. _glossary:
********
Glossary
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. if you add new entries, keep the alphabetical sorting!
. glossary::
``>>>``
The default Python prompt of the interactive shell. Often seen for code
examples which can be executed interactively in the interpreter.
``...``
The default Python prompt of the interactive shell when entering code for
an indented code block or within a pair of matching left and right
delimiters (parentheses, square brackets or curly braces).
2to3
A tool that tries to convert Python 2.x code to Python 3.x code by
handling most of the incompatibilites which can be detected by parsing the
source and traversing the parse tree.
2to3 is available in the standard library as :mod:`lib2to3`; a standalone
entry point is provided as :file:`Tools/scripts/2to3`. See
:ref:`2to3-reference`.
abstract base class
Abstract Base Classes (abbreviated ABCs) complement :term:`duck-typing` by
providing a way to define interfaces when other techniques like :func:`hasattr`
would be clumsy. Python comes with many builtin ABCs for data structures
(in the :mod:`collections` module), numbers (in the :mod:`numbers`
module), and streams (in the :mod:`io` module). You can create your own
ABC with the :mod:`abc` module.
argument
A value passed to a function or method, assigned to a named local
variable in the function 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.
attribute
A value associated with an object which is referenced by name using
dotted expressions. For example, if an object *o* has an attribute
*a* it would be referenced as *o.a*.
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 :term:`virtual machine`
that executes the machine code corresponding to each bytecode.
class
A template for creating user-defined objects. Class definitions
normally contain method definitions which operate on instances of the
class.
classic class
Any class which does not inherit from :class:`object`. See
:term:`new-style class`. Classic classes will be removed in Python 3.0.
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.
context manager
An object which controls the environment seen in a :keyword:`with`
statement by defining :meth:`__enter__` and :meth:`__exit__` methods.
See :pep:`343`.
CPython
The canonical implementation of the Python programming language. The
term "CPython" is used in contexts when necessary to distinguish this
implementation from others such as Jython or IronPython.
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(...):
...
descriptor
Any *new-style* object which 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` closely resembles that for :class:`list`, but the keys can
be any object with a :meth:`__hash__` function, not just integers.
Called a hash in Perl.
docstring
A string literal which appears as the first expression in a class,
function or module. While ignored when the suite is executed, it is
recognized by the compiler and put into the :attr:`__doc__` attribute
of the enclosing class, function or module. Since it is available via
introspection, it is the canonical place for documentation of the
object.
duck-typing
A pythonic programming style which 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`. (Note, however, that duck-typing can be complemented
with abstract base classes.) 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
common to 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 which all return a value.
In contrast to many other languages, not all language constructs are expressions.
There are also :term:`statement`\s which cannot be used as expressions,
such as :keyword:`print` or :keyword:`if`. Assignments are also statements,
not expressions.
extension module
A module written in C or 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 which 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 which
: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
executes in the :term:`CPython` :term:`virtual machine` at a time.
This simplifies the CPython implementation 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 much of the parallelism afforded by
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 so far none have been successful because
performance suffered in the common single-processor case.
hashable
An object is *hashable* if it has a hash value which 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 which 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 no mutable
containers (such as lists or dictionaries) are. Objects which 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 which 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 a fixed value. Immutable objects include numbers, strings and
tuples. 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 you can enter
statements and expressions at the interpreter prompt, immediately
execute them and 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,
though the distinction can be blurry because of the presence of the
bytecode compiler. This means that source files can be run directly
without explicitly 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 are 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
which 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
A built-in Python :term:`sequence`. Despite its name it is more akin
to an array in other languages than to a linked list since access to
elements are O(1).
list comprehension
A compact way to process all or part of the 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
even hex numbers (0x..) 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`) which 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 which 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 elements are also accessible using
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:`__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 which 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, and :meth:`__getattribute__`.
More information can be found in :ref:`newstyle`.
object
Any data with state (attributes or value) and defined behavior
(methods). Also the ultimate base class of any :term:`new-style
class`.
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 next major Python version, 3.0 (coined long ago
when the release of version 3 was something in the distant future.) This
is also abbreviated "Py3k".
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 to other languages. For example, a common idiom in Python is
to loop over all elements of an iterable using a :keyword:`for`
statement. Many other languages don't have this type of construct, so
people unfamiliar with Python sometimes 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 references to an object. When the reference count of an
object drops to zero, it is deallocated. Reference counting is
generally not visible to Python code, but it is a key element of the
:term:`CPython` implementation. The :mod:`sys` module defines a
:func:`getrefcount` function that programmers can call to return the
reference count for a particular object.
__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__` special method and defines a
:meth:`len` method that returns the length of the sequence.
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 (or in older
versions, :meth:`__getslice__` and :meth:`__setslice__`).
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:`print`.
triple-quoted string
A string which is bound by three instances of either a quotation mark
(") or an apostrophe ('). While they don't provide any functionality
not available with single-quoted strings, they are useful for a number
of reasons. They allow you to include unescaped single and double
quotes within a string and they can span multiple lines without the
use of the continuation character, making them especially useful when
writing docstrings.
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)``.
virtual machine
A computer defined entirely in software. Python's virtual machine
executes the :term:`bytecode` emitted by the bytecode compiler.
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.
