, if the class
definition looked like this::
class MyClass:
"A simple example class"
i = 12345
def f(self):
return 'hello world'
then ``MyClass.i`` and ``MyClass.f`` are valid attribute references, returning
an integer and a function object, respectively. Class attributes can also be
assigned to, so you can change the value of ``MyClass.i`` by assignment.
:attr:`__doc__` is also a valid attribute, returning the docstring belonging to
the class: ``"A simple example class"``.
Class *instantiation* uses function notation. Just pretend that the class
object is a parameterless function that returns a new instance of the class.
For example (assuming the above class)::
x = MyClass()
creates a new *instance* of the class and assigns this object to the local
variable ``x``.
The instantiation operation ("calling" a class object) creates an empty object.
Many classes like to create objects with instances customized to a specific
initial state. Therefore a class may define a special method named
:meth:`__init__`, like this::
def __init__(self):
self.data = []
When a class defines an :meth:`__init__` method, class instantiation
automatically invokes :meth:`__init__` for the newly-created class instance. So
in this example, a new, initialized instance can be obtained by::
x = MyClass()
Of course, the :meth:`__init__` method may have arguments for greater
flexibility. In that case, arguments given to the class instantiation operator
are passed on to :meth:`__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)
.. _tut-instanceobjects:
Instance Objects
----------------
Now what can we do with instance objects? The only operations understood by
instance objects are attribute references. There are two kinds of valid
attribute names, data attributes and methods.
*data attributes* correspond to "instance variables" in Smalltalk, and to "data
members" in C++. Data attributes need not be declared; like local variables,
they spring into existence when they are first assigned to. For example, if
``x`` is the instance of :class:`MyClass` created above, the following piece of
code will print the value ``16``, without leaving a trace::
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print x.counter
del x.counter
The other kind of instance attribute reference is a *method*. A method is a
function that "belongs to" an object. (In Python, the term method is not unique
to class instances: other object types can have methods as well. For example,
list objects have methods called append, insert, remove, sort, and so on.
However, in the following discussion, we'll use the term method exclusively to
mean methods of class instance objects, unless explicitly stated otherwise.)
.. index:: object: method
Valid method names of an instance object depend on its class. By definition,
all attributes of a class that are function objects define corresponding
methods of its instances. So in our example, ``x.f`` is a valid method
reference, since ``MyClass.f`` is a function, but ``x.i`` is not, since
``MyClass.i`` is not. But ``x.f`` is not the same thing as ``MyClass.f`` --- it
is a *method object*, not a function object.
.. _tut-methodobjects:
Method Objects
--------------
Usually, a method is called right after it is bound::
x.f()
In the :class:`MyClass` example, this will return the string ``'hello world'``.
However, it is not necessary to call a method right away: ``x.f`` is a method
object, and can be stored away and called at a later time. For example::
xf = x.f
while True:
print xf()
will continue to print ``hello world`` until the end of time.
What exactly happens when a method is called? You may have noticed that
``x.f()`` was called without an argument above, even though the function
definition for :meth:`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.
.. _tut-remarks:
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. Possible conventions include capitalizing method names, prefixing
data attribute names with a small unique string (perhaps just an underscore), or
using verbs for methods and nouns for data attributes.
Data attributes may be referenced by methods as well as by ordinary users
("clients") of an object. In other words, classes are not usable to implement
pure abstract data types. In fact, nothing in Python makes it possible to
enforce data hiding --- it is all based upon convention. (On the other hand,
the Python implementation, written in C, can completely hide implementation
details and control access to an object if necessary; this can be used by
extensions to Python written in C.)
Clients should use data attributes with care --- clients may mess up invariants
maintained by the methods by stamping on their data attributes. Note that
clients may add data attributes of their own to an instance object without
affecting the validity of the methods, as long as name conflicts are avoided ---
again, a naming convention can save a lot of headaches here.
There is no shorthand for referencing data attributes (or other methods!) from
within methods. I find that this actually increases the readability of methods:
there is no chance of confusing local variables and instance variables when
glancing through a method.
Often, the first argument of a method is called ``self``. This is nothing more
than a convention: the name ``self`` has absolutely no special meaning to
Python. (Note, however, that by not following the convention your code may be
less readable to other Python programmers, and it is also conceivable that a
*class browser* program might be written that relies upon such a convention.)
Any function object that is a class attribute defines a method for instances of
that class. It is not necessary that the function definition is textually
enclosed in the class definition: assigning a function object to a local
variable in the class is also ok. For example::
# Function defined outside the class
def f1(self, x, y):
return min(x, x+y)
class C:
f = f1
def g(self):
return 'hello world'
h = g
Now ``f``, ``g`` and ``h`` are all attributes of class :class:`C` that refer to
function objects, and consequently they are all methods of instances of
:class:`C` --- ``h`` being exactly equivalent to ``g``. Note that this practice
usually only serves to confuse the reader of a program.
Methods may call other methods by using method attributes of the ``self``
argument::
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
self.add(x)
self.add(x)
Methods may reference global names in the same way as ordinary functions. The
global scope associated with a method is the module containing the class
definition. (The class itself is never used as a global scope!) While one
rarely encounters a good reason for using global data in a method, there are
many legitimate uses of the global scope: for one thing, functions and modules
imported into the global scope can be used by methods, as well as functions and
classes defined in it. Usually, the class containing the method is itself
defined in this global scope, and in the next section we'll find some good
reasons why a method would want to reference its own class!
.. _tut-inheritance:
Inheritance
===========
Of course, a language feature would not be worthy of the name "class" without
supporting inheritance. The syntax for a derived class definition looks like
this::
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
The name :class:`BaseClassName` must be defined in a scope containing the
derived class definition. In place of a base class name, other arbitrary
expressions are also allowed. This can be useful, for example, when the base
class is defined in another module::
class DerivedClassName(modname.BaseClassName):
Execution of a derived class definition proceeds the same as for a base class.
When the class object is constructed, the base class is remembered. This is
used for resolving attribute references: if a requested attribute is not found
in the class, the search proceeds to look in the base class. This rule is
applied recursively if the base class itself is derived from some other class.
There's nothing special about instantiation of derived classes:
``DerivedClassName()`` creates a new instance of the class. Method references
are resolved as follows: the corresponding class attribute is searched,
descending down the chain of base classes if necessary, and the method reference
is valid if this yields a function object.
Derived classes may override methods of their base classes. Because methods
have no special privileges when calling other methods of the same object, a
method of a base class that calls another method defined in the same base class
may end up calling a method of a derived class that overrides it. (For C++
programmers: all methods in Python are effectively :keyword:`virtual`.)
An overriding method in a derived class may in fact want to extend rather than
simply replace the base class method of the same name. There is a simple way to
call the base class method directly: just call ``BaseClassName.methodname(self,
arguments)``. This is occasionally useful to clients as well. (Note that this
only works if the base class is defined or imported directly in the global
scope.)
.. _tut-multiple:
Multiple Inheritance
--------------------
Python supports a limited form of multiple inheritance as well. A class
definition with multiple base classes looks like this::
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
.
.
.
<statement-N>
For old-style classes, the only rule is depth-first, left-to-right. Thus, if an
attribute is not found in :class:`DerivedClassName`, it is searched in
:class:`Base1`, then (recursively) in the base classes of :class:`Base1`, and
only if it is not found there, it is searched in :class:`Base2`, and so on.
(To some people breadth first --- searching :class:`Base2` and :class:`Base3`
before the base classes of :class:`Base1` --- looks more natural. However, this
would require you to know whether a particular attribute of :class:`Base1` is
actually defined in :class:`Base1` or in one of its base classes before you can
figure out the consequences of a name conflict with an attribute of
:class:`Base2`. The depth-first rule makes no differences between direct and
inherited attributes of :class:`Base1`.)
For new-style classes, the method resolution order changes dynamically to
support cooperative calls to :func:`super`. This approach is known in some
other multiple-inheritance languages as call-next-method and is more powerful
than the super call found in single-inheritance languages.
With new-style classes, dynamic ordering is necessary because all cases of
multiple inheritance exhibit one or more diamond relationships (where one at
least one of the parent classes can be accessed through multiple paths from the
bottommost class). For example, all new-style classes inherit from
:class:`object`, so any case of multiple inheritance provides more than one path
to reach :class:`object`. To keep the base classes from being accessed more
than once, the dynamic algorithm linearizes the search order in a way that
preserves the left-to-right ordering specified in each class, that calls each
parent only once, and that is monotonic (meaning that a class can be subclassed
without affecting the precedence order of its parents). Taken together, these
properties make it possible to design reliable and extensible classes with
multiple inheritance. For more detail, see
http://www.python.org/download/releases/2.3/mro/.
.. _tut-private:
Private Variables
=================
There is limited support for class-private identifiers. Any identifier of the
form ``__spam`` (at least two leading underscores, at most one trailing
underscore) is textually replaced with ``_classname__spam``, where ``classname``
is the current class name with leading underscore(s) stripped. This mangling is
done without regard to the syntactic position of the identifier, so it can be
used to define class-private instance and class variables, methods, variables
stored in globals, and even variables stored in instances. private to this class
on instances of *other* classes. Truncation may occur when the mangled name
would be longer than 255 characters. Outside classes, or when the class name
consists of only underscores, no mangling occurs.
Name mangling is intended to give classes an easy way to define "private"
instance variables and methods, without having to worry about instance variables
defined by derived classes, or mucking with instance variables by code outside
the class. Note that the mangling rules are designed mostly to avoid accidents;
it still is possible for a determined soul to access or modify a variable that
is considered private. This can even be useful in special circumstances, such
as in the debugger, and that's one reason why this loophole is not closed.
(Buglet: derivation of a class with the same name as the base class makes use of
private variables of the base class possible.)
Notice that code passed to ``exec()`` or ``eval()`` 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.
.. _tut-odds:
Odds and Ends
=============
Sometimes it is useful to have a data type similar to the Pascal "record" or C
"struct", bundling together a few named data items. An empty class definition
will do nicely::
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
A piece of Python code that expects a particular abstract data type can often be
passed a class that emulates the methods of that data type instead. For
instance, if you have a function that formats some data from a file object, you
can define a class with methods :meth:`read` and :meth:`readline` that get 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
.. % \code{sys.stdin} will not cause the interpreter to read further input
.. % from it.)
Instance method objects have attributes, too: ``m.im_self`` is the instance
object with the method :meth:`m`, and ``m.im_func`` is the function object
corresponding to the method.
.. _tut-exceptionclasses:
Exceptions Are Classes Too
==========================
User-defined exceptions are identified by classes as well. Using this mechanism
it is possible to create extensible hierarchies of exceptions.
There are two new valid (semantic) forms for the raise statement::
raise Class, instance
raise instance
In the first form, ``instance`` must be an instance of :class:`Class` or of a
class derived from it. The second form is a shorthand for::
raise instance.__class__, instance
A class in an except clause is compatible with an exception if it is the same
class or a base class thereof (but not the other way around --- an except clause
listing a derived class is not compatible with a base class). For example, the
following code will print B, C, D in that order::
class B:
pass
class C(B):
pass
class D(C):
pass
for c in [B, C, D]:
try:
raise c()
except D:
print "D"
except C:
print "C"
except B:
print "B"
Note that if the except clauses were reversed (with ``except B`` first), it
would have printed B, B, B --- the first matching except clause is triggered.
When an error message is printed for an unhandled exception, the exception's
class name is printed, then a colon and a space, and finally the instance
converted to a string using the built-in function :func:`str`.
.. _tut-iterators:
Iterators
=========
By now you have probably noticed that most container objects can be looped over
using a :keyword:`for` statement::
for element in [1, 2, 3]:
print element
for element in (1, 2, 3):
print element
for key in {'one':1, 'two':2}:
print key
for char in "123":
print char
for line in open("myfile.txt"):
print line
This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the :keyword:`for` statement
calls :func:`iter` on the container object. The function returns an iterator
object that defines the method :meth:`__next__` which accesses elements in the
container one at a time. When there are no more elements, :meth:`__next__`
raises a :exc:`StopIteration` exception which tells the :keyword:`for` loop to
terminate. You can call the :meth:`__next__` method using the :func:`next`
builtin; this example shows how it all works::
>>> s = 'abc'
>>> it = iter(s)
>>> it
<iterator object at 0x00A1DB50>
>>> next(it)
'a'
>>> next(it)
'b'
>>> next(it)
'c'
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
next(it)
StopIteration
Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define a :meth:`__iter__` method which
returns an object with a :meth:`__next__` method. If the class defines
:meth:`__next__`, then :meth:`__iter__` can just return ``self``::
class Reverse:
"Iterator for looping over a sequence backwards"
def __init__(self, data):
self.data = data
self.index = len(data)
def __iter__(self):
return self
def __next__(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]
>>> for char in Reverse('spam'):
... print char
...
m
a
p
s
.. _tut-generators:
Generators
==========
Generators are a simple and powerful tool for creating iterators. They are
written like regular functions but use the :keyword:`yield` statement whenever
they want to return data. Each time :func:`next` is called on it, the generator
resumes where it left-off (it remembers all the data values and which statement
was last executed). An example shows that generators can be trivially easy to
create::
def reverse(data):
for index in range(len(data)-1, -1, -1):
yield data[index]
>>> for char in reverse('golf'):
... print char
...
f
l
o
g
Anything that can be done with generators can also be done with class based
iterators as described in the previous section. What makes generators so
compact is that the :meth:`__iter__` and :meth:`__next__` methods are created
automatically.
Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and
much more clear than an approach using instance variables like ``self.index``
and ``self.data``.
In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise :exc:`StopIteration`. In
combination, these features make it easy to create iterators with no more effort
than writing a regular function.
.. _tut-genexps:
Generator Expressions
=====================
Some simple generators can be coded succinctly as expressions using a syntax
similar to list comprehensions but with parentheses instead of brackets. These
expressions are designed for situations where the generator is used right away
by an enclosing function. Generator expressions are more compact but less
versatile than full generator definitions and tend to be more memory friendly
than equivalent list comprehensions.
Examples::
>>> sum(i*i for i in range(10)) # sum of squares
285
>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260
>>> from math import pi, sin
>>> sine_table = dict((x, sin(x*pi/180)) for x in range(0, 91))
>>> unique_words = set(word for line in page for word in line.split())
>>> valedictorian = max((student.gpa, student.name) for student in graduates)
>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1,-1,-1))
['f', 'l', 'o', 'g']
.. rubric:: Footnotes
.. [#] Except for one thing. Module objects have a secret read-only attribute called
:attr:`__dict__` which returns the dictionary used to implement the module's
namespace; the name :attr:`__dict__` is an attribute but not a global name.
Obviously, using this violates the abstraction of namespace implementation, and
should be restricted to things like post-mortem debuggers.

574
Doc/tutorial/controlflow.rst Normal file
View file

@ -0,0 +1,574 @@
.. _tut-morecontrol:
***********************
More Control Flow Tools
***********************
Besides the :keyword:`while` statement just introduced, Python knows the usual
control flow statements known from other languages, with some twists.
.. _tut-if:
:keyword:`if` Statements
========================
Perhaps the most well-known statement type is the :keyword:`if` statement. For
example::
>>> def raw_input(prompt):
... import sys
... sys.stdout.write(prompt)
... sys.stdout.flush()
... return sys.stdin.readline()
...
>>> x = int(raw_input("Please enter an integer: "))
>>> if x < 0:
... x = 0
... print 'Negative changed to zero'
... elif x == 0:
... print 'Zero'
... elif x == 1:
... print 'Single'
... else:
... print 'More'
...
There can be zero or more :keyword:`elif` parts, and the :keyword:`else` part is
optional. The keyword ':keyword:`elif`' is short for 'else if', and is useful
to avoid excessive indentation. An :keyword:`if` ... :keyword:`elif` ...
:keyword:`elif` ... sequence is a substitute for the :keyword:`switch` or
:keyword:`case` statements found in other languages.
.. % Weird spacings happen here if the wrapping of the source text
.. % gets changed in the wrong way.
.. _tut-for:
:keyword:`for` Statements
=========================
.. index::
statement: for
statement: for
The :keyword:`for` statement in Python differs a bit from what you may be used
to in C or Pascal. Rather than always iterating over an arithmetic progression
of numbers (like in Pascal), or giving the user the ability to define both the
iteration step and halting condition (as C), Python's :keyword:`for` statement
iterates over the items of any sequence (a list or a string), in the order that
they appear in the sequence. For example (no pun intended):
.. % One suggestion was to give a real C example here, but that may only
.. % serve to confuse non-C programmers.
::
>>> # Measure some strings:
... a = ['cat', 'window', 'defenestrate']
>>> for x in a:
... print x, len(x)
...
cat 3
window 6
defenestrate 12
It is not safe to modify the sequence being iterated over in the loop (this can
only happen for mutable sequence types, such as lists). If you need to modify
the list you are iterating over (for example, to duplicate selected items) you
must iterate over a copy. The slice notation makes this particularly
convenient::
>>> for x in a[:]: # make a slice copy of the entire list
... if len(x) > 6: a.insert(0, x)
...
>>> a
['defenestrate', 'cat', 'window', 'defenestrate']
.. _tut-range:
The :func:`range` Function
==========================
If you do need to iterate over a sequence of numbers, the built-in function
:func:`range` comes in handy. It generates lists containing arithmetic
progressions::
>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
The given end point is never part of the generated list; ``range(10)`` generates
a list of 10 values, the legal indices for items of a sequence of length 10. It
is possible to let the range start at another number, or to specify a different
increment (even negative; sometimes this is called the 'step')::
>>> range(5, 10)
[5, 6, 7, 8, 9]
>>> range(0, 10, 3)
[0, 3, 6, 9]
>>> range(-10, -100, -30)
[-10, -40, -70]
To iterate over the indices of a sequence, combine :func:`range` and :func:`len`
as follows::
>>> a = ['Mary', 'had', 'a', 'little', 'lamb']
>>> for i in range(len(a)):
... print i, a[i]
...
0 Mary
1 had
2 a
3 little
4 lamb
.. _tut-break:
:keyword:`break` and :keyword:`continue` Statements, and :keyword:`else` Clauses on Loops
=========================================================================================
The :keyword:`break` statement, like in C, breaks out of the smallest enclosing
:keyword:`for` or :keyword:`while` loop.
The :keyword:`continue` statement, also borrowed from C, continues with the next
iteration of the loop.
Loop statements may have an ``else`` clause; it is executed when the loop
terminates through exhaustion of the list (with :keyword:`for`) or when the
condition becomes false (with :keyword:`while`), but not when the loop is
terminated by a :keyword:`break` statement. This is exemplified by the
following loop, which searches for prime numbers::
>>> for n in range(2, 10):
... for x in range(2, n):
... if n % x == 0:
... print n, 'equals', x, '*', n/x
... break
... else:
... # loop fell through without finding a factor
... print n, 'is a prime number'
...
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3
.. _tut-pass:
:keyword:`pass` Statements
==========================
The :keyword:`pass` statement does nothing. It can be used when a statement is
required syntactically but the program requires no action. For example::
>>> while True:
... pass # Busy-wait for keyboard interrupt
...
.. _tut-functions:
Defining Functions
==================
We can create a function that writes the Fibonacci series to an arbitrary
boundary::
>>> def fib(n): # write Fibonacci series up to n
... """Print a Fibonacci series up to n."""
... a, b = 0, 1
... while b < n:
... print b,
... a, b = b, a+b
...
>>> # Now call the function we just defined:
... fib(2000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597
.. index::
single: documentation strings
single: docstrings
single: strings, documentation
The keyword :keyword:`def` introduces a function *definition*. It must be
followed by the function name and the parenthesized list of formal parameters.
The statements that form the body of the function start at the next line, and
must be indented. The first statement of the function body can optionally be a
string literal; this string literal is the function's documentation string, or
:dfn:`docstring`.
There are tools which use docstrings to automatically produce online or printed
documentation, or to let the user interactively browse through code; it's good
practice to include docstrings in code that you write, so try to make a habit of
it.
The *execution* of a function introduces a new symbol table used for the local
variables of the function. More precisely, all variable assignments in a
function store the value in the local symbol table; whereas variable references
first look in the local symbol table, then in the global symbol table, and then
in the table of built-in names. Thus, global variables cannot be directly
assigned a value within a function (unless named in a :keyword:`global`
statement), although they may be referenced.
The actual parameters (arguments) to a function call are introduced in the local
symbol table of the called function when it is called; thus, arguments are
passed using *call by value* (where the *value* is always an object *reference*,
not the value of the object). [#]_ When a function calls another function, a new
local symbol table is created for that call.
A function definition introduces the function name in the current symbol table.
The value of the function name has a type that is recognized by the interpreter
as a user-defined function. This value can be assigned to another name which
can then also be used as a function. This serves as a general renaming
mechanism::
>>> fib
<function fib at 10042ed0>
>>> f = fib
>>> f(100)
1 1 2 3 5 8 13 21 34 55 89
You might object that ``fib`` is not a function but a procedure. In Python,
like in C, procedures are just functions that don't return a value. In fact,
technically speaking, procedures do return a value, albeit a rather boring one.
This value is called ``None`` (it's a built-in name). Writing the value
``None`` is normally suppressed by the interpreter if it would be the only value
written. You can see it if you really want to::
>>> print fib(0)
None
It is simple to write a function that returns a list of the numbers of the
Fibonacci series, instead of printing it::
>>> def fib2(n): # return Fibonacci series up to n
... """Return a list containing the Fibonacci series up to n."""
... result = []
... a, b = 0, 1
... while b < n:
... result.append(b) # see below
... a, b = b, a+b
... return result
...
>>> f100 = fib2(100) # call it
>>> f100 # write the result
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
This example, as usual, demonstrates some new Python features:
* The :keyword:`return` statement returns with a value from a function.
:keyword:`return` without an expression argument returns ``None``. Falling off
the end of a procedure also returns ``None``.
* The statement ``result.append(b)`` calls a *method* of the list object
``result``. A method is a function that 'belongs' to an object and is named
``obj.methodname``, where ``obj`` is some object (this may be an expression),
and ``methodname`` is the name of a method that is defined by the object's type.
Different types define different methods. Methods of different types may have
the same name without causing ambiguity. (It is possible to define your own
object types and methods, using *classes*, as discussed later in this tutorial.)
The method :meth:`append` shown in the example is defined for list objects; it
adds a new element at the end of the list. In this example it is equivalent to
``result = result + [b]``, but more efficient.
.. _tut-defining:
More on Defining Functions
==========================
It is also possible to define functions with a variable number of arguments.
There are three forms, which can be combined.
.. _tut-defaultargs:
Default Argument Values
-----------------------
The most useful form is to specify a default value for one or more arguments.
This creates a function that can be called with fewer arguments than it is
defined to allow. For example::
def raw_input(prompt):
import sys
sys.stdout.write(prompt)
sys.stdout.flush()
return sys.stdin.readline()
def ask_ok(prompt, retries=4, complaint='Yes or no, please!'):
while True:
ok = raw_input(prompt)
if ok in ('y', 'ye', 'yes'): return True
if ok in ('n', 'no', 'nop', 'nope'): return False
retries = retries - 1
if retries < 0: raise IOError, 'refusenik user'
print complaint
This function can be called either like this: ``ask_ok('Do you really want to
quit?')`` or like this: ``ask_ok('OK to overwrite the file?', 2)``.
This example also introduces the :keyword:`in` keyword. This tests whether or
not a sequence contains a certain value.
The default values are evaluated at the point of function definition in the
*defining* scope, so that ::
i = 5
def f(arg=i):
print arg
i = 6
f()
will print ``5``.
**Important warning:** The default value is evaluated only once. This makes a
difference when the default is a mutable object such as a list, dictionary, or
instances of most classes. For example, the following function accumulates the
arguments passed to it on subsequent calls::
def f(a, L=[]):
L.append(a)
return L
print f(1)
print f(2)
print f(3)
This will print ::
[1]
[1, 2]
[1, 2, 3]
If you don't want the default to be shared between subsequent calls, you can
write the function like this instead::
def f(a, L=None):
if L is None:
L = []
L.append(a)
return L
.. _tut-keywordargs:
Keyword Arguments
-----------------
Functions can also be called using keyword arguments of the form ``keyword =
value``. For instance, the following function::
def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
print "-- This parrot wouldn't", action,
print "if you put", voltage, "volts through it."
print "-- Lovely plumage, the", type
print "-- It's", state, "!"
could be called in any of the following ways::
parrot(1000)
parrot(action = 'VOOOOOM', voltage = 1000000)
parrot('a thousand', state = 'pushing up the daisies')
parrot('a million', 'bereft of life', 'jump')
but the following calls would all be invalid::
parrot() # required argument missing
parrot(voltage=5.0, 'dead') # non-keyword argument following keyword
parrot(110, voltage=220) # duplicate value for argument
parrot(actor='John Cleese') # unknown keyword
In general, an argument list must have any positional arguments followed by any
keyword arguments, where the keywords must be chosen from the formal parameter
names. It's not important whether a formal parameter has a default value or
not. No argument may receive a value more than once --- formal parameter names
corresponding to positional arguments cannot be used as keywords in the same
calls. Here's an example that fails due to this restriction::
>>> def function(a):
... pass
...
>>> function(0, a=0)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: function() got multiple values for keyword argument 'a'
When a final formal parameter of the form ``**name`` is present, it receives a
dictionary (see :ref:`typesmapping`) containing all keyword arguments except for
those corresponding to a formal parameter. This may be combined with a formal
parameter of the form ``*name`` (described in the next subsection) which
receives a tuple containing the positional arguments beyond the formal parameter
list. (``*name`` must occur before ``**name``.) For example, if we define a
function like this::
def cheeseshop(kind, *arguments, **keywords):
print "-- Do you have any", kind, '?'
print "-- I'm sorry, we're all out of", kind
for arg in arguments: print arg
print '-'*40
keys = keywords.keys()
keys.sort()
for kw in keys: print kw, ':', keywords[kw]
It could be called like this::
cheeseshop('Limburger', "It's very runny, sir.",
"It's really very, VERY runny, sir.",
client='John Cleese',
shopkeeper='Michael Palin',
sketch='Cheese Shop Sketch')
and of course it would print::
-- Do you have any Limburger ?
-- I'm sorry, we're all out of Limburger
It's very runny, sir.
It's really very, VERY runny, sir.
----------------------------------------
client : John Cleese
shopkeeper : Michael Palin
sketch : Cheese Shop Sketch
Note that the :meth:`sort` method of the list of keyword argument names is
called before printing the contents of the ``keywords`` dictionary; if this is
not done, the order in which the arguments are printed is undefined.
.. _tut-arbitraryargs:
Arbitrary Argument Lists
------------------------
Finally, the least frequently used option is to specify that a function can be
called with an arbitrary number of arguments. These arguments will be wrapped
up in a tuple. Before the variable number of arguments, zero or more normal
arguments may occur. ::
def fprintf(file, format, *args):
file.write(format % args)
.. _tut-unpacking-arguments:
Unpacking Argument Lists
------------------------
The reverse situation occurs when the arguments are already in a list or tuple
but need to be unpacked for a function call requiring separate positional
arguments. For instance, the built-in :func:`range` function expects separate
*start* and *stop* arguments. If they are not available separately, write the
function call with the ``*``\ -operator to unpack the arguments out of a list
or tuple::
>>> range(3, 6) # normal call with separate arguments
[3, 4, 5]
>>> args = [3, 6]
>>> range(*args) # call with arguments unpacked from a list
[3, 4, 5]
In the same fashion, dictionaries can deliver keyword arguments with the ``**``\
-operator::
>>> def parrot(voltage, state='a stiff', action='voom'):
... print "-- This parrot wouldn't", action,
... print "if you put", voltage, "volts through it.",
... print "E's", state, "!"
...
>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
>>> parrot(**d)
-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !
.. _tut-lambda:
Lambda Forms
------------
By popular demand, a few features commonly found in functional programming
languages like Lisp have been added to Python. With the :keyword:`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 can
reference variables from the containing scope::
>>> def make_incrementor(n):
... return lambda x: x + n
...
>>> f = make_incrementor(42)
>>> f(0)
42
>>> f(1)
43
.. _tut-docstrings:
Documentation Strings
---------------------
.. index::
single: docstrings
single: documentation strings
single: strings, documentation
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 or more paragraphs describing the object's calling
conventions, its side effects, etc.
The Python parser does not strip indentation from multi-line string literals in
Python, so tools that process documentation have to strip indentation if
desired. 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).
Here is an example of a multi-line docstring::
>>> def my_function():
... """Do nothing, but document it.
...
... No, really, it doesn't do anything.
... """
... pass
...
>>> print my_function.__doc__
Do nothing, but document it.
No, really, it doesn't do anything.
.. rubric:: Footnotes
.. [#] Actually, *call by object reference* would be a better description, since if a
mutable object is passed, the caller will see any changes the callee makes to it
(items inserted into a list).

586
Doc/tutorial/datastructures.rst Normal file
View file

@ -0,0 +1,586 @@
.. _tut-structures:
***************
Data Structures
***************
This chapter describes some things you've learned about already in more detail,
and adds some new things as well.
.. _tut-morelists:
More on Lists
=============
The list data type has some more methods. Here are all of the methods of list
objects:
.. method:: list.append(x)
Add an item to the end of the list; equivalent to ``a[len(a):] = [x]``.
.. method:: list.extend(L)
Extend the list by appending all the items in the given list; equivalent to
``a[len(a):] = L``.
.. method:: list.insert(i, x)
Insert an item at a given position. The first argument is the index of the
element before which to insert, so ``a.insert(0, x)`` inserts at the front of
the list, and ``a.insert(len(a), x)`` is equivalent to ``a.append(x)``.
.. method:: list.remove(x)
Remove the first item from the list whose value is *x*. It is an error if there
is no such item.
.. method:: list.pop([i])
Remove the item at the given position in the list, and return it. If no index
is specified, ``a.pop()`` removes and returns the last item in the list. (The
square brackets around the *i* in the method signature denote that the parameter
is optional, not that you should type square brackets at that position. You
will see this notation frequently in the Python Library Reference.)
.. method:: list.index(x)
Return the index in the list of the first item whose value is *x*. It is an
error if there is no such item.
.. method:: list.count(x)
Return the number of times *x* appears in the list.
.. method:: list.sort()
Sort the items of the list, in place.
.. method:: list.reverse()
Reverse the elements of the list, in place.
An example that uses most of the list methods::
>>> a = [66.25, 333, 333, 1, 1234.5]
>>> print a.count(333), a.count(66.25), a.count('x')
2 1 0
>>> a.insert(2, -1)
>>> a.append(333)
>>> a
[66.25, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
1
>>> a.remove(333)
>>> a
[66.25, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.25]
>>> a.sort()
>>> a
[-1, 1, 66.25, 333, 333, 1234.5]
.. _tut-lists-as-stacks:
Using Lists as Stacks
---------------------
.. sectionauthor:: Ka-Ping Yee <ping@lfw.org>
The list methods make it very easy to use a list as a stack, where the last
element added is the first element retrieved ("last-in, first-out"). To add an
item to the top of the stack, use :meth:`append`. To retrieve an item from the
top of the stack, use :meth:`pop` without an explicit index. For example::
>>> stack = [3, 4, 5]
>>> stack.append(6)
>>> stack.append(7)
>>> stack
[3, 4, 5, 6, 7]
>>> stack.pop()
7
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
6
>>> stack.pop()
5
>>> stack
[3, 4]
.. _tut-lists-as-queues:
Using Lists as Queues
---------------------
.. sectionauthor:: Ka-Ping Yee <ping@lfw.org>
You can also use a list conveniently as a queue, where the first element added
is the first element retrieved ("first-in, first-out"). To add an item to the
back of the queue, use :meth:`append`. To retrieve an item from the front of
the queue, use :meth:`pop` with ``0`` as the index. For example::
>>> queue = ["Eric", "John", "Michael"]
>>> queue.append("Terry") # Terry arrives
>>> queue.append("Graham") # Graham arrives
>>> queue.pop(0)
'Eric'
>>> queue.pop(0)
'John'
>>> queue
['Michael', 'Terry', 'Graham']
.. _tut-functional:
Functional Programming Tools
----------------------------
There are two built-in functions that are very useful when used with lists:
:func:`filter` and :func:`map`.
``filter(function, sequence)`` returns a sequence consisting of those items from
the sequence for which ``function(item)`` is true. If *sequence* is a
:class:`string` or :class:`tuple`, the result will be of the same type;
otherwise, it is always a :class:`list`. For example, to compute some primes::
>>> def f(x): return x % 2 != 0 and x % 3 != 0
...
>>> filter(f, range(2, 25))
[5, 7, 11, 13, 17, 19, 23]
``map(function, sequence)`` calls ``function(item)`` for each of the sequence's
items and returns a list of the return values. For example, to compute some
cubes::
>>> def cube(x): return x*x*x
...
>>> map(cube, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
More than one sequence may be passed; the function must then have as many
arguments as there are sequences and is called with the corresponding item from
each sequence (or ``None`` if some sequence is shorter than another). For
example::
>>> seq = range(8)
>>> def add(x, y): return x+y
...
>>> map(add, seq, seq)
[0, 2, 4, 6, 8, 10, 12, 14]
.. versionadded:: 2.3
List Comprehensions
-------------------
List comprehensions provide a concise way to create lists without resorting to
use of :func:`map`, :func:`filter` and/or :keyword:`lambda`. The resulting list
definition tends often to be clearer than lists built using those constructs.
Each list comprehension consists of an expression followed by a :keyword:`for`
clause, then zero or more :keyword:`for` or :keyword:`if` clauses. The result
will be a list resulting from evaluating the expression in the context of the
:keyword:`for` and :keyword:`if` clauses which follow it. If the expression
would evaluate to a tuple, it must be parenthesized. ::
>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> vec = [2, 4, 6]
>>> [3*x for x in vec]
[6, 12, 18]
>>> [3*x for x in vec if x > 3]
[12, 18]
>>> [3*x for x in vec if x < 2]
[]
>>> [[x,x**2] for x in vec]
[[2, 4], [4, 16], [6, 36]]
>>> [x, x**2 for x in vec] # error - parens required for tuples
File "<stdin>", line 1, in ?
[x, x**2 for x in vec]
^
SyntaxError: invalid syntax
>>> [(x, x**2) for x in vec]
[(2, 4), (4, 16), (6, 36)]
>>> vec1 = [2, 4, 6]
>>> vec2 = [4, 3, -9]
>>> [x*y for x in vec1 for y in vec2]
[8, 6, -18, 16, 12, -36, 24, 18, -54]
>>> [x+y for x in vec1 for y in vec2]
[6, 5, -7, 8, 7, -5, 10, 9, -3]
>>> [vec1[i]*vec2[i] for i in range(len(vec1))]
[8, 12, -54]
List comprehensions are much more flexible than :func:`map` and can be applied
to complex expressions and nested functions::
>>> [str(round(355/113.0, i)) for i in range(1,6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']
.. _tut-del:
The :keyword:`del` statement
============================
There is a way to remove an item from a list given its index instead of its
value: the :keyword:`del` statement. This differs from the :meth:`pop` method
which returns a value. The :keyword:`del` statement can also be used to remove
slices from a list or clear the entire list (which we did earlier by assignment
of an empty list to the slice). For example::
>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.25, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.25, 1234.5]
>>> del a[:]
>>> a
[]
:keyword:`del` can also be used to delete entire variables::
>>> del a
Referencing the name ``a`` hereafter is an error (at least until another value
is assigned to it). We'll find other uses for :keyword:`del` later.
.. _tut-tuples:
Tuples and Sequences
====================
We saw that lists and strings have many common properties, such as indexing and
slicing operations. They are two examples of *sequence* data types (see
:ref:`typesseq`). Since Python is an evolving language, other sequence data
types may be added. There is also another standard sequence data type: the
*tuple*.
A tuple consists of a number of values separated by commas, for instance::
>>> t = 12345, 54321, 'hello!'
>>> t[0]
12345
>>> t
(12345, 54321, 'hello!')
>>> # Tuples may be nested:
... u = t, (1, 2, 3, 4, 5)
>>> u
((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
As you see, on output tuples are always enclosed in parentheses, so that nested
tuples are interpreted correctly; they may be input with or without surrounding
parentheses, although often parentheses are necessary anyway (if the tuple is
part of a larger expression).
Tuples have many uses. For example: (x, y) coordinate pairs, employee records
from a database, etc. Tuples, like strings, are immutable: it is not possible
to assign to the individual items of a tuple (you can simulate much of the same
effect with slicing and concatenation, though). It is also possible to create
tuples which contain mutable objects, such as lists.
A special problem is the construction of tuples containing 0 or 1 items: the
syntax has some extra quirks to accommodate these. Empty tuples are constructed
by an empty pair of parentheses; a tuple with one item is constructed by
following a value with a comma (it is not sufficient to enclose a single value
in parentheses). Ugly, but effective. For example::
>>> empty = ()
>>> singleton = 'hello', # <-- note trailing comma
>>> len(empty)
0
>>> len(singleton)
1
>>> singleton
('hello',)
The statement ``t = 12345, 54321, 'hello!'`` is an example of *tuple packing*:
the values ``12345``, ``54321`` and ``'hello!'`` are packed together in a tuple.
The reverse operation is also possible::
>>> x, y, z = t
This is called, appropriately enough, *sequence unpacking*. Sequence unpacking
requires the list of variables on the left to have the same number of elements
as the length of the sequence. Note that multiple assignment is really just a
combination of tuple packing and sequence unpacking!
There is a small bit of asymmetry here: packing multiple values always creates
a tuple, and unpacking works for any sequence.
.. % XXX Add a bit on the difference between tuples and lists.
.. _tut-sets:
Sets
====
Python also includes a data type for *sets*. A set is an unordered collection
with no duplicate elements. Basic uses include membership testing and
eliminating duplicate entries. Set objects also support mathematical operations
like union, intersection, difference, and symmetric difference.
Here is a brief demonstration::
>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> fruit = set(basket) # create a set without duplicates
>>> fruit
set(['orange', 'pear', 'apple', 'banana'])
>>> 'orange' in fruit # fast membership testing
True
>>> 'crabgrass' in fruit
False
>>> # Demonstrate set operations on unique letters from two words
...
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a # unique letters in a
set(['a', 'r', 'b', 'c', 'd'])
>>> a - b # letters in a but not in b
set(['r', 'd', 'b'])
>>> a | b # letters in either a or b
set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])
>>> a & b # letters in both a and b
set(['a', 'c'])
>>> a ^ b # letters in a or b but not both
set(['r', 'd', 'b', 'm', 'z', 'l'])
.. _tut-dictionaries:
Dictionaries
============
Another useful data type built into Python is the *dictionary* (see
:ref:`typesmapping`). Dictionaries are sometimes found in other languages as
"associative memories" or "associative arrays". Unlike sequences, which are
indexed by a range of numbers, dictionaries are indexed by *keys*, which can be
any immutable type; strings and numbers can always be keys. Tuples can be used
as keys if they contain only strings, numbers, or tuples; if a tuple contains
any mutable object either directly or indirectly, it cannot be used as a key.
You can't use lists as keys, since lists can be modified in place using index
assignments, slice assignments, or methods like :meth:`append` and
:meth:`extend`.
It is best to think of a dictionary as an unordered set of *key: value* pairs,
with the requirement that the keys are unique (within one dictionary). A pair of
braces creates an empty dictionary: ``{}``. Placing a comma-separated list of
key:value pairs within the braces adds initial key:value pairs to the
dictionary; this is also the way dictionaries are written on output.
The main operations on a dictionary are storing a value with some key and
extracting the value given the key. It is also possible to delete a key:value
pair with ``del``. If you store using a key that is already in use, the old
value associated with that key is forgotten. It is an error to extract a value
using a non-existent key.
The :meth:`keys` method of a dictionary object returns a list of all the keys
used in the dictionary, in arbitrary order (if you want it sorted, just apply
the :meth:`sort` method to the list of keys). To check whether a single key is
in the dictionary, either use the dictionary's :meth:`has_key` method or the
:keyword:`in` keyword.
Here is a small example using a dictionary::
>>> tel = {'jack': 4098, 'sape': 4139}
>>> tel['guido'] = 4127
>>> tel
{'sape': 4139, 'guido': 4127, 'jack': 4098}
>>> tel['jack']
4098
>>> del tel['sape']
>>> tel['irv'] = 4127
>>> tel
{'guido': 4127, 'irv': 4127, 'jack': 4098}
>>> tel.keys()
['guido', 'irv', 'jack']
>>> tel.has_key('guido')
True
>>> 'guido' in tel
True
The :func:`dict` constructor builds dictionaries directly from lists of
key-value pairs stored as tuples. When the pairs form a pattern, list
comprehensions can compactly specify the key-value list. ::
>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}
>>> dict([(x, x**2) for x in (2, 4, 6)]) # use a list comprehension
{2: 4, 4: 16, 6: 36}
Later in the tutorial, we will learn about Generator Expressions which are even
better suited for the task of supplying key-values pairs to the :func:`dict`
constructor.
When the keys are simple strings, it is sometimes easier to specify pairs using
keyword arguments::
>>> dict(sape=4139, guido=4127, jack=4098)
{'sape': 4139, 'jack': 4098, 'guido': 4127}
.. _tut-loopidioms:
Looping Techniques
==================
When looping through dictionaries, the key and corresponding value can be
retrieved at the same time using the :meth:`iteritems` method. ::
>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
>>> for k, v in knights.iteritems():
... print k, v
...
gallahad the pure
robin the brave
When looping through a sequence, the position index and corresponding value can
be retrieved at the same time using the :func:`enumerate` function. ::
>>> for i, v in enumerate(['tic', 'tac', 'toe']):
... print i, v
...
0 tic
1 tac
2 toe
To loop over two or more sequences at the same time, the entries can be paired
with the :func:`zip` function. ::
>>> questions = ['name', 'quest', 'favorite color']
>>> answers = ['lancelot', 'the holy grail', 'blue']
>>> for q, a in zip(questions, answers):
... print 'What is your %s? It is %s.' % (q, a)
...
What is your name? It is lancelot.
What is your quest? It is the holy grail.
What is your favorite color? It is blue.
To loop over a sequence in reverse, first specify the sequence in a forward
direction and then call the :func:`reversed` function. ::
>>> for i in reversed(range(1,10,2)):
... print i
...
9
7
5
3
1
To loop over a sequence in sorted order, use the :func:`sorted` function which
returns a new sorted list while leaving the source unaltered. ::
>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for f in sorted(set(basket)):
... print f
...
apple
banana
orange
pear
.. _tut-conditions:
More on Conditions
==================
The conditions used in ``while`` and ``if`` statements can contain any
operators, not just comparisons.
The comparison operators ``in`` and ``not in`` check whether a value occurs
(does not occur) in a sequence. The operators ``is`` and ``is not`` compare
whether two objects are really the same object; this only matters for mutable
objects like lists. All comparison operators have the same priority, which is
lower than that of all numerical operators.
Comparisons can be chained. For example, ``a < b == c`` tests whether ``a`` is
less than ``b`` and moreover ``b`` equals ``c``.
Comparisons may be combined using the Boolean operators ``and`` and ``or``, and
the outcome of a comparison (or of any other Boolean expression) may be negated
with ``not``. These have lower priorities than comparison operators; between
them, ``not`` has the highest priority and ``or`` the lowest, so that ``A and
not B or C`` is equivalent to ``(A and (not B)) or C``. As always, parentheses
can be used to express the desired composition.
The Boolean operators ``and`` and ``or`` are so-called *short-circuit*
operators: their arguments are evaluated from left to right, and evaluation
stops as soon as the outcome is determined. For example, if ``A`` and ``C`` are
true but ``B`` is false, ``A and B and C`` does not evaluate the expression
``C``. When used as a general value and not as a Boolean, the return value of a
short-circuit operator is the last evaluated argument.
It is possible to assign the result of a comparison or other Boolean expression
to a variable. For example, ::
>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
>>> non_null = string1 or string2 or string3
>>> non_null
'Trondheim'
Note that in Python, unlike C, assignment cannot occur inside expressions. C
programmers may grumble about this, but it avoids a common class of problems
encountered in C programs: typing ``=`` in an expression when ``==`` was
intended.
.. _tut-comparing:
Comparing Sequences and Other Types
===================================
Sequence objects may be compared to other objects with the same sequence type.
The comparison uses *lexicographical* ordering: first the first two items are
compared, and if they differ this determines the outcome of the comparison; if
they are equal, the next two items are compared, and so on, until either
sequence is exhausted. If two items to be compared are themselves sequences of
the same type, the lexicographical comparison is carried out recursively. If
all items of two sequences compare equal, the sequences are considered equal.
If one sequence is an initial sub-sequence of the other, the shorter sequence is
the smaller (lesser) one. Lexicographical ordering for strings uses the ASCII
ordering for individual characters. Some examples of comparisons between
sequences of the same type::
(1, 2, 3) < (1, 2, 4)
[1, 2, 3] < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4) < (1, 2, 4)
(1, 2) < (1, 2, -1)
(1, 2, 3) == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4)
Note that comparing objects of different types is legal. The outcome is
deterministic but arbitrary: the types are ordered by their name. Thus, a list
is always smaller than a string, a string is always smaller than a tuple, etc.
[#]_ Mixed numeric types are compared according to their numeric value, so 0
equals 0.0, etc.
.. rubric:: Footnotes
.. [#] The rules for comparing objects of different types should not be relied upon;
they may change in a future version of the language.

418
Doc/tutorial/errors.rst Normal file
View file

@ -0,0 +1,418 @@
.. _tut-errors:
*********************
Errors and Exceptions
*********************
Until now error messages haven't been more than mentioned, but if you have tried
out the examples you have probably seen some. There are (at least) two
distinguishable kinds of errors: *syntax errors* and *exceptions*.
.. _tut-syntaxerrors:
Syntax Errors
=============
Syntax errors, also known as parsing errors, are perhaps the most common kind of
complaint you get while you are still learning Python::
>>> while True print 'Hello world'
File "<stdin>", line 1, in ?
while True print 'Hello world'
^
SyntaxError: invalid syntax
The parser repeats the offending line and displays a little 'arrow' pointing at
the earliest point in the line where the error was detected. The error is
caused by (or at least detected at) the token *preceding* the arrow: in the
example, the error is detected at the keyword :keyword:`print`, since a colon
(``':'``) is missing before it. File name and line number are printed so you
know where to look in case the input came from a script.
.. _tut-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 (most recent call last):
File "<stdin>", line 1, in ?
ZeroDivisionError: integer division or modulo by zero
>>> 4 + spam*3
Traceback (most recent call last):
File "<stdin>", line 1, in ?
NameError: name 'spam' is not defined
>>> '2' + 2
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: cannot concatenate 'str' and 'int' objects
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 :exc:`ZeroDivisionError`, :exc:`NameError` and :exc:`TypeError`.
The string printed as the exception type is the name of the built-in 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 provides detail based on the type of exception and what
caused it.
The preceding part of the error message shows the context where the exception
happened, in the form of a stack traceback. In general it contains a stack
traceback listing source lines; however, it will not display lines read from
standard input.
:ref:`bltin-exceptions` lists the built-in exceptions and their meanings.
.. _tut-handling:
Handling Exceptions
===================
It is possible to write programs that handle selected exceptions. Look at the
following example, which asks the user for input until a valid integer has been
entered, but allows the user to interrupt the program (using :kbd:`Control-C` or
whatever the operating system supports); note that a user-generated interruption
is signalled by raising the :exc:`KeyboardInterrupt` exception. ::
>>> def raw_input(prompt):
... import sys
... sys.stdout.write(prompt)
... sys.stdout.flush()
... return sys.stdin.readline()
...
>>> while True:
... try:
... x = int(raw_input("Please enter a number: "))
... break
... except ValueError:
... print "Oops! That was no valid number. Try again..."
...
The :keyword:`try` statement works as follows.
* First, the *try clause* (the statement(s) between the :keyword:`try` and
:keyword:`except` keywords) is executed.
* If no exception occurs, the *except clause* is skipped and execution of the
:keyword:`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
:keyword:`except` keyword, the except clause is executed, and then execution
continues after the :keyword:`try` statement.
* If an exception occurs which does not match the exception named in the except
clause, it is passed on to outer :keyword:`try` statements; if no handler is
found, it is an *unhandled exception* and execution stops with a message as
shown above.
A :keyword:`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 :keyword:`try` statement. An except clause may
name multiple exceptions as a parenthesized tuple, for example::
... 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! It can also be used to print an error message and then re-raise
the exception (allowing a caller to handle the exception as well)::
import sys
try:
f = open('myfile.txt')
s = f.readline()
i = int(s.strip())
except IOError as e:
(errno, strerror) = e
print "I/O error(%s): %s" % (e.errno, e.strerror)
except ValueError:
print "Could not convert data to an integer."
except:
print "Unexpected error:", sys.exc_info()[0]
raise
The :keyword:`try` ... :keyword:`except` statement has an optional *else
clause*, which, when present, must follow all except clauses. It is useful for
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()
The use of the :keyword:`else` clause is better than adding additional code to
the :keyword:`try` clause because it avoids accidentally catching an exception
that wasn't raised by the code being protected by the :keyword:`try` ...
:keyword:`except` statement.
When an exception occurs, it may have an associated value, also known as the
exception's *argument*. The presence and type of the argument depend on the
exception type.
The except clause may specify a variable after the exception name (or tuple).
The variable is bound to an exception instance with the arguments stored in
``instance.args``. For convenience, the exception instance defines
:meth:`__getitem__` and :meth:`__str__` so the arguments can be accessed or
printed directly without having to reference ``.args``.
But use of ``.args`` is discouraged. Instead, the preferred use is to pass a
single argument to an exception (which can be a tuple if multiple arguments are
needed) and have it bound to the ``message`` attribute. One may also
instantiate an exception first before raising it and add any attributes to it as
desired. ::
>>> try:
... raise Exception('spam', 'eggs')
... except Exception as inst:
... print type(inst) # the exception instance
... print inst.args # arguments stored in .args
... print inst # __str__ allows args to printed directly
... x, y = inst # __getitem__ allows args to be unpacked directly
... print 'x =', x
... print 'y =', y
...
<type 'Exception'>
('spam', 'eggs')
('spam', 'eggs')
x = spam
y = eggs
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 as detail:
... print 'Handling run-time error:', detail
...
Handling run-time error: integer division or modulo by zero
.. _tut-raising:
Raising Exceptions
==================
The :keyword:`raise` statement allows the programmer to force a specified
exception to occur. For example::
>>> raise NameError, 'HiThere'
Traceback (most recent call last):
File "<stdin>", line 1, in ?
NameError: HiThere
The first argument to :keyword:`raise` names the exception to be raised. The
optional second argument specifies the exception's argument. Alternatively, the
above could be written as ``raise NameError('HiThere')``. Either form works
fine, but there seems to be a growing stylistic preference for the latter.
If you need to determine whether an exception was raised but don't intend to
handle it, a simpler form of the :keyword:`raise` statement allows you to
re-raise the exception::
>>> try:
... raise NameError, 'HiThere'
... except NameError:
... print 'An exception flew by!'
... raise
...
An exception flew by!
Traceback (most recent call last):
File "<stdin>", line 2, in ?
NameError: HiThere
.. _tut-userexceptions:
User-defined Exceptions
=======================
Programs may name their own exceptions by creating a new exception class.
Exceptions should typically be derived from the :exc:`Exception` class, either
directly or indirectly. For example::
>>> class MyError(Exception):
... def __init__(self, value):
... self.value = value
... def __str__(self):
... return repr(self.value)
...
>>> try:
... raise MyError(2*2)
... except MyError as e:
... print 'My exception occurred, value:', e.value
...
My exception occurred, value: 4
>>> raise MyError, 'oops!'
Traceback (most recent call last):
File "<stdin>", line 1, in ?
__main__.MyError: 'oops!'
In this example, the default :meth:`__init__` of :class:`Exception` has been
overridden. The new behavior simply creates the *value* attribute. This
replaces the default behavior of creating the *args* attribute.
Exception classes can be defined which do anything any other class can do, but
are usually kept simple, often only offering a number of attributes that allow
information about the error to be extracted by handlers for the exception. When
creating a module that can raise several distinct errors, a common practice is
to create a base class for exceptions defined by that module, and subclass that
to create specific exception classes for different error conditions::
class Error(Exception):
"""Base class for exceptions in this module."""
pass
class InputError(Error):
"""Exception raised for errors in the input.
Attributes:
expression -- input expression in which the error occurred
message -- explanation of the error
"""
def __init__(self, expression, message):
self.expression = expression
self.message = message
class TransitionError(Error):
"""Raised when an operation attempts a state transition that's not
allowed.
Attributes:
previous -- state at beginning of transition
next -- attempted new state
message -- explanation of why the specific transition is not allowed
"""
def __init__(self, previous, next, message):
self.previous = previous
self.next = next
self.message = message
Most exceptions are defined with names that end in "Error," similar to the
naming of the standard exceptions.
Many standard modules define their own exceptions to report errors that may
occur in functions they define. More information on classes is presented in
chapter :ref:`tut-classes`.
.. _tut-cleanup:
Defining Clean-up Actions
=========================
The :keyword:`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 (most recent call last):
File "<stdin>", line 2, in ?
KeyboardInterrupt
A *finally clause* is always executed before leaving the :keyword:`try`
statement, whether an exception has occurred or not. When an exception has
occurred in the :keyword:`try` clause and has not been handled by an
:keyword:`except` clause (or it has occurred in a :keyword:`except` or
:keyword:`else` clause), it is re-raised after the :keyword:`finally` clause has
been executed. The :keyword:`finally` clause is also executed "on the way out"
when any other clause of the :keyword:`try` statement is left via a
:keyword:`break`, :keyword:`continue` or :keyword:`return` statement. A more
complicated example (having :keyword:`except` and :keyword:`finally` clauses in
the same :keyword:`try` statement works as of Python 2.5)::
>>> def divide(x, y):
... try:
... result = x / y
... except ZeroDivisionError:
... print "division by zero!"
... else:
... print "result is", result
... finally:
... print "executing finally clause"
...
>>> divide(2, 1)
result is 2
executing finally clause
>>> divide(2, 0)
division by zero!
executing finally clause
>>> divide("2", "1")
executing finally clause
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 3, in divide
TypeError: unsupported operand type(s) for /: 'str' and 'str'
As you can see, the :keyword:`finally` clause is executed in any event. The
:exc:`TypeError` raised by dividing two strings is not handled by the
:keyword:`except` clause and therefore re-raised after the :keyword:`finally`
clauses has been executed.
In real world applications, the :keyword:`finally` clause is useful for
releasing external resources (such as files or network connections), regardless
of whether the use of the resource was successful.
.. _tut-cleanup-with:
Predefined Clean-up Actions
===========================
Some objects define standard clean-up actions to be undertaken when the object
is no longer needed, regardless of whether or not the operation using the object
succeeded or failed. Look at the following example, which tries to open a file
and print its contents to the screen. ::
for line in open("myfile.txt"):
print line
The problem with this code is that it leaves the file open for an indeterminate
amount of time after the code has finished executing. This is not an issue in
simple scripts, but can be a problem for larger applications. The
:keyword:`with` statement allows objects like files to be used in a way that
ensures they are always cleaned up promptly and correctly. ::
with open("myfile.txt") as f:
for line in f:
print line
After the statement is executed, the file *f* is always closed, even if a
problem was encountered while processing the lines. Other objects which provide
predefined clean-up actions will indicate this in their documentation.

220
Doc/tutorial/floatingpoint.rst Normal file
View file

@ -0,0 +1,220 @@
.. _tut-fp-issues:
**************************************************
Floating Point Arithmetic: Issues and Limitations
**************************************************
.. sectionauthor:: Tim Peters <tim_one@users.sourceforge.net>
Floating-point numbers are represented in computer hardware as base 2 (binary)
fractions. For example, the decimal fraction ::
0.125
has value 1/10 + 2/100 + 5/1000, and in the same way the binary fraction ::
0.001
has value 0/2 + 0/4 + 1/8. These two fractions have identical values, the only
real difference being that the first is written in base 10 fractional notation,
and the second in base 2.
Unfortunately, most decimal fractions cannot be represented exactly as binary
fractions. A consequence is that, in general, the decimal floating-point
numbers you enter are only approximated by the binary floating-point numbers
actually stored in the machine.
The problem is easier to understand at first in base 10. Consider the fraction
1/3. You can approximate that as a base 10 fraction::
0.3
or, better, ::
0.33
or, better, ::
0.333
and so on. No matter how many digits you're willing to write down, the result
will never be exactly 1/3, but will be an increasingly better approximation of
1/3.
In the same way, no matter how many base 2 digits you're willing to use, the
decimal value 0.1 cannot be represented exactly as a base 2 fraction. In base
2, 1/10 is the infinitely repeating fraction ::
0.0001100110011001100110011001100110011001100110011...
Stop at any finite number of bits, and you get an approximation. This is why
you see things like::
>>> 0.1
0.10000000000000001
On most machines today, that is what you'll see if you enter 0.1 at a Python
prompt. You may not, though, because the number of bits used by the hardware to
store floating-point values can vary across machines, and Python only prints a
decimal approximation to the true decimal value of the binary approximation
stored by the machine. On most machines, if Python were to print the true
decimal value of the binary approximation stored for 0.1, it would have to
display ::
>>> 0.1
0.1000000000000000055511151231257827021181583404541015625
instead! The Python prompt uses the builtin :func:`repr` function to obtain a
string version of everything it displays. For floats, ``repr(float)`` rounds
the true decimal value to 17 significant digits, giving ::
0.10000000000000001
``repr(float)`` produces 17 significant digits because it turns out that's
enough (on most machines) so that ``eval(repr(x)) == x`` exactly for all finite
floats *x*, but rounding to 16 digits is not enough to make that true.
Note that this is in the very nature of binary floating-point: this is not a bug
in Python, and it is not a bug in your code either. You'll see the same kind of
thing in all languages that support your hardware's floating-point arithmetic
(although some languages may not *display* the difference by default, or in all
output modes).
Python's builtin :func:`str` function produces only 12 significant digits, and
you may wish to use that instead. It's unusual for ``eval(str(x))`` to
reproduce *x*, but the output may be more pleasant to look at::
>>> print str(0.1)
0.1
It's important to realize that this is, in a real sense, an illusion: the value
in the machine is not exactly 1/10, you're simply rounding the *display* of the
true machine value.
Other surprises follow from this one. For example, after seeing ::
>>> 0.1
0.10000000000000001
you may be tempted to use the :func:`round` function to chop it back to the
single digit you expect. But that makes no difference::
>>> round(0.1, 1)
0.10000000000000001
The problem is that the binary floating-point value stored for "0.1" was already
the best possible binary approximation to 1/10, so trying to round it again
can't make it better: it was already as good as it gets.
Another consequence is that since 0.1 is not exactly 1/10, summing ten values of
0.1 may not yield exactly 1.0, either::
>>> sum = 0.0
>>> for i in range(10):
... sum += 0.1
...
>>> sum
0.99999999999999989
Binary floating-point arithmetic holds many surprises like this. The problem
with "0.1" is explained in precise detail below, in the "Representation Error"
section. See `The Perils of Floating Point <http://www.lahey.com/float.htm>`_
for a more complete account of other common surprises.
As that says near the end, "there are no easy answers." Still, don't be unduly
wary of floating-point! The errors in Python float operations are inherited
from the floating-point hardware, and on most machines are on the order of no
more than 1 part in 2\*\*53 per operation. That's more than adequate for most
tasks, but you do need to keep in mind that it's not decimal arithmetic, and
that every float operation can suffer a new rounding error.
While pathological cases do exist, for most casual use of floating-point
arithmetic you'll see the result you expect in the end if you simply round the
display of your final results to the number of decimal digits you expect.
:func:`str` usually suffices, and for finer control see the discussion of
Python's ``%`` format operator: the ``%g``, ``%f`` and ``%e`` format codes
supply flexible and easy ways to round float results for display.
.. _tut-fp-error:
Representation Error
====================
This section explains the "0.1" example in detail, and shows how you can perform
an exact analysis of cases like this yourself. Basic familiarity with binary
floating-point representation is assumed.
:dfn:`Representation error` refers to the fact that some (most, actually)
decimal fractions cannot be represented exactly as binary (base 2) fractions.
This is the chief reason why Python (or Perl, C, C++, Java, Fortran, and many
others) often won't display the exact decimal number you expect::
>>> 0.1
0.10000000000000001
Why is that? 1/10 is not exactly representable as a binary fraction. Almost all
machines today (November 2000) use IEEE-754 floating point arithmetic, and
almost all platforms map Python floats to IEEE-754 "double precision". 754
doubles contain 53 bits of precision, so on input the computer strives to
convert 0.1 to the closest fraction it can of the form *J*/2\*\**N* where *J* is
an integer containing exactly 53 bits. Rewriting ::
1 / 10 ~= J / (2**N)
as ::
J ~= 2**N / 10
and recalling that *J* has exactly 53 bits (is ``>= 2**52`` but ``< 2**53``),
the best value for *N* is 56::
>>> 2**52
4503599627370496L
>>> 2**53
9007199254740992L
>>> 2**56/10
7205759403792793L
That is, 56 is the only value for *N* that leaves *J* with exactly 53 bits. The
best possible value for *J* is then that quotient rounded::
>>> q, r = divmod(2**56, 10)
>>> r
6L
Since the remainder is more than half of 10, the best approximation is obtained
by rounding up::
>>> q+1
7205759403792794L
Therefore the best possible approximation to 1/10 in 754 double precision is
that over 2\*\*56, or ::
7205759403792794 / 72057594037927936
Note that since we rounded up, this is actually a little bit larger than 1/10;
if we had not rounded up, the quotient would have been a little bit smaller than
1/10. But in no case can it be *exactly* 1/10!
So the computer never "sees" 1/10: what it sees is the exact fraction given
above, the best 754 double approximation it can get::
>>> .1 * 2**56
7205759403792794.0
If we multiply that fraction by 10\*\*30, we can see the (truncated) value of
its 30 most significant decimal digits::
>>> 7205759403792794 * 10**30 / 2**56
100000000000000005551115123125L
meaning that the exact number stored in the computer is approximately equal to
the decimal value 0.100000000000000005551115123125. Rounding that to 17
significant digits gives the 0.10000000000000001 that Python displays (well,
will display on any 754-conforming platform that does best-possible input and
output conversions in its C library --- yours may not!).

329
Doc/tutorial/glossary.rst Normal file
View file

@ -0,0 +1,329 @@
.. _tut-glossary:
********
Glossary
********
.. % %% keep the entries sorted and include at least one \index{} item for each
.. % %% cross-references are marked with \emph{entry}
``>>>``
The typical Python prompt of the interactive shell. Often seen for code
examples that can be tried right away in the interpreter.
.. index:: single: ...
``...``
The typical Python prompt of the interactive shell when entering code for an
indented code block.
.. index:: single: BDFL
BDFL
Benevolent Dictator For Life, a.k.a. `Guido van Rossum
<http://www.python.org/~guido/>`_, Python's creator.
.. index:: single: byte code
byte code
The internal representation of a Python program in the interpreter. The byte
code is also cached in ``.pyc`` and ``.pyo`` files so that executing the same
file is faster the second time (recompilation from source to byte code can be
avoided). This "intermediate language" is said to run on a "virtual machine"
that calls the subroutines corresponding to each bytecode.
.. index:: single: classic class
classic class
Any class which does not inherit from :class:`object`. See *new-style class*.
.. index:: single: complex number
complex number
An extension of the familiar real number system in which all numbers are
expressed as a sum of a real part and an imaginary part. Imaginary numbers are
real multiples of the imaginary unit (the square root of ``-1``), often written
``i`` in mathematics or ``j`` in engineering. Python has builtin support for
complex numbers, which are written with this latter notation; the imaginary part
is written with a ``j`` suffix, e.g., ``3+1j``. To get access to complex
equivalents of the :mod:`math` module, use :mod:`cmath`. Use of complex numbers
is a fairly advanced mathematical feature. If you're not aware of a need for
them, it's almost certain you can safely ignore them.
.. index:: single: descriptor
descriptor
Any *new-style* object that defines the methods :meth:`__get__`,
:meth:`__set__`, or :meth:`__delete__`. When a class attribute is a descriptor,
its special binding behavior is triggered upon attribute lookup. Normally,
writing *a.b* looks up the object *b* in the class dictionary for *a*, but if
*b* is a descriptor, the defined method gets called. Understanding descriptors
is a key to a deep understanding of Python because they are the basis for many
features including functions, methods, properties, class methods, static
methods, and reference to super classes.
.. index:: single: dictionary
dictionary
An associative array, where arbitrary keys are mapped to values. The use of
:class:`dict` much resembles that for :class:`list`, but the keys can be any
object with a :meth:`__hash__` function, not just integers starting from zero.
Called a hash in Perl.
.. index:: single: duck-typing
duck-typing
Pythonic programming style that determines an object's type by inspection of its
method or attribute signature rather than by explicit relationship to some type
object ("If it looks like a duck and quacks like a duck, it must be a duck.")
By emphasizing interfaces rather than specific types, well-designed code
improves its flexibility by allowing polymorphic substitution. Duck-typing
avoids tests using :func:`type` or :func:`isinstance`. Instead, it typically
employs :func:`hasattr` tests or *EAFP* programming.
.. index:: single: EAFP
EAFP
Easier to ask for forgiveness than permission. This common Python coding style
assumes the existence of valid keys or attributes and catches exceptions if the
assumption proves false. This clean and fast style is characterized by the
presence of many :keyword:`try` and :keyword:`except` statements. The technique
contrasts with the *LBYL* style that is common in many other languages such as
C.
.. index:: single: __future__
__future__
A pseudo module which programmers can use to enable new language features which
are not compatible with the current interpreter. To enable ``new_feature`` ::
from __future__ import new_feature
By importing the :mod:`__future__` module and evaluating its variables, you
can see when a new feature was first added to the language and when it will
become the default::
>>> import __future__
>>> __future__.division
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
.. index:: single: generator
generator
A function that returns an iterator. It looks like a normal function except
that values are returned to the caller using a :keyword:`yield` statement
instead of a :keyword:`return` statement. Generator functions often contain one
or more :keyword:`for` or :keyword:`while` loops that :keyword:`yield` elements
back to the caller. The function execution is stopped at the :keyword:`yield`
keyword (returning the result) and is resumed there when the next element is
requested by calling the :meth:`__next__` method of the returned iterator.
.. index:: single: generator expression
generator expression
An expression that returns a generator. It looks like a normal expression
followed by a :keyword:`for` expression defining a loop variable, range, and an
optional :keyword:`if` expression. The combined expression generates values for
an enclosing function::
>>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81
285
.. index:: single: GIL
GIL
See *global interpreter lock*.
.. index:: single: global interpreter lock
global interpreter lock
The lock used by Python threads to assure that only one thread can be run at
a time. This simplifies Python by assuring that no two processes can access
the same memory at the same time. Locking the entire interpreter makes it
easier for the interpreter to be multi-threaded, at the expense of some
parallelism on multi-processor machines. Efforts have been made in the past
to create a "free-threaded" interpreter (one which locks shared data at a
much finer granularity), but performance suffered in the common
single-processor case.
.. index:: single: IDLE
IDLE
An Integrated Development Environment for Python. IDLE is a basic editor and
interpreter environment that ships with the standard distribution of Python.
Good for beginners, it also serves as clear example code for those wanting to
implement a moderately sophisticated, multi-platform GUI application.
.. index:: single: immutable
immutable
An object with fixed value. Immutable objects are numbers, strings or tuples
(and more). Such an object cannot be altered. A new object has to be created
if a different value has to be stored. They play an important role in places
where a constant hash value is needed, for example as a key in a dictionary.
.. index:: single: integer division
integer division
Mathematical division including any remainder. The result will always be a
float. For example, the expression ``11/4`` evaluates to ``2.75``. Integer
division can be forced by using the ``//`` operator instead of the ``/``
operator.
.. index:: single: interactive
interactive
Python has an interactive interpreter which means that you can try out things
and immediately see their results. Just launch ``python`` with no arguments
(possibly by selecting it from your computer's main menu). It is a very powerful
way to test out new ideas or inspect modules and packages (remember
``help(x)``).
.. index:: single: interpreted
interpreted
Python is an interpreted language, as opposed to a compiled one. This means
that the source files can be run directly without first creating an executable
which is then run. Interpreted languages typically have a shorter
development/debug cycle than compiled ones, though their programs generally also
run more slowly. See also *interactive*.
.. index:: single: iterable
iterable
A container object capable of returning its members one at a time. Examples of
iterables include all sequence types (such as :class:`list`, :class:`str`, and
:class:`tuple`) and some non-sequence types like :class:`dict` and :class:`file`
and objects of any classes you define with an :meth:`__iter__` or
:meth:`__getitem__` method. Iterables can be used in a :keyword:`for` loop and
in many other places where a sequence is needed (:func:`zip`, :func:`map`, ...).
When an iterable object is passed as an argument to the builtin function
:func:`iter`, it returns an iterator for the object. This iterator is good for
one pass over the set of values. When using iterables, it is usually not
necessary to call :func:`iter` or deal with iterator objects yourself. The
``for`` statement does that automatically for you, creating a temporary unnamed
variable to hold the iterator for the duration of the loop. See also
*iterator*, *sequence*, and *generator*.
.. index:: single: iterator
iterator
An object representing a stream of data. Repeated calls to the iterator's
:meth:`__next__` method return successive items in the stream. When no more
data is available a :exc:`StopIteration` exception is raised instead. At this
point, the iterator object is exhausted and any further calls to its
:meth:`__next__` method just raise :exc:`StopIteration` again. Iterators are
required to have an :meth:`__iter__` method that returns the iterator object
itself so every iterator is also iterable and may be used in most places where
other iterables are accepted. One notable exception is code that attempts
multiple iteration passes. A container object (such as a :class:`list`)
produces a fresh new iterator each time you pass it to the :func:`iter` function
or use it in a :keyword:`for` loop. Attempting this with an iterator will just
return the same exhausted iterator object used in the previous iteration pass,
making it appear like an empty container.
.. index:: single: LBYL
LBYL
Look before you leap. This coding style explicitly tests for pre-conditions
before making calls or lookups. This style contrasts with the *EAFP* approach
and is characterized by the presence of many :keyword:`if` statements.
.. index:: single: list comprehension
list comprehension
A compact way to process all or a subset of elements in a sequence and return a
list with the results. ``result = ["0x%02x" % x for x in range(256) if x % 2 ==
0]`` generates a list of strings containing hex numbers (0x..) that are even and
in the range from 0 to 255. The :keyword:`if` clause is optional. If omitted,
all elements in ``range(256)`` are processed.
.. index:: single: mapping
mapping
A container object (such as :class:`dict`) that supports arbitrary key lookups
using the special method :meth:`__getitem__`.
.. index:: single: metaclass
metaclass
The class of a class. Class definitions create a class name, a class
dictionary, and a list of base classes. The metaclass is responsible for taking
those three arguments and creating the class. Most object oriented programming
languages provide a default implementation. What makes Python special is that
it is possible to create custom metaclasses. Most users never need this tool,
but when the need arises, metaclasses can provide powerful, elegant solutions.
They have been used for logging attribute access, adding thread-safety, tracking
object creation, implementing singletons, and many other tasks.
.. index:: single: mutable
mutable
Mutable objects can change their value but keep their :func:`id`. See also
*immutable*.
.. index:: single: namespace
namespace
The place where a variable is stored. Namespaces are implemented as
dictionaries. There are the local, global and builtin namespaces as well as
nested namespaces in objects (in methods). Namespaces support modularity by
preventing naming conflicts. For instance, the functions
:func:`__builtin__.open` and :func:`os.open` are distinguished by their
namespaces. Namespaces also aid readability and maintainability by making it
clear which module implements a function. For instance, writing
:func:`random.seed` or :func:`itertools.izip` makes it clear that those
functions are implemented by the :mod:`random` and :mod:`itertools` modules
respectively.
.. index:: single: nested scope
nested scope
The ability to refer to a variable in an enclosing definition. For instance, a
function defined inside another function can refer to variables in the outer
function. Note that nested scopes work only for reference and not for
assignment which will always write to the innermost scope. In contrast, local
variables both read and write in the innermost scope. Likewise, global
variables read and write to the global namespace.
.. index:: single: new-style class
new-style class
Any class that inherits from :class:`object`. This includes all built-in types
like :class:`list` and :class:`dict`. Only new-style classes can use Python's
newer, versatile features like :meth:`__slots__`, descriptors, properties,
:meth:`__getattribute__`, class methods, and static methods.
.. index:: single: Python3000
Python3000
A mythical python release, not required to be backward compatible, with
telepathic interface.
.. index:: single: __slots__
__slots__
A declaration inside a *new-style class* that saves memory by pre-declaring
space for instance attributes and eliminating instance dictionaries. Though
popular, the technique is somewhat tricky to get right and is best reserved for
rare cases where there are large numbers of instances in a memory-critical
application.
.. index:: single: sequence
sequence
An *iterable* which supports efficient element access using integer indices via
the :meth:`__getitem__` and :meth:`__len__` special methods. Some built-in
sequence types are :class:`list`, :class:`str`, :class:`tuple`, and
:class:`unicode`. Note that :class:`dict` also supports :meth:`__getitem__` and
:meth:`__len__`, but is considered a mapping rather than a sequence because the
lookups use arbitrary *immutable* keys rather than integers.
.. index:: single: Zen of Python
Zen of Python
Listing of Python design principles and philosophies that are helpful in
understanding and using the language. The listing can be found by typing
"``import this``" at the interactive prompt.

60
Doc/tutorial/index.rst Normal file
View file

@ -0,0 +1,60 @@
.. _tutorial-index:
######################
The Python tutorial
######################
:Release: |version|
:Date: |today|
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 may 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 Extending and Embedding
the Python Interpreter and Python/C API Reference. 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.
.. toctree::
appetite.rst
interpreter.rst
introduction.rst
controlflow.rst
datastructures.rst
modules.rst
inputoutput.rst
errors.rst
classes.rst
stdlib.rst
stdlib2.rst
whatnow.rst
interactive.rst
floatingpoint.rst
glossary.rst

354
Doc/tutorial/inputoutput.rst Normal file
View file

@ -0,0 +1,354 @@
.. _tut-io:
****************
Input and Output
****************
There are several ways to present the output of a program; data can be printed
in a human-readable form, or written to a file for future use. This chapter will
discuss some of the possibilities.
.. _tut-formatting:
Fancier Output Formatting
=========================
So far we've encountered two ways of writing values: *expression statements* and
the :keyword:`print` statement. (A third way is using the :meth:`write` method
of file objects; the standard output file can be referenced as ``sys.stdout``.
See the Library Reference for more information on this.)
.. index:: module: string
Often you'll want more control over the formatting of your output than simply
printing space-separated values. There are two ways to format your output; the
first way is to do all the string handling yourself; using string slicing and
concatenation operations you can create any layout you can imagine. The
standard module :mod:`string` contains some useful operations for padding
strings to a given column width; these will be discussed shortly. The second
way is to use the ``%`` operator with a string as the left argument. The ``%``
operator interprets the left argument much like a :cfunc:`sprintf`\ -style
format string to be applied to the right argument, and returns the string
resulting from this formatting operation.
One question remains, of course: how do you convert values to strings? Luckily,
Python has ways to convert any value to a string: pass it to the :func:`repr`
or :func:`str` functions. Reverse quotes (``````) are equivalent to
:func:`repr`, but they are no longer used in modern Python code and will likely
not be in future versions of the language.
The :func:`str` function is meant to return representations of values which are
fairly human-readable, while :func:`repr` is meant to generate representations
which can be read by the interpreter (or will force a :exc:`SyntaxError` if
there is not equivalent syntax). For objects which don't have a particular
representation for human consumption, :func:`str` will return the same value as
:func:`repr`. Many values, such as numbers or structures like lists and
dictionaries, have the same representation using either function. Strings and
floating point numbers, in particular, have two distinct representations.
Some examples::
>>> s = 'Hello, world.'
>>> str(s)
'Hello, world.'
>>> repr(s)
"'Hello, world.'"
>>> str(0.1)
'0.1'
>>> repr(0.1)
'0.10000000000000001'
>>> x = 10 * 3.25
>>> y = 200 * 200
>>> s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
>>> print s
The value of x is 32.5, and y is 40000...
>>> # The repr() of a string adds string quotes and backslashes:
... hello = 'hello, world\n'
>>> hellos = repr(hello)
>>> print hellos
'hello, world\n'
>>> # The argument to repr() may be any Python object:
... repr((x, y, ('spam', 'eggs')))
"(32.5, 40000, ('spam', 'eggs'))"
>>> # reverse quotes are convenient in interactive sessions:
... `x, y, ('spam', 'eggs')`
"(32.5, 40000, ('spam', 'eggs'))"
Here are two ways to write a table of squares and cubes::
>>> for x in range(1, 11):
... print repr(x).rjust(2), repr(x*x).rjust(3),
... # Note trailing comma on previous line
... print repr(x*x*x).rjust(4)
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
>>> for x in range(1,11):
... print '%2d %3d %4d' % (x, x*x, x*x*x)
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
(Note that in the first example, one space between each column was added by the
way :keyword:`print` works: it always adds spaces between its arguments.)
This example demonstrates the :meth:`rjust` method of string objects, which
right-justifies a string in a field of a given width by padding it with spaces
on the left. There are similar methods :meth:`ljust` and :meth:`center`. These
methods do not write anything, they just return a new string. If the input
string is too long, they don't truncate it, but return it unchanged; this will
mess up your column lay-out but that's usually better than the alternative,
which would be lying about a value. (If you really want truncation you can
always add a slice operation, as in ``x.ljust(n)[:n]``.)
There is another method, :meth:`zfill`, which pads a numeric string on the left
with zeros. It understands about plus and minus signs::
>>> '12'.zfill(5)
'00012'
>>> '-3.14'.zfill(7)
'-003.14'
>>> '3.14159265359'.zfill(5)
'3.14159265359'
Using the ``%`` operator looks like this::
>>> import math
>>> print 'The value of PI is approximately %5.3f.' % math.pi
The value of PI is approximately 3.142.
If there is more than one format in the string, you need to pass a tuple as
right operand, as in this example::
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
>>> for name, phone in table.items():
... print '%-10s ==> %10d' % (name, phone)
...
Jack ==> 4098
Dcab ==> 7678
Sjoerd ==> 4127
Most formats work exactly as in C and require that you pass the proper type;
however, if you don't you get an exception, not a core dump. The ``%s`` format
is more relaxed: if the corresponding argument is not a string object, it is
converted to string using the :func:`str` built-in function. Using ``*`` to
pass the width or precision in as a separate (integer) argument is supported.
The C formats ``%n`` and ``%p`` are not supported.
If you have a really long format string that you don't want to split up, it
would be nice if you could reference the variables to be formatted by name
instead of by position. This can be done by using form ``%(name)format``, as
shown here::
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print 'Jack: %(Jack)d; Sjoerd: %(Sjoerd)d; Dcab: %(Dcab)d' % table
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This is particularly useful in combination with the new built-in :func:`vars`
function, which returns a dictionary containing all local variables.
.. _tut-files:
Reading and Writing Files
=========================
.. index::
builtin: open
object: file
:func:`open` returns a file object, and is most commonly used with two
arguments: ``open(filename, mode)``.
.. % Opening files
::
>>> f=open('/tmp/workfile', 'w')
>>> print f
<open file '/tmp/workfile', mode 'w' at 80a0960>
The first argument is a string containing the filename. The second argument is
another string containing a few characters describing the way in which the file
will be used. *mode* can be ``'r'`` when the file will only be read, ``'w'``
for only writing (an existing file with the same name will be erased), and
``'a'`` opens the file for appending; any data written to the file is
automatically added to the end. ``'r+'`` opens the file for both reading and
writing. The *mode* argument is optional; ``'r'`` will be assumed if it's
omitted.
On Windows and the Macintosh, ``'b'`` appended to the mode opens the file in
binary mode, so there are also modes like ``'rb'``, ``'wb'``, and ``'r+b'``.
Windows makes a distinction between text and binary files; the end-of-line
characters in text files are automatically altered slightly when data is read or
written. This behind-the-scenes modification to file data is fine for ASCII
text files, but it'll corrupt binary data like that in :file:`JPEG` or
:file:`EXE` files. Be very careful to use binary mode when reading and writing
such files.
.. _tut-filemethods:
Methods of File Objects
-----------------------
The rest of the examples in this section will assume that a file object called
``f`` has already been created.
To read a file's contents, call ``f.read(size)``, which reads some quantity of
data and returns it as a string. *size* is an optional numeric argument. When
*size* is omitted or negative, the entire contents of the file will be read and
returned; it's your problem if the file is twice as large as your machine's
memory. Otherwise, at most *size* bytes are read and returned. If the end of
the file has been reached, ``f.read()`` will return an empty string (``""``).
::
>>> f.read()
'This is the entire file.\n'
>>> f.read()
''
``f.readline()`` reads a single line from the file; a newline character (``\n``)
is left at the end of the string, and is only omitted on the last line of the
file if the file doesn't end in a newline. This makes the return value
unambiguous; if ``f.readline()`` returns an empty string, the end of the file
has been reached, while a blank line is represented by ``'\n'``, a string
containing only a single newline. ::
>>> f.readline()
'This is the first line of the file.\n'
>>> f.readline()
'Second line of the file\n'
>>> f.readline()
''
``f.readlines()`` returns a list containing all the lines of data in the file.
If given an optional parameter *sizehint*, it reads that many bytes from the
file and enough more to complete a line, and returns the lines from that. This
is often used to allow efficient reading of a large file by lines, but without
having to load the entire file in memory. Only complete lines will be returned.
::
>>> f.readlines()
['This is the first line of the file.\n', 'Second line of the file\n']
An alternate approach to reading lines is to loop over the file object. This is
memory efficient, fast, and leads to simpler code::
>>> for line in f:
print line,
This is the first line of the file.
Second line of the file
The alternative approach is simpler but does not provide as fine-grained
control. Since the two approaches manage line buffering differently, they
should not be mixed.
``f.write(string)`` writes the contents of *string* to the file, returning
``None``. ::
>>> f.write('This is a test\n')
To write something other than a string, it needs to be converted to a string
first::
>>> value = ('the answer', 42)
>>> s = str(value)
>>> f.write(s)
``f.tell()`` returns an integer giving the file object's current position in the
file, measured in bytes from the beginning of the file. To change the file
object's position, use ``f.seek(offset, from_what)``. The position is computed
from adding *offset* to a reference point; the reference point is selected by
the *from_what* argument. A *from_what* value of 0 measures from the beginning
of the file, 1 uses the current file position, and 2 uses the end of the file as
the reference point. *from_what* can be omitted and defaults to 0, using the
beginning of the file as the reference point. ::
>>> f = open('/tmp/workfile', 'r+')
>>> f.write('0123456789abcdef')
>>> f.seek(5) # Go to the 6th byte in the file
>>> f.read(1)
'5'
>>> f.seek(-3, 2) # Go to the 3rd byte before the end
>>> f.read(1)
'd'
When you're done with a file, call ``f.close()`` to close it and free up any
system resources taken up by the open file. After calling ``f.close()``,
attempts to use the file object will automatically fail. ::
>>> f.close()
>>> f.read()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: I/O operation on closed file
File objects have some additional methods, such as :meth:`isatty` and
:meth:`truncate` which are less frequently used; consult the Library Reference
for a complete guide to file objects.
.. _tut-pickle:
The :mod:`pickle` Module
------------------------
.. index:: module: pickle
Strings can easily be written to and read from a file. Numbers take a bit more
effort, since the :meth:`read` method only returns strings, which will have to
be passed to a function like :func:`int`, which takes a string like ``'123'``
and returns its numeric value 123. However, when you want to save more complex
data types like lists, dictionaries, or class instances, things get a lot more
complicated.
Rather than have users be constantly writing and debugging code to save
complicated data types, Python provides a standard module called :mod:`pickle`.
This is an amazing module that can take almost any Python object (even some
forms of Python code!), and convert it to a string representation; this process
is called :dfn:`pickling`. Reconstructing the object from the string
representation is called :dfn:`unpickling`. Between pickling and unpickling,
the string representing the object may have been stored in a file or data, or
sent over a network connection to some distant machine.
If you have an object ``x``, and a file object ``f`` that's been opened for
writing, the simplest way to pickle the object takes only one line of code::
pickle.dump(x, f)
To unpickle the object again, if ``f`` is a file object which has been opened
for reading::
x = pickle.load(f)
(There are other variants of this, used when pickling many objects or when you
don't want to write the pickled data to a file; consult the complete
documentation for :mod:`pickle` in the Python Library Reference.)
:mod:`pickle` is the standard way to make Python objects which can be stored and
reused by other programs or by a future invocation of the same program; the
technical term for this is a :dfn:`persistent` object. Because :mod:`pickle` is
so widely used, many authors who write Python extensions take care to ensure
that new data types such as matrices can be properly pickled and unpickled.

167
Doc/tutorial/interactive.rst Normal file
View file

@ -0,0 +1,167 @@
.. _tut-interacting:
**************************************************
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.
.. _tut-lineediting:
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:
:kbd:`C-A` (Control-A) moves the cursor to the beginning of the line, :kbd:`C-E`
to the end, :kbd:`C-B` moves it one position to the left, :kbd:`C-F` to the
right. Backspace erases the character to the left of the cursor, :kbd:`C-D` the
character to its right. :kbd:`C-K` kills (erases) the rest of the line to the
right of the cursor, :kbd:`C-Y` yanks back the last killed string.
:kbd:`C-underscore` undoes the last change you made; it can be repeated for
cumulative effect.
.. _tut-history:
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. :kbd:`C-P` moves one line up (back) in
the history buffer, :kbd:`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 :kbd:`Return` key passes the current line to the
interpreter. :kbd:`C-R` starts an incremental reverse search; :kbd:`C-S` starts
a forward search.
.. _tut-keybindings:
Key Bindings
============
The key bindings and some other parameters of the Readline library can be
customized by placing commands in an initialization file called
:file:`~/.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 :kbd:`Tab` in Python is to insert a :kbd:`Tab`
character instead of Readline's default filename completion function. If you
insist, you can override this by putting ::
Tab: complete
in your :file:`~/.inputrc`. (Of course, this makes it harder to type indented
continuation lines if you're accustomed to using :kbd:`Tab` for that purpose.)
.. index::
module: rlcompleter
module: readline
Automatic completion of variable and module names is optionally available. To
enable it in the interpreter's interactive mode, add the following to your
startup file: [#]_ ::
import rlcompleter, readline
readline.parse_and_bind('tab: complete')
This binds the :kbd:`Tab` key to the completion function, so hitting the
:kbd:`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 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
:meth:`__getattr__` method is part of the expression.
A more capable startup file might look like this example. Note that this
deletes the names it creates once they are no longer needed; this is done since
the startup file is executed in the same namespace as the interactive commands,
and removing the names avoids creating side effects in the interactive
environment. You may find it convenient to keep some of the imported modules,
such as :mod:`os`, which turn out to be needed in most sessions with the
interpreter. ::
# Add auto-completion and a stored history file of commands to your Python
# interactive interpreter. Requires Python 2.0+, readline. Autocomplete is
# bound to the Esc key by default (you can change it - see readline docs).
#
# Store the file in ~/.pystartup, and set an environment variable to point
# to it: "export PYTHONSTARTUP=/max/home/itamar/.pystartup" in bash.
#
# Note that PYTHONSTARTUP does *not* expand "~", so you have to put in the
# full path to your home directory.
import atexit
import os
import readline
import rlcompleter
historyPath = os.path.expanduser("~/.pyhistory")
def save_history(historyPath=historyPath):
import readline
readline.write_history_file(historyPath)
if os.path.exists(historyPath):
readline.read_history_file(historyPath)
atexit.register(save_history)
del os, atexit, readline, rlcompleter, save_history, historyPath
.. _tut-commentary:
Commentary
==========
This facility is an enormous step forward compared to earlier 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.
.. rubric:: Footnotes
.. [#] Python will execute the contents of a file identified by the
:envvar:`PYTHONSTARTUP` environment variable when you start an interactive
interpreter.

248
Doc/tutorial/interpreter.rst Normal file
View file

@ -0,0 +1,248 @@
.. _tut-using:
****************************
Using the Python Interpreter
****************************
.. _tut-invoking:
Invoking the Interpreter
========================
The Python interpreter is usually installed as :file:`/usr/local/bin/python` on
those machines where it is available; putting :file:`/usr/local/bin` in your
Unix shell's search path makes it possible to start it by typing the command ::
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., :file:`/usr/local/python` is a popular
alternative location.)
On Windows machines, the Python installation is usually placed in
:file:`C:\Python30`, though you can change this when you're running the
installer. To add this directory to your path, you can type the following
command into the command prompt in a DOS box::
set path=%path%;C:\python30
Typing an end-of-file character (:kbd:`Control-D` on Unix, :kbd:`Control-Z` on
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 :ref:`tut-interacting` 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 second way of starting the interpreter is ``python -c command [arg] ...``,
which executes the statement(s) in *command*, analogous to the shell's
:option:`-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.
Some Python modules are also useful as scripts. These can be invoked using
``python -m module [arg] ...``, which executes the source file for *module* as
if you had spelled out its full name on the command line.
Note that there is a difference between ``python file`` and ``python <file``.
In the latter case, input requests from the program, such as calling
``sys.stdin.read()``, 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 end-of-file 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 :option:`-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.)
.. _tut-argpassing:
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
:option:`-c` *command* is used, ``sys.argv[0]`` is set to ``'-c'``. When
:option:`-m` *module* is used, ``sys.argv[0]`` is set to the full name of the
located module. Options found after :option:`-c` *command* or :option:`-m`
*module* are not consumed by the Python interpreter's option processing but
left in ``sys.argv`` for the command or module to handle.
.. _tut-interactive:
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::
python
Python 1.5.2b2 (#1, Feb 28 1999, 00:02:06) [GCC 2.8.1] on sunos5
Copyright 1991-1995 Stichting Mathematisch Centrum, Amsterdam
>>>
Continuation lines are needed when entering a multi-line construct. As an
example, take a look at this :keyword:`if` statement::
>>> the_world_is_flat = 1
>>> if the_world_is_flat:
... print "Be careful not to fall off!"
...
Be careful not to fall off!
.. _tut-interp:
The Interpreter and Its Environment
===================================
.. _tut-error:
Error Handling
--------------
When an error occurs, the interpreter prints an error message and a stack trace.
In interactive mode, it then returns to the primary prompt; when input came from
a file, it exits with a nonzero exit status after printing the stack trace.
(Exceptions handled by an :keyword:`except` clause in a :keyword:`try` statement
are not errors in this context.) Some errors are unconditionally fatal and
cause an exit with a nonzero exit; this applies to internal inconsistencies and
some cases of running out of memory. All error messages are written to the
standard error stream; normal output from executed commands is written to
standard output.
Typing the interrupt character (usually Control-C or DEL) to the primary or
secondary prompt cancels the input and returns to the primary prompt. [#]_
Typing an interrupt while a command is executing raises the
:exc:`KeyboardInterrupt` exception, which may be handled by a :keyword:`try`
statement.
.. _tut-scripts:
Executable Python Scripts
-------------------------
On BSD'ish Unix systems, Python scripts can be made directly executable, like
shell scripts, by putting the line ::
#! /usr/bin/env python
(assuming that the interpreter is on the user's :envvar:`PATH`) at the beginning
of the script and giving the file an executable mode. The ``#!`` must be the
first two characters of the file. On some platforms, this first line must end
with a Unix-style line ending (``'\n'``), not a Mac OS (``'\r'``) or Windows
(``'\r\n'``) line ending. Note that the hash, or pound, character, ``'#'``, is
used to start a comment in Python.
The script can be given an executable mode, or permission, using the
:program:`chmod` command::
$ chmod +x myscript.py
Source Code Encoding
--------------------
It is possible to use encodings different than ASCII in Python source files. The
best way to do it is to put one more special comment line right after the ``#!``
line to define the source file encoding::
# -*- coding: encoding -*-
With that declaration, all characters in the source file will be treated as
having the encoding *encoding*, and it will be possible to directly write
Unicode string literals in the selected encoding. The list of possible
encodings can be found in the Python Library Reference, in the section on
:mod:`codecs`.
For example, to write Unicode literals including the Euro currency symbol, the
ISO-8859-15 encoding can be used, with the Euro symbol having the ordinal value
164. This script will print the value 8364 (the Unicode codepoint corresponding
to the Euro symbol) and then exit::
# -*- coding: iso-8859-15 -*-
currency = u"€"
print ord(currency)
If your editor supports saving files as ``UTF-8`` with a UTF-8 *byte order mark*
(aka BOM), you can use that instead of an encoding declaration. IDLE supports
this capability if ``Options/General/Default Source Encoding/UTF-8`` is set.
Notice that this signature is not understood in older Python releases (2.2 and
earlier), and also not understood by the operating system for script files with
``#!`` lines (only used on Unix systems).
By using UTF-8 (either through the signature or an encoding declaration),
characters of most languages in the world can be used simultaneously in string
literals and comments. Using non-ASCII characters in identifiers is not
supported. To display all these characters properly, your editor must recognize
that the file is UTF-8, and it must use a font that supports all the characters
in the file.
.. _tut-startup:
The Interactive Startup File
----------------------------
When you use Python interactively, it is frequently handy to have some standard
commands executed every time the interpreter is started. You can do this by
setting an environment variable named :envvar:`PYTHONSTARTUP` to the name of a
file containing your start-up commands. This is similar to the :file:`.profile`
feature of the Unix shells.
.. % XXX This should probably be dumped in an appendix, since most people
.. % don't use Python interactively in non-trivial ways.
This file is only read in interactive sessions, not when Python reads commands
from a script, and not when :file:`/dev/tty` is given as the explicit source of
commands (which otherwise behaves like an interactive session). It is executed
in the same namespace where interactive commands are executed, so that objects
that it defines or imports can be used without qualification in the interactive
session. You can also change the prompts ``sys.ps1`` and ``sys.ps2`` in this
file.
If you want to read an additional start-up file from the current directory, you
can program this in the global start-up file using code like ``if
os.path.isfile('.pythonrc.py'): exec(open('.pythonrc.py').read())``.
If you want to use the startup file in a script, you must do this explicitly
in the script::
import os
filename = os.environ.get('PYTHONSTARTUP')
if filename and os.path.isfile(filename):
exec(open(filename).read())
.. rubric:: Footnotes
.. [#] A problem with the GNU Readline package may prevent this.

645
Doc/tutorial/introduction.rst Normal file
View file

@ -0,0 +1,645 @@
.. _tut-informal:
**********************************
An Informal Introduction to Python
**********************************
In the following examples, input and output are distinguished by the presence or
absence of prompts (``>>>`` and ``...``): to repeat the example, you must type
everything after the prompt, when the prompt appears; lines that do not begin
with a prompt are output from the interpreter. Note that a secondary prompt on a
line by itself in an example means you must type a blank line; this is used to
end a multi-line command.
.. %
.. % \footnote{
.. % I'd prefer to use different fonts to distinguish input
.. % from output, but the amount of LaTeX hacking that would require
.. % is currently beyond my ability.
.. % }
Many of the examples in this manual, even those entered at the interactive
prompt, include comments. Comments in Python start with the hash character,
``'#'``, and extend to the end of the physical line. A comment may appear at
the start of a line or following whitespace or code, but not within a string
literal. A hash character within a string literal is just a hash character.
Some examples::
# this is the first comment
SPAM = 1 # and this is the second comment
# ... and now a third!
STRING = "# This is not a comment."
.. _tut-calculator:
Using Python as a Calculator
============================
Let's try some simple Python commands. Start the interpreter and wait for the
primary prompt, ``>>>``. (It shouldn't take long.)
.. _tut-numbers:
Numbers
-------
The interpreter acts as a simple calculator: you can type an expression at it
and it will write the value. Expression syntax is straightforward: the
operators ``+``, ``-``, ``*`` and ``/`` work just like in most other languages
(for example, Pascal or C); parentheses can be used for grouping. For example::
>>> 2+2
4
>>> # This is a comment
... 2+2
4
>>> 2+2 # and a comment on the same line as code
4
>>> (50-5*6)/4
5
>>> # Integer division returns the floor:
... 7/3
2
>>> 7/-3
-3
The equal sign (``'='``) is used to assign a value to a variable. Afterwards, no
result is displayed before the next interactive prompt::
>>> width = 20
>>> height = 5*9
>>> width * height
900
A value can be assigned to several variables simultaneously::
>>> x = y = z = 0 # Zero x, y and z
>>> x
0
>>> y
0
>>> z
0
There is full support for floating point; operators with mixed type operands
convert the integer operand to floating point::
>>> 3 * 3.75 / 1.5
7.5
>>> 7.0 / 2
3.5
Complex numbers are also supported; imaginary numbers are written with a suffix
of ``j`` or ``J``. Complex numbers with a nonzero real component are written as
``(real+imagj)``, or can be created with the ``complex(real, imag)`` function.
::
>>> 1j * 1J
(-1+0j)
>>> 1j * complex(0,1)
(-1+0j)
>>> 3+1j*3
(3+3j)
>>> (3+1j)*3
(9+3j)
>>> (1+2j)/(1+1j)
(1.5+0.5j)
Complex numbers are always represented as two floating point numbers, the real
and imaginary part. To extract these parts from a complex number *z*, use
``z.real`` and ``z.imag``. ::
>>> a=1.5+0.5j
>>> a.real
1.5
>>> a.imag
0.5
The conversion functions to floating point and integer (:func:`float`,
:func:`int` and :func:`long`) don't work for complex numbers --- there is no one
correct way to convert a complex number to a real number. Use ``abs(z)`` to get
its magnitude (as a float) or ``z.real`` to get its real part. ::
>>> a=3.0+4.0j
>>> float(a)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: can't convert complex to float; use abs(z)
>>> a.real
3.0
>>> a.imag
4.0
>>> abs(a) # sqrt(a.real**2 + a.imag**2)
5.0
>>>
In interactive mode, the last printed expression is assigned to the variable
``_``. This means that when you are using Python as a desk calculator, it is
somewhat easier to continue calculations, for example::
>>> tax = 12.5 / 100
>>> price = 100.50
>>> price * tax
12.5625
>>> price + _
113.0625
>>> round(_, 2)
113.06
>>>
This variable should be treated as read-only by the user. Don't explicitly
assign a value to it --- you would create an independent local variable with the
same name masking the built-in variable with its magic behavior.
.. _tut-strings:
Strings
-------
Besides numbers, Python can also manipulate strings, which can be expressed in
several ways. They can be enclosed in single quotes or double quotes::
>>> 'spam eggs'
'spam eggs'
>>> 'doesn\'t'
"doesn't"
>>> "doesn't"
"doesn't"
>>> '"Yes," he said.'
'"Yes," he said.'
>>> "\"Yes,\" he said."
'"Yes," he said.'
>>> '"Isn\'t," she said.'
'"Isn\'t," she said.'
String literals can span multiple lines in several ways. Continuation lines can
be used, with a backslash as the last character on the line indicating that the
next line is a logical continuation of the line::
hello = "This is a rather long string containing\n\
several lines of text just as you would do in C.\n\
Note that whitespace at the beginning of the line is\
significant."
print hello
Note that newlines still need to be embedded in the string using ``\n``; the
newline following the trailing backslash is discarded. This example would print
the following::
This is a rather long string containing
several lines of text just as you would do in C.
Note that whitespace at the beginning of the line is significant.
If we make the string literal a "raw" string, however, the ``\n`` sequences are
not converted to newlines, but the backslash at the end of the line, and the
newline character in the source, are both included in the string as data. Thus,
the example::
hello = r"This is a rather long string containing\n\
several lines of text much as you would do in C."
print hello
would print::
This is a rather long string containing\n\
several lines of text much as you would do in C.
Or, strings can be surrounded in a pair of matching triple-quotes: ``"""`` or
``'''``. End of lines do not need to be escaped when using triple-quotes, but
they will be included in the string. ::
print """
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
"""
produces the following output::
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
The interpreter prints the result of string operations in the same way as they
are typed for input: inside quotes, and with quotes and other funny characters
escaped by backslashes, to show the precise value. The string is enclosed in
double quotes if the string contains a single quote and no double quotes, else
it's enclosed in single quotes. (The :keyword:`print` statement, described
later, can be used to write strings without quotes or escapes.)
Strings can be concatenated (glued together) with the ``+`` operator, and
repeated with ``*``::
>>> word = 'Help' + 'A'
>>> word
'HelpA'
>>> '<' + word*5 + '>'
'<HelpAHelpAHelpAHelpAHelpA>'
Two string literals next to each other are automatically concatenated; the first
line above could also have been written ``word = 'Help' 'A'``; this only works
with two literals, not with arbitrary string expressions::
>>> 'str' 'ing' # <- This is ok
'string'
>>> 'str'.strip() + 'ing' # <- This is ok
'string'
>>> 'str'.strip() 'ing' # <- This is invalid
File "<stdin>", line 1, in ?
'str'.strip() 'ing'
^
SyntaxError: invalid syntax
Strings can be subscripted (indexed); like in C, the first character of a string
has subscript (index) 0. There is no separate character type; a character is
simply a string of size one. Like in Icon, substrings can be specified with the
*slice notation*: two indices separated by a colon. ::
>>> word[4]
'A'
>>> word[0:2]
'He'
>>> word[2:4]
'lp'
Slice indices have useful defaults; an omitted first index defaults to zero, an
omitted second index defaults to the size of the string being sliced. ::
>>> word[:2] # The first two characters
'He'
>>> word[2:] # Everything except the first two characters
'lpA'
Unlike a C string, Python strings cannot be changed. Assigning to an indexed
position in the string results in an error::
>>> word[0] = 'x'
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: object doesn't support item assignment
>>> word[:1] = 'Splat'
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: object doesn't support slice assignment
However, creating a new string with the combined content is easy and efficient::
>>> 'x' + word[1:]
'xelpA'
>>> 'Splat' + word[4]
'SplatA'
Here's a useful invariant of slice operations: ``s[:i] + s[i:]`` equals ``s``.
::
>>> word[:2] + word[2:]
'HelpA'
>>> word[:3] + word[3:]
'HelpA'
Degenerate slice indices are handled gracefully: an index that is too large is
replaced by the string size, an upper bound smaller than the lower bound returns
an empty string. ::
>>> word[1:100]
'elpA'
>>> word[10:]
''
>>> word[2:1]
''
Indices may be negative numbers, to start counting from the right. For example::
>>> word[-1] # The last character
'A'
>>> word[-2] # The last-but-one character
'p'
>>> word[-2:] # The last two characters
'pA'
>>> word[:-2] # Everything except the last two characters
'Hel'
But note that -0 is really the same as 0, so it does not count from the right!
::
>>> word[-0] # (since -0 equals 0)
'H'
Out-of-range negative slice indices are truncated, but don't try this for
single-element (non-slice) indices::
>>> word[-100:]
'HelpA'
>>> word[-10] # error
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IndexError: string index out of range
One way to remember how slices work is to think of the indices as pointing
*between* characters, with the left edge of the first character numbered 0.
Then the right edge of the last character of a string of *n* characters has
index *n*, for example::
+---+---+---+---+---+
| H | e | l | p | A |
+---+---+---+---+---+
0 1 2 3 4 5
-5 -4 -3 -2 -1
The first row of numbers gives the position of the indices 0...5 in the string;
the second row gives the corresponding negative indices. The slice from *i* to
*j* consists of all characters between the edges labeled *i* and *j*,
respectively.
For non-negative indices, the length of a slice is the difference of the
indices, if both are within bounds. For example, the length of ``word[1:3]`` is
2.
The built-in function :func:`len` returns the length of a string::
>>> s = 'supercalifragilisticexpialidocious'
>>> len(s)
34
.. seealso::
:ref:`typesseq`
Strings, and the Unicode strings described in the next section, are
examples of *sequence types*, and support the common operations supported
by such types.
:ref:`string-methods`
Both strings and Unicode strings support a large number of methods for
basic transformations and searching.
:ref:`string-formatting`
The formatting operations invoked when strings and Unicode strings are the
left operand of the ``%`` operator are described in more detail here.
.. _tut-unicodestrings:
Unicode Strings
---------------
.. sectionauthor:: Marc-Andre Lemburg <mal@lemburg.com>
Starting with Python 2.0 a new data type for storing text data is available to
the programmer: the Unicode object. It can be used to store and manipulate
Unicode data (see http://www.unicode.org/) and integrates well with the existing
string objects, providing auto-conversions where necessary.
Unicode has the advantage of providing one ordinal for every character in every
script used in modern and ancient texts. Previously, there were only 256
possible ordinals for script characters. Texts were typically bound to a code
page which mapped the ordinals to script characters. This lead to very much
confusion especially with respect to internationalization (usually written as
``i18n`` --- ``'i'`` + 18 characters + ``'n'``) of software. Unicode solves
these problems by defining one code page for all scripts.
Creating Unicode strings in Python is just as simple as creating normal
strings::
>>> u'Hello World !'
u'Hello World !'
The small ``'u'`` in front of the quote indicates that a Unicode string is
supposed to be created. If you want to include special characters in the string,
you can do so by using the Python *Unicode-Escape* encoding. The following
example shows how::
>>> u'Hello\u0020World !'
u'Hello World !'
The escape sequence ``\u0020`` indicates to insert the Unicode character with
the ordinal value 0x0020 (the space character) at the given position.
Other characters are interpreted by using their respective ordinal values
directly as Unicode ordinals. If you have literal strings in the standard
Latin-1 encoding that is used in many Western countries, you will find it
convenient that the lower 256 characters of Unicode are the same as the 256
characters of Latin-1.
For experts, there is also a raw mode just like the one for normal strings. You
have to prefix the opening quote with 'ur' to have Python use the
*Raw-Unicode-Escape* encoding. It will only apply the above ``\uXXXX``
conversion if there is an uneven number of backslashes in front of the small
'u'. ::
>>> ur'Hello\u0020World !'
u'Hello World !'
>>> ur'Hello\\u0020World !'
u'Hello\\\\u0020World !'
The raw mode is most useful when you have to enter lots of backslashes, as can
be necessary in regular expressions.
Apart from these standard encodings, Python provides a whole set of other ways
of creating Unicode strings on the basis of a known encoding.
.. index:: builtin: unicode
The built-in function :func:`unicode` provides access to all registered Unicode
codecs (COders and DECoders). Some of the more well known encodings which these
codecs can convert are *Latin-1*, *ASCII*, *UTF-8*, and *UTF-16*. The latter two
are variable-length encodings that store each Unicode character in one or more
bytes. The default encoding is normally set to ASCII, which passes through
characters in the range 0 to 127 and rejects any other characters with an error.
When a Unicode string is printed, written to a file, or converted with
:func:`str`, conversion takes place using this default encoding. ::
>>> u"abc"
u'abc'
>>> str(u"abc")
'abc'
>>> u"äöü"
u'\xe4\xf6\xfc'
>>> str(u"äöü")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-2: ordinal not in range(128)
To convert a Unicode string into an 8-bit string using a specific encoding,
Unicode objects provide an :func:`encode` method that takes one argument, the
name of the encoding. Lowercase names for encodings are preferred. ::
>>> u"äöü".encode('utf-8')
'\xc3\xa4\xc3\xb6\xc3\xbc'
If you have data in a specific encoding and want to produce a corresponding
Unicode string from it, you can use the :func:`unicode` function with the
encoding name as the second argument. ::
>>> unicode('\xc3\xa4\xc3\xb6\xc3\xbc', 'utf-8')
u'\xe4\xf6\xfc'
.. _tut-lists:
Lists
-----
Python knows a number of *compound* data types, used to group together other
values. The most versatile is the *list*, which can be written as a list of
comma-separated values (items) between square brackets. List items need not all
have the same type. ::
>>> a = ['spam', 'eggs', 100, 1234]
>>> a
['spam', 'eggs', 100, 1234]
Like string indices, list indices start at 0, and lists can be sliced,
concatenated and so on::
>>> a[0]
'spam'
>>> a[3]
1234
>>> a[-2]
100
>>> a[1:-1]
['eggs', 100]
>>> a[:2] + ['bacon', 2*2]
['spam', 'eggs', 'bacon', 4]
>>> 3*a[:3] + ['Boo!']
['spam', 'eggs', 100, 'spam', 'eggs', 100, 'spam', 'eggs', 100, 'Boo!']
Unlike strings, which are *immutable*, it is possible to change individual
elements of a list::
>>> a
['spam', 'eggs', 100, 1234]
>>> a[2] = a[2] + 23
>>> a
['spam', 'eggs', 123, 1234]
Assignment to slices is also possible, and this can even change the size of the
list or clear it entirely::
>>> # Replace some items:
... a[0:2] = [1, 12]
>>> a
[1, 12, 123, 1234]
>>> # Remove some:
... a[0:2] = []
>>> a
[123, 1234]
>>> # Insert some:
... a[1:1] = ['bletch', 'xyzzy']
>>> a
[123, 'bletch', 'xyzzy', 1234]
>>> # Insert (a copy of) itself at the beginning
>>> a[:0] = a
>>> a
[123, 'bletch', 'xyzzy', 1234, 123, 'bletch', 'xyzzy', 1234]
>>> # Clear the list: replace all items with an empty list
>>> a[:] = []
>>> a
[]
The built-in function :func:`len` also applies to lists::
>>> len(a)
8
It is possible to nest lists (create lists containing other lists), for
example::
>>> q = [2, 3]
>>> p = [1, q, 4]
>>> len(p)
3
>>> p[1]
[2, 3]
>>> p[1][0]
2
>>> p[1].append('xtra') # See section 5.1
>>> p
[1, [2, 3, 'xtra'], 4]
>>> q
[2, 3, 'xtra']
Note that in the last example, ``p[1]`` and ``q`` really refer to the same
object! We'll come back to *object semantics* later.
.. _tut-firststeps:
First Steps Towards Programming
===============================
Of course, we can use Python for more complicated tasks than adding two and two
together. For instance, we can write an initial sub-sequence of the *Fibonacci*
series as follows::
>>> # Fibonacci series:
... # the sum of two elements defines the next
... a, b = 0, 1
>>> while b < 10:
... print b
... a, b = b, a+b
...
1
1
2
3
5
8
This example introduces several new features.
* The first line contains a *multiple assignment*: the variables ``a`` and ``b``
simultaneously get the new values 0 and 1. On the last line this is used again,
demonstrating that the expressions on the right-hand side are all evaluated
first before any of the assignments take place. The right-hand side expressions
are evaluated from the left to the right.
* The :keyword:`while` loop executes as long as the condition (here: ``b < 10``)
remains true. In Python, like in C, any non-zero integer value is true; zero is
false. The condition may also be a string or list value, in fact any sequence;
anything with a non-zero length is true, empty sequences are false. The test
used in the example is a simple comparison. The standard comparison operators
are written the same as in C: ``<`` (less than), ``>`` (greater than), ``==``
(equal to), ``<=`` (less than or equal to), ``>=`` (greater than or equal to)
and ``!=`` (not equal to).
* The *body* of the loop is *indented*: indentation is Python's way of grouping
statements. Python does not (yet!) provide an intelligent input line editing
facility, so you have to type a tab or space(s) for each indented line. In
practice you will prepare more complicated input for Python with a text editor;
most text editors have an auto-indent facility. When a compound statement is
entered interactively, it must be followed by a blank line to indicate
completion (since the parser cannot guess when you have typed the last line).
Note that each line within a basic block must be indented by the same amount.
* The :keyword:`print` statement writes the value of the expression(s) it is
given. It differs from just writing the expression you want to write (as we did
earlier in the calculator examples) in the way it handles multiple expressions
and strings. Strings are printed without quotes, and a space is inserted
between items, so you can format things nicely, like this::
>>> i = 256*256
>>> print 'The value of i is', i
The value of i is 65536
A trailing comma avoids the newline after the output::
>>> a, b = 0, 1
>>> while b < 1000:
... print b,
... a, b = b, a+b
...
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
Note that the interpreter inserts a newline before it prints the next prompt if
the last line was not completed.

551
Doc/tutorial/modules.rst Normal file
View file

@ -0,0 +1,551 @@
.. _tut-modules:
*******
Modules
*******
If you quit from the Python interpreter and enter it again, the definitions you
have made (functions and variables) are lost. Therefore, if you want to write a
somewhat longer program, you are better off using a text editor to prepare the
input for the interpreter and running it with that file as input instead. This
is known as creating a *script*. As your program gets longer, you may want to
split it into several files for easier maintenance. You may also want to use a
handy function that you've written in several programs without copying its
definition into each program.
To support this, Python has a way to put definitions in a file and use them in a
script or in an interactive instance of the interpreter. Such a file is called a
*module*; definitions from a module can be *imported* into other modules or into
the *main* module (the collection of variables that you have access to in a
script executed at the top level and in calculator mode).
A module is a file containing Python definitions and statements. The file name
is the module name with the suffix :file:`.py` appended. Within a module, the
module's name (as a string) is available as the value of the global variable
``__name__``. For instance, use your favorite text editor to create a file
called :file:`fibo.py` in the current directory with the following contents::
# Fibonacci numbers module
def fib(n): # write Fibonacci series up to n
a, b = 0, 1
while b < n:
print b,
a, b = b, a+b
def fib2(n): # return Fibonacci series up to n
result = []
a, b = 0, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
Now enter the Python interpreter and import this module with the following
command::
>>> import fibo
This does not enter the names of the functions defined in ``fibo`` directly in
the current symbol table; it only enters the module name ``fibo`` there. Using
the module name you can access the functions::
>>> fibo.fib(1000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
>>> fibo.fib2(100)
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
>>> fibo.__name__
'fibo'
If you intend to use a function often you can assign it to a local name::
>>> fib = fibo.fib
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
.. _tut-moremodules:
More on Modules
===============
A module can contain executable statements as well as function definitions.
These statements are intended to initialize the module. They are executed only
the *first* time the module is imported somewhere. [#]_
Each module has its own private symbol table, which is used as the global symbol
table by all functions defined in the module. Thus, the author of a module can
use global variables in the module without worrying about accidental clashes
with a user's global variables. On the other hand, if you know what you are
doing you can touch a module's global variables with the same notation used to
refer to its functions, ``modname.itemname``.
Modules can import other modules. It is customary but not required to place all
:keyword:`import` statements at the beginning of a module (or script, for that
matter). The imported module names are placed in the importing module's global
symbol table.
There is a variant of the :keyword:`import` statement that imports names from a
module directly into the importing module's symbol table. For example::
>>> from fibo import fib, fib2
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This does not introduce the module name from which the imports are taken in the
local symbol table (so in the example, ``fibo`` is not defined).
There is even a variant to import all names that a module defines::
>>> from fibo import *
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This imports all names except those beginning with an underscore (``_``).
.. _tut-modulesasscripts:
Executing modules as scripts
----------------------------
When you run a Python module with ::
python fibo.py <arguments>
the code in the module will be executed, just as if you imported it, but with
the ``__name__`` set to ``"__main__"``. That means that by adding this code at
the end of your module::
if __name__ == "__main__":
import sys
fib(int(sys.argv[1]))
you can make the file usable as a script as well as an importable module,
because the code that parses the command line only runs if the module is
executed as the "main" file::
$ python fibo.py 50
1 1 2 3 5 8 13 21 34
If the module is imported, the code is not run::
>>> import fibo
>>>
This is often used either to provide a convenient user interface to a module, or
for testing purposes (running the module as a script executes a test suite).
.. _tut-searchpath:
The Module Search Path
----------------------
.. index:: triple: module; search; path
When a module named :mod:`spam` is imported, the interpreter searches for a file
named :file:`spam.py` in the current directory, and then in the list of
directories specified by the environment variable :envvar:`PYTHONPATH`. This
has the same syntax as the shell variable :envvar:`PATH`, that is, a list of
directory names. When :envvar:`PYTHONPATH` is not set, or when the file is not
found there, the search continues in an installation-dependent default path; on
Unix, this is usually :file:`.:/usr/local/lib/python`.
Actually, modules are searched in the list of directories given by the variable
``sys.path`` which is initialized from the directory containing the input script
(or the current directory), :envvar:`PYTHONPATH` and the installation- dependent
default. This allows Python programs that know what they're doing to modify or
replace the module search path. Note that because the directory containing the
script being run is on the search path, it is important that the script not have
the same name as a standard module, or Python will attempt to load the script as
a module when that module is imported. This will generally be an error. See
section :ref:`tut-standardmodules` for more information.
"Compiled" Python files
-----------------------
As an important speed-up of the start-up time for short programs that use a lot
of standard modules, if a file called :file:`spam.pyc` exists in the directory
where :file:`spam.py` is found, this is assumed to contain an
already-"byte-compiled" version of the module :mod:`spam`. The modification time
of the version of :file:`spam.py` used to create :file:`spam.pyc` is recorded in
:file:`spam.pyc`, and the :file:`.pyc` file is ignored if these don't match.
Normally, you don't need to do anything to create the :file:`spam.pyc` file.
Whenever :file:`spam.py` is successfully compiled, an attempt is made to write
the compiled version to :file:`spam.pyc`. It is not an error if this attempt
fails; if for any reason the file is not written completely, the resulting
:file:`spam.pyc` file will be recognized as invalid and thus ignored later. The
contents of the :file:`spam.pyc` file are platform independent, so a Python
module directory can be shared by machines of different architectures.
Some tips for experts:
* When the Python interpreter is invoked with the :option:`-O` flag, optimized
code is generated and stored in :file:`.pyo` files. The optimizer currently
doesn't help much; it only removes :keyword:`assert` statements. When
:option:`-O` is used, *all* bytecode is optimized; ``.pyc`` files are ignored
and ``.py`` files are compiled to optimized bytecode.
* Passing two :option:`-O` flags to the Python interpreter (:option:`-OO`) will
cause the bytecode compiler to perform optimizations that could in some rare
cases result in malfunctioning programs. Currently only ``__doc__`` strings are
removed from the bytecode, resulting in more compact :file:`.pyo` files. Since
some programs may rely on having these available, you should only use this
option if you know what you're doing.
* A program doesn't run any faster when it is read from a :file:`.pyc` or
:file:`.pyo` file than when it is read from a :file:`.py` file; the only thing
that's faster about :file:`.pyc` or :file:`.pyo` files is the speed with which
they are loaded.
* When a script is run by giving its name on the command line, the bytecode for
the script is never written to a :file:`.pyc` or :file:`.pyo` file. Thus, the
startup time of a script may be reduced by moving most of its code to a module
and having a small bootstrap script that imports that module. It is also
possible to name a :file:`.pyc` or :file:`.pyo` file directly on the command
line.
* It is possible to have a file called :file:`spam.pyc` (or :file:`spam.pyo`
when :option:`-O` is used) without a file :file:`spam.py` for the same module.
This can be used to distribute a library of Python code in a form that is
moderately hard to reverse engineer.
.. index:: module: compileall
* The module :mod:`compileall` can create :file:`.pyc` files (or :file:`.pyo`
files when :option:`-O` is used) for all modules in a directory.
.. %
.. _tut-standardmodules:
Standard Modules
================
.. index:: module: sys
Python comes with a library of standard modules, described in a separate
document, the Python Library Reference ("Library Reference" hereafter). Some
modules are built into the interpreter; these provide access to operations that
are not part of the core of the language but are nevertheless built in, either
for efficiency or to provide access to operating system primitives such as
system calls. The set of such modules is a configuration option which also
depends on the underlying platform For example, the :mod:`winreg` module is only
provided on Windows systems. One particular module deserves some attention:
:mod:`sys`, which is built into every Python interpreter. The variables
``sys.ps1`` and ``sys.ps2`` define the strings used as primary and secondary
prompts:
.. %
::
>>> import sys
>>> sys.ps1
'>>> '
>>> sys.ps2
'... '
>>> sys.ps1 = 'C> '
C> print 'Yuck!'
Yuck!
C>
These two variables are only defined if the interpreter is in interactive mode.
The variable ``sys.path`` is a list of strings that determines the interpreter's
search path for modules. It is initialized to a default path taken from the
environment variable :envvar:`PYTHONPATH`, or from a built-in default if
:envvar:`PYTHONPATH` is not set. You can modify it using standard list
operations::
>>> import sys
>>> sys.path.append('/ufs/guido/lib/python')
.. _tut-dir:
The :func:`dir` Function
========================
The built-in function :func:`dir` is used to find out which names a module
defines. It returns a sorted list of strings::
>>> import fibo, sys
>>> dir(fibo)
['__name__', 'fib', 'fib2']
>>> dir(sys)
['__displayhook__', '__doc__', '__excepthook__', '__name__', '__stderr__',
'__stdin__', '__stdout__', '_getframe', 'api_version', 'argv',
'builtin_module_names', 'byteorder', 'callstats', 'copyright',
'displayhook', 'exc_info', 'excepthook',
'exec_prefix', 'executable', 'exit', 'getdefaultencoding', 'getdlopenflags',
'getrecursionlimit', 'getrefcount', 'hexversion', 'maxint', 'maxunicode',
'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache',
'platform', 'prefix', 'ps1', 'ps2', 'setcheckinterval', 'setdlopenflags',
'setprofile', 'setrecursionlimit', 'settrace', 'stderr', 'stdin', 'stdout',
'version', 'version_info', 'warnoptions']
Without arguments, :func:`dir` lists the names you have defined currently::
>>> a = [1, 2, 3, 4, 5]
>>> import fibo
>>> fib = fibo.fib
>>> dir()
['__builtins__', '__doc__', '__file__', '__name__', 'a', 'fib', 'fibo', 'sys']
Note that it lists all types of names: variables, modules, functions, etc.
.. index:: module: __builtin__
:func:`dir` does not list the names of built-in functions and variables. If you
want a list of those, they are defined in the standard module
:mod:`__builtin__`::
>>> import __builtin__
>>> dir(__builtin__)
['ArithmeticError', 'AssertionError', 'AttributeError', 'DeprecationWarning',
'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False',
'FloatingPointError', 'FutureWarning', 'IOError', 'ImportError',
'IndentationError', 'IndexError', 'KeyError', 'KeyboardInterrupt',
'LookupError', 'MemoryError', 'NameError', 'None', 'NotImplemented',
'NotImplementedError', 'OSError', 'OverflowError',
'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError',
'RuntimeWarning', 'StopIteration', 'SyntaxError',
'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', 'True',
'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
'UserWarning', 'ValueError', 'Warning', 'WindowsError',
'ZeroDivisionError', '_', '__debug__', '__doc__', '__import__',
'__name__', 'abs', 'basestring', 'bool', 'buffer',
'chr', 'classmethod', 'cmp', 'compile',
'complex', 'copyright', 'credits', 'delattr', 'dict', 'dir', 'divmod',
'enumerate', 'eval', 'exec', 'exit', 'filter', 'float',
'frozenset', 'getattr', 'globals', 'hasattr', 'hash', 'help', 'hex',
'id', 'input', 'int', 'isinstance', 'issubclass', 'iter',
'len', 'license', 'list', 'locals', 'map', 'max', 'min',
'object', 'oct', 'open', 'ord', 'pow', 'property', 'quit', 'range',
'repr', 'reversed', 'round', 'set',
'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super',
'tuple', 'type', 'vars', 'zip']
.. _tut-packages:
Packages
========
Packages are a way of structuring Python's module namespace by using "dotted
module names". For example, the module name :mod:`A.B` designates a submodule
named ``B`` in a package named ``A``. Just like the use of modules saves the
authors of different modules from having to worry about each other's global
variable names, the use of dotted module names saves the authors of multi-module
packages like NumPy or the Python Imaging Library from having to worry about
each other's module names.
Suppose you want to design a collection of modules (a "package") for the uniform
handling of sound files and sound data. There are many different sound file
formats (usually recognized by their extension, for example: :file:`.wav`,
:file:`.aiff`, :file:`.au`), so you may need to create and maintain a growing
collection of modules for the conversion between the various file formats.
There are also many different operations you might want to perform on sound data
(such as mixing, adding echo, applying an equalizer function, creating an
artificial stereo effect), so in addition you will be writing a never-ending
stream of modules to perform these operations. Here's a possible structure for
your package (expressed in terms of a hierarchical filesystem)::
sound/ Top-level package
__init__.py Initialize the sound package
formats/ Subpackage for file format conversions
__init__.py
wavread.py
wavwrite.py
aiffread.py
aiffwrite.py
auread.py
auwrite.py
...
effects/ Subpackage for sound effects
__init__.py
echo.py
surround.py
reverse.py
...
filters/ Subpackage for filters
__init__.py
equalizer.py
vocoder.py
karaoke.py
...
When importing the package, Python searches through the directories on
``sys.path`` looking for the package subdirectory.
The :file:`__init__.py` files are required to make Python treat the directories
as containing packages; this is done to prevent directories with a common name,
such as ``string``, from unintentionally hiding valid modules that occur later
on the module search path. In the simplest case, :file:`__init__.py` can just be
an empty file, but it can also execute initialization code for the package or
set the ``__all__`` variable, described later.
Users of the package can import individual modules from the package, for
example::
import sound.effects.echo
This loads the submodule :mod:`sound.effects.echo`. It must be referenced with
its full name. ::
sound.effects.echo.echofilter(input, output, delay=0.7, atten=4)
An alternative way of importing the submodule is::
from sound.effects import echo
This also loads the submodule :mod:`echo`, and makes it available without its
package prefix, so it can be used as follows::
echo.echofilter(input, output, delay=0.7, atten=4)
Yet another variation is to import the desired function or variable directly::
from sound.effects.echo import echofilter
Again, this loads the submodule :mod:`echo`, but this makes its function
:func:`echofilter` directly available::
echofilter(input, output, delay=0.7, atten=4)
Note that when using ``from package import item``, the item can be either a
submodule (or subpackage) of the package, or some other name defined in the
package, like a function, class or variable. The ``import`` statement first
tests whether the item is defined in the package; if not, it assumes it is a
module and attempts to load it. If it fails to find it, an :exc:`ImportError`
exception is raised.
Contrarily, when using syntax like ``import item.subitem.subsubitem``, each item
except for the last must be a package; the last item can be a module or a
package but can't be a class or function or variable defined in the previous
item.
.. _tut-pkg-import-star:
Importing \* From a Package
---------------------------
.. index:: single: __all__
Now what happens when the user writes ``from sound.effects import *``? Ideally,
one would hope that this somehow goes out to the filesystem, finds which
submodules are present in the package, and imports them all. Unfortunately,
this operation does not work very well on Windows platforms, where the
filesystem does not always have accurate information about the case of a
filename! On these platforms, there is no guaranteed way to know whether a file
:file:`ECHO.PY` should be imported as a module :mod:`echo`, :mod:`Echo` or
:mod:`ECHO`. (For example, Windows 95 has the annoying practice of showing all
file names with a capitalized first letter.) The DOS 8+3 filename restriction
adds another interesting problem for long module names.
.. % The \code{__all__} Attribute
The only solution is for the package author to provide an explicit index of the
package. The import statement uses the following convention: if a package's
:file:`__init__.py` code defines a list named ``__all__``, it is taken to be the
list of module names that should be imported when ``from package import *`` is
encountered. It is up to the package author to keep this list up-to-date when a
new version of the package is released. Package authors may also decide not to
support it, if they don't see a use for importing \* from their package. For
example, the file :file:`sounds/effects/__init__.py` could contain the following
code::
__all__ = ["echo", "surround", "reverse"]
This would mean that ``from sound.effects import *`` would import the three
named submodules of the :mod:`sound` package.
If ``__all__`` is not defined, the statement ``from sound.effects import *``
does *not* import all submodules from the package :mod:`sound.effects` into the
current namespace; it only ensures that the package :mod:`sound.effects` has
been imported (possibly running any initialization code in :file:`__init__.py`)
and then imports whatever names are defined in the package. This includes any
names defined (and submodules explicitly loaded) by :file:`__init__.py`. It
also includes any submodules of the package that were explicitly loaded by
previous import statements. Consider this code::
import sound.effects.echo
import sound.effects.surround
from sound.effects import *
In this example, the echo and surround modules are imported in the current
namespace because they are defined in the :mod:`sound.effects` package when the
``from...import`` statement is executed. (This also works when ``__all__`` is
defined.)
Note that in general the practice of importing ``*`` from a module or package is
frowned upon, since it often causes poorly readable code. However, it is okay to
use it to save typing in interactive sessions, and certain modules are designed
to export only names that follow certain patterns.
Remember, there is nothing wrong with using ``from Package import
specific_submodule``! In fact, this is the recommended notation unless the
importing module needs to use submodules with the same name from different
packages.
Intra-package References
------------------------
The submodules often need to refer to each other. For example, the
:mod:`surround` module might use the :mod:`echo` module. In fact, such
references are so common that the :keyword:`import` statement first looks in the
containing package before looking in the standard module search path. Thus, the
:mod:`surround` module can simply use ``import echo`` or ``from echo import
echofilter``. If the imported module is not found in the current package (the
package of which the current module is a submodule), the :keyword:`import`
statement looks for a top-level module with the given name.
When packages are structured into subpackages (as with the :mod:`sound` package
in the example), you can use absolute imports to refer to submodules of siblings
packages. For example, if the module :mod:`sound.filters.vocoder` needs to use
the :mod:`echo` module in the :mod:`sound.effects` package, it can use ``from
sound.effects import echo``.
Starting with Python 2.5, in addition to the implicit relative imports described
above, you can write explicit relative imports with the ``from module import
name`` form of import statement. These explicit relative imports use leading
dots to indicate the current and parent packages involved in the relative
import. From the :mod:`surround` module for example, you might use::
from . import echo
from .. import formats
from ..filters import equalizer
Note that both explicit and implicit relative imports are based on the name of
the current module. Since the name of the main module is always ``"__main__"``,
modules intended for use as the main module of a Python application should
always use absolute imports.
Packages in Multiple Directories
--------------------------------
Packages support one more special attribute, :attr:`__path__`. This is
initialized to be a list containing the name of the directory holding the
package's :file:`__init__.py` before the code in that file is executed. This
variable can be modified; doing so affects future searches for modules and
subpackages contained in the package.
While this feature is not often needed, it can be used to extend the set of
modules found in a package.
.. rubric:: Footnotes
.. [#] In fact function definitions are also 'statements' that are 'executed'; the
execution enters the function name in the module's global symbol table.

313
Doc/tutorial/stdlib.rst Normal file
View file

@ -0,0 +1,313 @@
.. _tut-brieftour:
**********************************
Brief Tour of the Standard Library
**********************************
.. _tut-os-interface:
Operating System Interface
==========================
The :mod:`os` module provides dozens of functions for interacting with the
operating system::
>>> import os
>>> os.system('time 0:02')
0
>>> os.getcwd() # Return the current working directory
'C:\\Python30'
>>> os.chdir('/server/accesslogs')
Be sure to use the ``import os`` style instead of ``from os import *``. This
will keep :func:`os.open` from shadowing the builtin :func:`open` function which
operates much differently.
.. index:: builtin: help
The builtin :func:`dir` and :func:`help` functions are useful as interactive
aids for working with large modules like :mod:`os`::
>>> import os
>>> dir(os)
<returns a list of all module functions>
>>> help(os)
<returns an extensive manual page created from the module's docstrings>
For daily file and directory management tasks, the :mod:`shutil` module provides
a higher level interface that is easier to use::
>>> import shutil
>>> shutil.copyfile('data.db', 'archive.db')
>>> shutil.move('/build/executables', 'installdir')
.. _tut-file-wildcards:
File Wildcards
==============
The :mod:`glob` module provides a function for making file lists from directory
wildcard searches::
>>> import glob
>>> glob.glob('*.py')
['primes.py', 'random.py', 'quote.py']
.. _tut-command-line-arguments:
Command Line Arguments
======================
Common utility scripts often need to process command line arguments. These
arguments are stored in the :mod:`sys` module's *argv* attribute as a list. For
instance the following output results from running ``python demo.py one two
three`` at the command line::
>>> import sys
>>> print sys.argv
['demo.py', 'one', 'two', 'three']
The :mod:`getopt` module processes *sys.argv* using the conventions of the Unix
:func:`getopt` function. More powerful and flexible command line processing is
provided by the :mod:`optparse` module.
.. _tut-stderr:
Error Output Redirection and Program Termination
================================================
The :mod:`sys` module also has attributes for *stdin*, *stdout*, and *stderr*.
The latter is useful for emitting warnings and error messages to make them
visible even when *stdout* has been redirected::
>>> sys.stderr.write('Warning, log file not found starting a new one\n')
Warning, log file not found starting a new one
The most direct way to terminate a script is to use ``sys.exit()``.
.. _tut-string-pattern-matching:
String Pattern Matching
=======================
The :mod:`re` module provides regular expression tools for advanced string
processing. For complex matching and manipulation, regular expressions offer
succinct, optimized solutions::
>>> import re
>>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')
['foot', 'fell', 'fastest']
>>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat')
'cat in the hat'
When only simple capabilities are needed, string methods are preferred because
they are easier to read and debug::
>>> 'tea for too'.replace('too', 'two')
'tea for two'
.. _tut-mathematics:
Mathematics
===========
The :mod:`math` module gives access to the underlying C library functions for
floating point math::
>>> import math
>>> math.cos(math.pi / 4.0)
0.70710678118654757
>>> math.log(1024, 2)
10.0
The :mod:`random` module provides tools for making random selections::
>>> import random
>>> random.choice(['apple', 'pear', 'banana'])
'apple'
>>> random.sample(range(100), 10) # sampling without replacement
[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
>>> random.random() # random float
0.17970987693706186
>>> random.randrange(6) # random integer chosen from range(6)
4
.. _tut-internet-access:
Internet Access
===============
There are a number of modules for accessing the internet and processing internet
protocols. Two of the simplest are :mod:`urllib2` for retrieving data from urls
and :mod:`smtplib` for sending mail::
>>> import urllib2
>>> for line in urllib2.urlopen('http://tycho.usno.navy.mil/cgi-bin/timer.pl'):
... if 'EST' in line or 'EDT' in line: # look for Eastern Time
... print line
<BR>Nov. 25, 09:43:32 PM EST
>>> import smtplib
>>> server = smtplib.SMTP('localhost')
>>> server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
"""To: jcaesar@example.org
From: soothsayer@example.org
Beware the Ides of March.
""")
>>> server.quit()
.. _tut-dates-and-times:
Dates and Times
===============
The :mod:`datetime` module supplies classes for manipulating dates and times in
both simple and complex ways. While date and time arithmetic is supported, the
focus of the implementation is on efficient member extraction for output
formatting and manipulation. The module also supports objects that are timezone
aware. ::
# dates are easily constructed and formatted
>>> from datetime import date
>>> now = date.today()
>>> now
datetime.date(2003, 12, 2)
>>> now.strftime("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
'12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'
# dates support calendar arithmetic
>>> birthday = date(1964, 7, 31)
>>> age = now - birthday
>>> age.days
14368
.. _tut-data-compression:
Data Compression
================
Common data archiving and compression formats are directly supported by modules
including: :mod:`zlib`, :mod:`gzip`, :mod:`bz2`, :mod:`zipfile` and
:mod:`tarfile`. ::
>>> import zlib
>>> s = 'witch which has which witches wrist watch'
>>> len(s)
41
>>> t = zlib.compress(s)
>>> len(t)
37
>>> zlib.decompress(t)
'witch which has which witches wrist watch'
>>> zlib.crc32(s)
226805979
.. _tut-performance-measurement:
Performance Measurement
=======================
Some Python users develop a deep interest in knowing the relative performance of
different approaches to the same problem. Python provides a measurement tool
that answers those questions immediately.
For example, it may be tempting to use the tuple packing and unpacking feature
instead of the traditional approach to swapping arguments. The :mod:`timeit`
module quickly demonstrates a modest performance advantage::
>>> from timeit import Timer
>>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
0.57535828626024577
>>> Timer('a,b = b,a', 'a=1; b=2').timeit()
0.54962537085770791
In contrast to :mod:`timeit`'s fine level of granularity, the :mod:`profile` and
:mod:`pstats` modules provide tools for identifying time critical sections in
larger blocks of code.
.. _tut-quality-control:
Quality Control
===============
One approach for developing high quality software is to write tests for each
function as it is developed and to run those tests frequently during the
development process.
The :mod:`doctest` module provides a tool for scanning a module and validating
tests embedded in a program's docstrings. Test construction is as simple as
cutting-and-pasting a typical call along with its results into the docstring.
This improves the documentation by providing the user with an example and it
allows the doctest module to make sure the code remains true to the
documentation::
def average(values):
"""Computes the arithmetic mean of a list of numbers.
>>> print average([20, 30, 70])
40.0
"""
return sum(values, 0.0) / len(values)
import doctest
doctest.testmod() # automatically validate the embedded tests
The :mod:`unittest` module is not as effortless as the :mod:`doctest` module,
but it allows a more comprehensive set of tests to be maintained in a separate
file::
import unittest
class TestStatisticalFunctions(unittest.TestCase):
def test_average(self):
self.assertEqual(average([20, 30, 70]), 40.0)
self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
self.assertRaises(ZeroDivisionError, average, [])
self.assertRaises(TypeError, average, 20, 30, 70)
unittest.main() # Calling from the command line invokes all tests
.. _tut-batteries-included:
Batteries Included
==================
Python has a "batteries included" philosophy. This is best seen through the
sophisticated and robust capabilities of its larger packages. For example:
* The :mod:`xmlrpclib` and :mod:`SimpleXMLRPCServer` modules make implementing
remote procedure calls into an almost trivial task. Despite the modules
names, no direct knowledge or handling of XML is needed.
* The :mod:`email` package is a library for managing email messages, including
MIME and other RFC 2822-based message documents. Unlike :mod:`smtplib` and
:mod:`poplib` which actually send and receive messages, the email package has
a complete toolset for building or decoding complex message structures
(including attachments) and for implementing internet encoding and header
protocols.
* The :mod:`xml.dom` and :mod:`xml.sax` packages provide robust support for
parsing this popular data interchange format. Likewise, the :mod:`csv` module
supports direct reads and writes in a common database format. Together, these
modules and packages greatly simplify data interchange between python
applications and other tools.
* Internationalization is supported by a number of modules including
:mod:`gettext`, :mod:`locale`, and the :mod:`codecs` package.

394
Doc/tutorial/stdlib2.rst Normal file
View file

@ -0,0 +1,394 @@
.. _tut-brieftourtwo:
*********************************************
Brief Tour of the Standard Library -- Part II
*********************************************
This second tour covers more advanced modules that support professional
programming needs. These modules rarely occur in small scripts.
.. _tut-output-formatting:
Output Formatting
=================
The :mod:`repr` module provides a version of :func:`repr` customized for
abbreviated displays of large or deeply nested containers::
>>> import repr
>>> repr.repr(set('supercalifragilisticexpialidocious'))
"set(['a', 'c', 'd', 'e', 'f', 'g', ...])"
The :mod:`pprint` module offers more sophisticated control over printing both
built-in and user defined objects in a way that is readable by the interpreter.
When the result is longer than one line, the "pretty printer" adds line breaks
and indentation to more clearly reveal data structure::
>>> import pprint
>>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',
... 'yellow'], 'blue']]]
...
>>> pprint.pprint(t, width=30)
[[[['black', 'cyan'],
'white',
['green', 'red']],
[['magenta', 'yellow'],
'blue']]]
The :mod:`textwrap` module formats paragraphs of text to fit a given screen
width::
>>> import textwrap
>>> doc = """The wrap() method is just like fill() except that it returns
... a list of strings instead of one big string with newlines to separate
... the wrapped lines."""
...
>>> print textwrap.fill(doc, width=40)
The wrap() method is just like fill()
except that it returns a list of strings
instead of one big string with newlines
to separate the wrapped lines.
The :mod:`locale` module accesses a database of culture specific data formats.
The grouping attribute of locale's format function provides a direct way of
formatting numbers with group separators::
>>> import locale
>>> locale.setlocale(locale.LC_ALL, 'English_United States.1252')
'English_United States.1252'
>>> conv = locale.localeconv() # get a mapping of conventions
>>> x = 1234567.8
>>> locale.format("%d", x, grouping=True)
'1,234,567'
>>> locale.format("%s%.*f", (conv['currency_symbol'],
... conv['frac_digits'], x), grouping=True)
'$1,234,567.80'
.. _tut-templating:
Templating
==========
The :mod:`string` module includes a versatile :class:`Template` class with a
simplified syntax suitable for editing by end-users. This allows users to
customize their applications without having to alter the application.
The format uses placeholder names formed by ``$`` with valid Python identifiers
(alphanumeric characters and underscores). Surrounding the placeholder with
braces allows it to be followed by more alphanumeric letters with no intervening
spaces. Writing ``$$`` creates a single escaped ``$``::
>>> from string import Template
>>> t = Template('${village}folk send $$10 to $cause.')
>>> t.substitute(village='Nottingham', cause='the ditch fund')
'Nottinghamfolk send $10 to the ditch fund.'
The :meth:`substitute` method raises a :exc:`KeyError` when a placeholder is not
supplied in a dictionary or a keyword argument. For mail-merge style
applications, user supplied data may be incomplete and the
:meth:`safe_substitute` method may be more appropriate --- it will leave
placeholders unchanged if data is missing::
>>> t = Template('Return the $item to $owner.')
>>> d = dict(item='unladen swallow')
>>> t.substitute(d)
Traceback (most recent call last):
. . .
KeyError: 'owner'
>>> t.safe_substitute(d)
'Return the unladen swallow to $owner.'
Template subclasses can specify a custom delimiter. For example, a batch
renaming utility for a photo browser may elect to use percent signs for
placeholders such as the current date, image sequence number, or file format::
>>> import time, os.path, sys
>>> def raw_input(prompt):
... sys.stdout.write(prompt)
... sys.stdout.flush()
... return sys.stdin.readline()
...
>>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']
>>> class BatchRename(Template):
... delimiter = '%'
>>> fmt = raw_input('Enter rename style (%d-date %n-seqnum %f-format): ')
Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f
>>> t = BatchRename(fmt)
>>> date = time.strftime('%d%b%y')
>>> for i, filename in enumerate(photofiles):
... base, ext = os.path.splitext(filename)
... newname = t.substitute(d=date, n=i, f=ext)
... print '%s --> %s' % (filename, newname)
img_1074.jpg --> Ashley_0.jpg
img_1076.jpg --> Ashley_1.jpg
img_1077.jpg --> Ashley_2.jpg
Another application for templating is separating program logic from the details
of multiple output formats. This makes it possible to substitute custom
templates for XML files, plain text reports, and HTML web reports.
.. _tut-binary-formats:
Working with Binary Data Record Layouts
=======================================
The :mod:`struct` module provides :func:`pack` and :func:`unpack` functions for
working with variable length binary record formats. The following example shows
how to loop through header information in a ZIP file (with pack codes ``"H"``
and ``"L"`` representing two and four byte unsigned numbers respectively)::
import struct
data = open('myfile.zip', 'rb').read()
start = 0
for i in range(3): # show the first 3 file headers
start += 14
fields = struct.unpack('LLLHH', data[start:start+16])
crc32, comp_size, uncomp_size, filenamesize, extra_size = fields
start += 16
filename = data[start:start+filenamesize]
start += filenamesize
extra = data[start:start+extra_size]
print filename, hex(crc32), comp_size, uncomp_size
start += extra_size + comp_size # skip to the next header
.. _tut-multi-threading:
Multi-threading
===============
Threading is a technique for decoupling tasks which are not sequentially
dependent. Threads can be used to improve the responsiveness of applications
that accept user input while other tasks run in the background. A related use
case is running I/O in parallel with computations in another thread.
The following code shows how the high level :mod:`threading` module can run
tasks in background while the main program continues to run::
import threading, zipfile
class AsyncZip(threading.Thread):
def __init__(self, infile, outfile):
threading.Thread.__init__(self)
self.infile = infile
self.outfile = outfile
def run(self):
f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
f.write(self.infile)
f.close()
print 'Finished background zip of: ', self.infile
background = AsyncZip('mydata.txt', 'myarchive.zip')
background.start()
print 'The main program continues to run in foreground.'
background.join() # Wait for the background task to finish
print 'Main program waited until background was done.'
The principal challenge of multi-threaded applications is coordinating threads
that share data or other resources. To that end, the threading module provides
a number of synchronization primitives including locks, events, condition
variables, and semaphores.
While those tools are powerful, minor design errors can result in problems that
are difficult to reproduce. So, the preferred approach to task coordination is
to concentrate all access to a resource in a single thread and then use the
:mod:`Queue` module to feed that thread with requests from other threads.
Applications using :class:`Queue` objects for inter-thread communication and
coordination are easier to design, more readable, and more reliable.
.. _tut-logging:
Logging
=======
The :mod:`logging` module offers a full featured and flexible logging system.
At its simplest, log messages are sent to a file or to ``sys.stderr``::
import logging
logging.debug('Debugging information')
logging.info('Informational message')
logging.warning('Warning:config file %s not found', 'server.conf')
logging.error('Error occurred')
logging.critical('Critical error -- shutting down')
This produces the following output::
WARNING:root:Warning:config file server.conf not found
ERROR:root:Error occurred
CRITICAL:root:Critical error -- shutting down
By default, informational and debugging messages are suppressed and the output
is sent to standard error. Other output options include routing messages
through email, datagrams, sockets, or to an HTTP Server. New filters can select
different routing based on message priority: :const:`DEBUG`, :const:`INFO`,
:const:`WARNING`, :const:`ERROR`, and :const:`CRITICAL`.
The logging system can be configured directly from Python or can be loaded from
a user editable configuration file for customized logging without altering the
application.
.. _tut-weak-references:
Weak References
===============
Python does automatic memory management (reference counting for most objects and
garbage collection to eliminate cycles). The memory is freed shortly after the
last reference to it has been eliminated.
This approach works fine for most applications but occasionally there is a need
to track objects only as long as they are being used by something else.
Unfortunately, just tracking them creates a reference that makes them permanent.
The :mod:`weakref` module provides tools for tracking objects without creating a
reference. When the object is no longer needed, it is automatically removed
from a weakref table and a callback is triggered for weakref objects. Typical
applications include caching objects that are expensive to create::
>>> import weakref, gc
>>> class A:
... def __init__(self, value):
... self.value = value
... def __repr__(self):
... return str(self.value)
...
>>> a = A(10) # create a reference
>>> d = weakref.WeakValueDictionary()
>>> d['primary'] = a # does not create a reference
>>> d['primary'] # fetch the object if it is still alive
10
>>> del a # remove the one reference
>>> gc.collect() # run garbage collection right away
0
>>> d['primary'] # entry was automatically removed
Traceback (most recent call last):
File "<pyshell#108>", line 1, in -toplevel-
d['primary'] # entry was automatically removed
File "C:/python30/lib/weakref.py", line 46, in __getitem__
o = self.data[key]()
KeyError: 'primary'
.. _tut-list-tools:
Tools for Working with Lists
============================
Many data structure needs can be met with the built-in list type. However,
sometimes there is a need for alternative implementations with different
performance trade-offs.
The :mod:`array` module provides an :class:`array()` object that is like a list
that stores only homogenous data and stores it more compactly. The following
example shows an array of numbers stored as two byte unsigned binary numbers
(typecode ``"H"``) rather than the usual 16 bytes per entry for regular lists of
python int objects::
>>> from array import array
>>> a = array('H', [4000, 10, 700, 22222])
>>> sum(a)
26932
>>> a[1:3]
array('H', [10, 700])
The :mod:`collections` module provides a :class:`deque()` object that is like a
list with faster appends and pops from the left side but slower lookups in the
middle. These objects are well suited for implementing queues and breadth first
tree searches::
>>> from collections import deque
>>> d = deque(["task1", "task2", "task3"])
>>> d.append("task4")
>>> print "Handling", d.popleft()
Handling task1
unsearched = deque([starting_node])
def breadth_first_search(unsearched):
node = unsearched.popleft()
for m in gen_moves(node):
if is_goal(m):
return m
unsearched.append(m)
In addition to alternative list implementations, the library also offers other
tools such as the :mod:`bisect` module with functions for manipulating sorted
lists::
>>> import bisect
>>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')]
>>> bisect.insort(scores, (300, 'ruby'))
>>> scores
[(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]
The :mod:`heapq` module provides functions for implementing heaps based on
regular lists. The lowest valued entry is always kept at position zero. This
is useful for applications which repeatedly access the smallest element but do
not want to run a full list sort::
>>> from heapq import heapify, heappop, heappush
>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> heapify(data) # rearrange the list into heap order
>>> heappush(data, -5) # add a new entry
>>> [heappop(data) for i in range(3)] # fetch the three smallest entries
[-5, 0, 1]
.. _tut-decimal-fp:
Decimal Floating Point Arithmetic
=================================
The :mod:`decimal` module offers a :class:`Decimal` datatype for decimal
floating point arithmetic. Compared to the built-in :class:`float`
implementation of binary floating point, the new class is especially helpful for
financial applications and other uses which require exact decimal
representation, control over precision, control over rounding to meet legal or
regulatory requirements, tracking of significant decimal places, or for
applications where the user expects the results to match calculations done by
hand.
For example, calculating a 5% tax on a 70 cent phone charge gives different
results in decimal floating point and binary floating point. The difference
becomes significant if the results are rounded to the nearest cent::
>>> from decimal import *
>>> Decimal('0.70') * Decimal('1.05')
Decimal("0.7350")
>>> .70 * 1.05
0.73499999999999999
The :class:`Decimal` result keeps a trailing zero, automatically inferring four
place significance from multiplicands with two place significance. Decimal
reproduces mathematics as done by hand and avoids issues that can arise when
binary floating point cannot exactly represent decimal quantities.
Exact representation enables the :class:`Decimal` class to perform modulo
calculations and equality tests that are unsuitable for binary floating point::
>>> Decimal('1.00') % Decimal('.10')
Decimal("0.00")
>>> 1.00 % 0.10
0.09999999999999995
>>> sum([Decimal('0.1')]*10) == Decimal('1.0')
True
>>> sum([0.1]*10) == 1.0
False
The :mod:`decimal` module provides arithmetic with as much precision as needed::
>>> getcontext().prec = 36
>>> Decimal(1) / Decimal(7)
Decimal("0.142857142857142857142857142857142857")

68
Doc/tutorial/whatnow.rst Normal file
View file

@ -0,0 +1,68 @@
.. _tut-whatnow:
*********
What Now?
*********
Reading this tutorial has probably reinforced your interest in using Python ---
you should be eager to apply Python to solving your real-world problems. Where
should you go to learn more?
This tutorial is part of Python's documentation set. Some other documents in
the set are:
* :ref:`library-index`:
You should browse through this manual, which gives complete (though terse)
reference material about types, functions, and the modules in the standard
library. The standard Python distribution includes a *lot* of additional code.
There are modules to read Unix mailboxes, retrieve documents via HTTP, generate
random numbers, parse command-line options, write CGI programs, compress data,
and many other tasks. Skimming through the Library Reference will give you an
idea of what's available.
* :ref:`install-index` explains how to install external modules written by other
Python users.
* :ref:`reference-index`: A detailed explanation of Python's syntax and
semantics. It's heavy reading, but is useful as a complete guide to the
language itself.
More Python resources:
* http://www.python.org: The major Python Web site. 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.
* http://docs.python.org: Fast access to Python's documentation.
* http://cheeseshop.python.org: The Python Package Index, nicknamed the Cheese
Shop, is an index of user-created Python modules that are available for
download. Once you begin releasing code, you can register it here so that
others can find it.
* http://aspn.activestate.com/ASPN/Python/Cookbook/: The Python Cookbook is a
sizable collection of code examples, larger modules, and useful scripts.
Particularly notable contributions are collected in a book also titled Python
Cookbook (O'Reilly & Associates, ISBN 0-596-00797-3.)
For Python-related questions and problem reports, you can post to the newsgroup
:newsgroup:`comp.lang.python`, or send them to the mailing list at
python-list@python.org. The newsgroup and mailing list are gatewayed, so
messages posted to one will automatically be forwarded to the other. There are
around 120 postings a day (with peaks up to several hundred), asking (and
answering) questions, suggesting new features, and announcing new modules.
Before posting, be sure to check the list of `Frequently Asked Questions
<http://www.python.org/doc/faq/>`_ (also called the FAQ), or look for it in the
:file:`Misc/` directory of the Python source distribution. Mailing list
archives are available at http://mail.python.org/pipermail/. The FAQ answers
many of the questions that come up again and again, and may already contain the
solution for your problem.
.. % Postings figure based on average of last six months activity as
.. % reported by www.egroups.com; Jan. 2000 - June 2000: 21272 msgs / 182
.. % days = 116.9 msgs / day and steadily increasing.