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.. _glossary:
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Glossary
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.. 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.
argument
A value passed to a function or method, assigned to a name local to
the body. A function or method may have both positional arguments and
keyword arguments in its definition. Positional and keyword arguments
may be variable-length: ``*`` accepts or passes (if in the function
definition or call) several positional arguments in a list, while ``**``
does the same for keyword arguments in a dictionary.
Any expression may be used within the argument list, and the evaluated
value is passed to the local variable.
BDFL
Benevolent Dictator For Life, a.k.a. `Guido van Rossum
<http://www.python.org/~guido/>`_, Python's creator.
bytecode
Python source code is compiled into bytecode, the internal representation
of a Python program in the interpreter. The bytecode is also cached in
``.pyc`` and ``.pyo`` files so that executing the same file is faster the
second time (recompilation from source to bytecode can be avoided). This
"intermediate language" is said to run on a "virtual machine" that calls
the subroutines corresponding to each bytecode.
classic class
One of the two flavors of classes in earlier Python versions. Since
Python 3.0, there are no classic classes anymore.
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.
context manager
An objects that controls the environment seen in a :keyword:`with`
statement by defining :meth:`__enter__` and :meth:`__exit__` methods.
See :pep:`343`.
decorator
A function returning another function, usually applied as a function
transformation using the ``@wrapper`` syntax. Common examples for
decorators are :func:`classmethod` and :func:`staticmethod`.
The decorator syntax is merely syntactic sugar, the following two
function definitions are semantically equivalent::
def f(...):
...
f = staticmethod(f)
@staticmethod
def f(...):
...
The same concept exists for classes, but is less commonly used there.
descriptor
An 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, using
*a.b* to get, set or delete an attribute looks up the object named *b* in
the class dictionary for *a*, but if *b* is a descriptor, the respective
descriptor 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.
For more information about descriptors' methods, see :ref:`descriptors`.
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.
expression
A piece of syntax which can be evaluated to some value. In other words,
an expression is an accumulation of expression elements like literals, names,
attribute access, operators or function calls that all return a value.
In contrast to other languages, not all language constructs are expressions,
but there are also :term:`statement`\s that cannot be used as expressions,
such as :keyword:`while` or :keyword:`if`. Assignments are also not
expressions.
extension module
A module written in C, using Python's C API to interact with the core and
with user code.
function
A series of statements which returns some value to a caller. It can also
be passed zero or more arguments which may be used in the execution of
the body. See also :term:`argument` and :term:`method`.
__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.
hashable
An object is *hashable* if it has a hash value that never changes during
its lifetime (it needs a :meth:`__hash__` method), and can be compared to
other objects (it needs an :meth:`__eq__` or :meth:`__cmp__` method).
Hashable objects that compare equal must have the same hash value.
Hashability makes an object usable as a dictionary key and a set member,
because these data structures use the hash value internally.
All of Python's immutable built-in objects are hashable, while all mutable
containers (such as lists or dictionaries) are not. Objects that are
instances of user-defined classes are hashable by default; they all
compare unequal, and their hash value is their :func:`id`.
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 the operands types are
different, one of them will be converted to the other's 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.
More information can be found in :ref:`typeiter`.
keyword argument
Arguments which are preceded with a ``variable_name=`` in the call.
The variable name designates the local name in the function to which the
value is assigned. ``**`` is used to accept or pass a dictionary of
keyword arguments. See :term:`argument`.
lambda
An anonymous inline function consisting of a single :term:`expression`
which is evaluated when the function is called. The syntax to create
a lambda function is ``lambda [arguments]: expression``
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.
More information can be found in :ref:`metaclasses`.
method
A function that is defined inside a class body. If called as an attribute
of an instance of that class, the method will get the instance object as
its first :term:`argument` (which is usually called ``self``).
See :term:`function` and :term:`nested scope`.
mutable
Mutable objects can change their value but keep their :func:`id`. See
also :term:`immutable`.
named tuple
Any tuple subclass whose indexable fields are also accessible with
named attributes (for example, :func:`time.localtime` returns a
tuple-like object where the *year* is accessible either with an
index such as ``t[0]`` or with a named attribute like ``t.tm_year``).
A named tuple can be a built-in type such as :class:`time.struct_time`,
or it can be created with a regular class definition. A full featured
named tuple can also be created with the factory function
:func:`collections.namedtuple`. The latter approach automatically
provides extra features such as a self-documenting representation like
``Employee(name='jones', title='programmer')``.
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:`builtins.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
Old name for the flavor of classes now used for all class objects. In
earlier Python versions, only new-style classes could use Python's newer,
versatile features like :attr:`__slots__`, descriptors, properties,
:meth:`__getattribute__`, class methods, and static methods.
More information can be found in :ref:`newstyle`.
positional argument
The arguments assigned to local names inside a function or method,
determined by the order in which they were given in the call. ``*`` is
used to either accept multiple positional arguments (when in the
definition), or pass several arguments as a list to a function. See
:term:`argument`.
Python 3000
Nickname for the Python 3.x release line (coined long ago when the
release of version 3 was something in the distant future.)
Pythonic
An idea or piece of code which closely follows the most common idioms of
the Python language, rather than implementing code using concepts common
in other languages. For example, a common idiom in Python is the :keyword:`for`
loop structure; other languages don't have this easy keyword, so people
use a numerical counter instead::
for i in range(len(food)):
print(food[i])
As opposed to the cleaner, Pythonic method::
for piece in food:
print(piece)
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 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.
slice
An object usually containing a portion of a :term:`sequence`. A slice is
created using the subscript notation, ``[]`` with colons between numbers
when several are given, such as in ``variable_name[1:3:5]``. The bracket
(subscript) notation uses :class:`slice` objects internally.
statement
A statement is part of a suite (a "block" of code). A statement is either
an :term:`expression` or a one of several constructs with a keyword, such
as :keyword:`if`, :keyword:`while` or :keyword:`for`.
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.