.. _glossary:

********

Glossary

********

.. 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.