cpython/Doc/tut/glossary.tex

\chapter{Glossary\label{glossary}}

%%% keep the entries sorted and include at least one \index{} item for each

\begin{description}

\index{...}
\item[...]{The typical Python prompt of the interactive shell when entering
code for an indented code block.}

\index{>>>}
\item[>>>]{The typical Python prompt of the interactive shell.  Often seen
for code examples that can be tried right away in the interpreter.}

\index{__slots__}
\item[__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{BDFL}
\item[BDFL]{Benevolent Dictator For Life, a.k.a. \ulink{Guido van
Rossum}{http://www.python.org/~guido/}, Python's creator.}

\index{byte code}
\item[byte code]{The internal represenatation of a Python program in the
interpreter.  The byte code is also cached in the \code{.pyc} and
{}\code{.pyo} files so that executing the same file is faster the second
time (compilation from source to byte code can be saved).  This
"intermediate language" is said to run on a "virtual machine" that calls the
subroutines corresponding to each bytecode.}

\index{classic class}
\item[classic class]{Any class which does not inherit from \class{object}.
See new-style class.}

\index{coercion}
\item[coercion]{Converting data from one type to another.  For example,
int(3.15) coerces the floating point number to the integer, 3.  Most
mathematical operations have rules for coercing their arguments to a common
type.  For instance, adding 3 + 4.5, causes the integer 3 to be coerced to
be a float (3.0) before adding to 4.5 resulting in the float 7.5.}

\index{descriptor}
\item[descriptor]{Any object that defines the methods __get__(), __set__(),
or __delete__().  When a class attribute is a descriptor, its special
binding behavior is triggered upon attribute lookup.  Normally, writing
{}\var{a.b} looks up the object \var{b} in the class dictionary for \var{a},
but if \var{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{dictionary}
\item[dictionary]{An associative array, where arbitrary keys are mapped to
values.  The use of `dict` much resembles that for `list`, but the keys can
be any object with a `__hash__` function, not just integers starting from
zero.  Called a hash in Perl.}

\index{EAFP}
\item[EAFP]{Easier to ask for forgiveness than permission.  This common
Python coding style assumes the existance 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 `try` and `except` statments.
The technique contrasts with the '''LBYL''' style that is common in many
other languages such as C.}

\index{__future__}
\item[__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 \code{11 / 4} currently evaluates to \code{2}.
If the module in which it is executed had enabled ``true division`` by
executing}

\begin{verbatim}
from __future__ import division
\end{verbatim}

the expression \code{11 / 4} would evaluate to \code{2.75}.  By actually
importing the __future__ module and evaluating its variables, you can see
when a new feature was first added to the language and when it will becode
the default:

\begin{verbatim}
>>> import __future__
>>> __future__.division
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
\end{verbatim}

\index{generator}
\item[generator]{A function that returns an iterator.  It looks like a
normal function except that the \keyword{yield} keyword is used instead of
{}\keyword{return}.  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 \function{next()} method of the returned iterator.}

\index{GIL}
\item[GIL]{See \em{global interpreter lock}.}

\index{global interpreter lock}
\item[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{IDLE}
\item[IDLE]{an Integrated Development Environment for Python.  IDLE is a
basic editor and intepreter environment that ships with the standard
distribution of Python.  Good for beginners and those on a budget, it also
serves as clear example code for those wanting to implement a moderately
sophisticated, multi-platform GUI application.}

\index{immutable}
\item[immutable]{A 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{integer division}
\item[integer division]{Mathematical division discarding any remainder.  For
example, the expression \code{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 float), the result will be coerced (see coercion) to
a common type.  For example, a integer divided by a float will result in a
float value, possibly with a decimal fraction.  Integer division can be
forced by using the \code{//} operator instead of the \code{/} operator.
See also, __future__.}

\index{interactive}
\item[interactive]{Python has an interactive interpreter which means that
you can try out things and directly see its result.  Just launch
{}\code{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 \code{help(x)}).}

\index{interpreted}
\item[interpreted]{Python is an interpreted language, opposed to a compiled
one.  This means that the source files can be run right away without first
making an executable which is then run.  Interpreted languages typicaly have
a shorter development/debug cycle than compiled ones.  See also
{}\em{interactive}.}

\index{iterable}
\item[iterable]{A container object capable of returning its members one at a
time.  Examples of iterables include all sequence types (\class{list},
{}\class{str}, \class{tuple}, etc.) and some non-sequence types like
{}\class{dict} and \class{file} and objects of any classes you define with
an \function{__iter__} or \function{__getitem__} method.  Iterables can be
used in a \keyword{for} loop and in many other places where a sequence is
needed (\function{zip}, \function{map}, ...).  When an iterable object is
passed as an argument to the builtin function \function{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
{}\function{iter()} or deal with iterator objects yourself - the \code{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{iterator}
\item[iterator]{An object representing a stream of data.  Repeated calls to
the iterator's \function{next()} method return successive items in the
stream.  When no more data is available a \exception{StopIteration}
exception is raised instead.  At this point the iterator object is exhausted
and any further calls to its \function{next()} method just raise
{}\exception{StopIteration} again.  Iterators are required to have an
{}\function{__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 list) produces a
fresh new iterator each time you pass it to the \function{iter()} function
or use it in a \function{for} loop.  Attempting this with an iterator will
just return the same exhausted iterator object from the second iteration
pass and on, making it appear like an empty container.}

\index{list comprehension}
\item[list comprehension]{A compact way to process all or a subset of elements
in a sequence and return a list with the results.  \code{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
{}\code{range(256)} are processed in that case.}

\index{mapping}
\item[mapping]{A container object (such as \class{dict}) that supports
arbitrary key lookups using the special method \function{__getitem__()}.}

\index{metaclass}
\item[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{LBYL}
\item[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 the presence of many \keyword{if}
statements.}

\index{mutable}
\item[mutable]{Mutable objects can change their value but keep their
\function{id()}.  See also immutable.}

\index{namespace}
\item[namespace]{The place where a variable is stored.  Namespaces are
implemented as dictionary.  There is the local, global and builtins
namespace and the nested namespaces in objects (in methods).  Namespaces
support modularity by preventing naming conflicts.  For instance, the
functions \function{__builtins__.open()} and \function{os.open()} are
distinguished by their namespaces.  Namespaces also aid readability and
maintainabilty by making it clear which modules implement a function.  For
instance, writing \function{random.seed()} or \function{itertools.izip()}
makes it clear that those functions are implemented by the \module{random}
and \module{itertools} modules respectively.}

\index{nested scope}
\item[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{new-style class}
\item[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
{}\var{__slots__}, descriptors, properties, \var{__getattribute__}, class
methods, and static methods.}

\index{Python3000}
\item[Python3000]{A mythical python release, allowed not to be backward
compatible, with telepathic interface.}

\index{sequence}
\item[sequence]{An iterable which supports efficient element access using
integer indices via the \function{__getitem__} and \function{__len()__}
special methods.  Some builtin sequence types are \class{list}, \class{str},
{}\class{tuple}, and \class{unicode}.  Note that \class{dict} also supports
{}\function{__getitem__} and \function{__len__}, but is considered a mapping
rather than a sequence because the lookups use arbitrary immutable keys
rather than integers.}

\index{Zen of Python}
\item[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.}

\end{description}