Another merge. Only doc stuff was affected (but this aligns the UTF-32

codec changes in trubk and branch).  Hopefully the Py3k glossary wasn't
different from the trunk one.
This commit is contained in:
Guido van Rossum 2007-08-17 18:30:38 +00:00
parent eb1cf4e73b
commit f10aa9825e
6 changed files with 349 additions and 335 deletions

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@ -14,6 +14,7 @@
install/index.rst
documenting/index.rst
howto/index.rst
glossary.rst
about.rst
bugs.rst

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@ -290,6 +290,11 @@ Variable names are an exception, they should be marked simply with ``*var*``.
For all other roles, you have to write ``:rolename:`content```.
.. note::
For all cross-referencing roles, if you prefix the content with ``!``, no
reference/hyperlink will be created.
The following roles refer to objects in modules and are possibly hyperlinked if
a matching identifier is found:
@ -374,6 +379,20 @@ to objects:
The name of a grammar token (used in the reference manual to create links
between production displays).
The following role creates a cross-reference to the term in the glossary:
.. describe:: term
Reference to a term in the glossary. The glossary is created using the
``glossary`` directive containing a definition list with terms and
definitions. It does not have to be in the same file as the ``term``
markup, in fact, by default the Python docs have one global glossary
in the ``glossary.rst`` file.
If you use a term that's not explained in a glossary, you'll get a warning
during build.
---------
The following roles don't do anything special except formatting the text

320
Doc/glossary.rst Normal file
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@ -0,0 +1,320 @@
.. _glossary:
********
Glossary
********
.. if you add new entries, keep the alphabetical sorting!
.. glossary::
``>>>``
The typical Python prompt of the interactive shell. Often seen for code
examples that can be tried right away in the interpreter.
``...``
The typical Python prompt of the interactive shell when entering code for
an indented code block.
BDFL
Benevolent Dictator For Life, a.k.a. `Guido van Rossum
<http://www.python.org/~guido/>`_, Python's creator.
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.
classic class
Any class which does not inherit from :class:`object`. See
:term:`new-style class`.
coercion
The implicit conversion of an instance of one type to another during an
operation which involves two arguments of the same type. For example,
``int(3.15)`` converts the floating point number to the integer ``3``, but
in ``3+4.5``, each argument is of a different type (one int, one float),
and both must be converted to the same type before they can be added or it
will raise a ``TypeError``. Coercion between two operands can be
performed with the ``coerce`` builtin function; thus, ``3+4.5`` is
equivalent to calling ``operator.add(*coerce(3, 4.5))`` and results in
``operator.add(3.0, 4.5)``. Without coercion, all arguments of even
compatible types would have to be normalized to the same value by the
programmer, e.g., ``float(3)+4.5`` rather than just ``3+4.5``.
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.
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.
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.
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
:term:`EAFP` programming.
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 :term:`LBYL` style that is
common in many other languages such as C.
extension module
A module written in C, using Python's C API to interact with the core and
with user code.
__future__
A pseudo module which programmers can use to enable new language features
which are not compatible with the current interpreter. For example, the
expression ``11/4`` currently evaluates to ``2``. If the module in which
it is executed had enabled *true division* by executing::
from __future__ import division
the expression ``11/4`` would evaluate to ``2.75``. 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)
garbage collection
The process of freeing memory when it is not used anymore. Python
performs garbage collection via reference counting and a cyclic garbage
collector that is able to detect and break reference cycles.
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
GIL
See :term:`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.
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.
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.
integer division
Mathematical division discarding any remainder. For example, the
expression ``11/4`` currently evaluates to ``2`` in contrast to the
``2.75`` returned by float division. Also called *floor division*.
When dividing two integers the outcome will always be another integer
(having the floor function applied to it). However, if one of the operands
is another numeric type (such as a :class:`float`), the result will be
coerced (see :term:`coercion`) to a common type. For example, an integer
divided by a float will result in a float value, possibly with a decimal
fraction. Integer division can be forced by using the ``//`` operator
instead of the ``/`` operator. See also :term:`__future__`.
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)``).
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 :term:`interactive`.
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
:term:`iterator`, :term:`sequence`, and :term:`generator`.
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.
LBYL
Look before you leap. This coding style explicitly tests for
pre-conditions before making calls or lookups. This style contrasts with
the :term:`EAFP` approach and is characterized by the presence of many
:keyword:`if` statements.
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.
mapping
A container object (such as :class:`dict`) that supports arbitrary key
lookups using the special method :meth:`__getitem__`.
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.
mutable
Mutable objects can change their value but keep their :func:`id`. See
also :term:`immutable`.
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.
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.
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 :attr:`__slots__`,
descriptors, properties, :meth:`__getattribute__`, class methods, and
static methods.
Python 3000
Nickname for the next major Python version, 3.0 (coined long ago when the
release of version 3 was something in the distant future.)
reference count
The number of places where a certain object is referenced to. When the
reference count drops to zero, an object is deallocated. While reference
counting is invisible on the Python code level, it is used on the
implementation level to keep track of allocated memory.
__slots__
A declaration inside a :term:`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.
sequence
An :term:`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
:term:`immutable` keys rather than integers.
type
The type of a Python object determines what kind of object it is; every
object has a type. An object's type is accessible as its
:attr:`__class__` attribute or can be retrieved with ``type(obj)``.
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.

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@ -1,329 +0,0 @@
.. _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.

View File

@ -41,6 +41,8 @@ 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.
The :ref:`glossary` is also worth going through.
.. toctree::
appetite.rst
@ -57,4 +59,3 @@ various Python library modules described in the Python Library Reference.
whatnow.rst
interactive.rst
floatingpoint.rst
glossary.rst

View File

@ -158,13 +158,15 @@ and :mod:`smtplib` for sending mail::
>>> 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.
""")
... """To: jcaesar@example.org
... From: soothsayer@example.org
...
... Beware the Ides of March.
... """)
>>> server.quit()
(Note that the second example needs a mailserver running on localhost.)
.. _tut-dates-and-times: