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.. 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).

      A tool that tries to convert Python 2.x code to Python 3.x code by
      handling most of the incompatibilities 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

   abstract base class
      :ref:`abstract-base-classes` complement :term:`duck-typing` by
      providing a way to define interfaces when other techniques like
      :func:`hasattr` would be clumsy. Python comes with many built-in 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.

      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.

      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*.

      Benevolent Dictator For Life, a.k.a. `Guido van Rossum
      <>`_, Python's creator.

      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.

      A list of bytecode instructions can be found in the documentation for
      :ref:`the dis module <bytecodes>`.

      A template for creating user-defined objects. Class definitions
      normally contain method definitions which operate on instances of the

   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.

      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`` built-in 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 built-in 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`.

      The canonical implementation of the Python programming language, as
      distributed on ` <>`_.  The term "CPython"
      is used when necessary to distinguish this implementation from others
      such as Jython or IronPython.

      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)

         def f(...):

      See :ref:`the documentation for function definition <function>` for more
      about decorators.

      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`.

      An associative array, where arbitrary keys are mapped to values.  The keys
      can be any object with :meth:`__hash__` function and :meth:`__eq__`
      methods. Called a hash in Perl.

      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

      A programming style which does not look at an object's type to determine
      if it has the right interface; instead, the method or attribute is simply
      called or used ("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 :term:`abstract base class`\ es.)  Instead, it typically employs
      :func:`hasattr` tests or :term:`EAFP` programming.

      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.

      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 any other .NET language), using IronPython's
      .NET API to interact with the core and with user code.

      An object that tries to find the :term:`loader` for a module. It must
      implement a method named :meth:`find_module`. See :pep:`302` for

   floor division
      Mathematical division that rounds down to nearest integer.  The floor
      division operator is ``//``.  For example, the expression ``11 // 4``
      evaluates to ``2`` in contrast to the ``2.75`` returned by float true
      division.  Note that ``(-11) // 4`` is ``-3`` because that is ``-2.75``
      rounded *downward*. See :pep:`238`.

      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`.

      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

         >>> 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.

      .. index:: single: generator

      A function which returns an iterator.  It looks like a normal function
      except that it contains :keyword:`yield` statements for producing a series
      a values usable in a for-loop or that can be retrieved one at a time with
      the :func:`next` function. Each :keyword:`yield` temporarily suspends
      processing, remembering the location execution state (including local
      variables and pending try-statements).  When the generator resumes, it
      picks-up where it left-off (in contrast to functions which start fresh on
      every invocation).

      .. index:: single: generator expression

   generator expression
      An expression that returns an iterator.  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

      See :term:`global interpreter lock`.

   global interpreter lock
      The mechanism used by the :term:`CPython` interpreter to assure that
      only one thread executes Python :term:`bytecode` at a time.
      This simplifies the CPython implementation by making the object model
      (including critical built-in types such as :class:`dict`) implicitly
      safe against concurrent access.  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

      However, some extension modules, either standard or third-party,
      are designed so as to release the GIL when doing computationally-intensive
      tasks such as compression or hashing.  Also, the GIL is always released
      when doing I/O.

      Past efforts to create a "free-threaded" interpreter (one which locks
      shared data at a much finer granularity) have not been successful
      because performance suffered in the common single-processor case. It
      is believed that overcoming this performance issue would make the
      implementation much more complicated and therefore costlier to maintain.

      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`.

      An Integrated Development Environment for Python.  IDLE is a basic editor
      and interpreter environment which ships with the standard distribution of

      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__`.

      An object that both finds and loads a module; both a
      :term:`finder` and :term:`loader` object.

      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)``).

      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`.

      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 built-in 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`.

      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`.

   key function
      A key function or collation function is a callable that returns a value
      used for sorting or ordering.  For example, :func:`locale.strxfrm` is
      used to produce a sort key that is aware of locale specific sort

      A number of tools in Python accept key functions to control how elements
      are ordered or grouped.  They include :func:`min`, :func:`max`,
      :func:`sorted`, :meth:`list.sort`, :func:`heapq.nsmallest`,
      :func:`heapq.nlargest`, and :func:`itertools.groupby`.

      There are several ways to create a key function.  For example. the
      :meth:`str.lower` method can serve as a key function for case insensitive
      sorts.  Alternatively, an ad-hoc key function can be built from a
      :keyword:`lambda` expression such as ``lambda r: (r[0], r[2])``.  Also,
      the :mod:`operator` module provides three key function constuctors:
      :func:`~operator.attrgetter`, :func:`~operator.itemgetter`, and
      :func:`~operator.methodcaller`.  See the :ref:`Sorting HOW TO
      <sortinghowto>` for examples of how to create and use key functions.

   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`.

      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``

      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.

      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

      An object that loads a module. It must define a method named
      :meth:`load_module`. A loader is typically returned by a
      :term:`finder`. See :pep:`302` for details.

      A container object that supports arbitrary key lookups and implements the
      methods specified in the :class:`Mapping` or :class:`MutableMapping`
      :ref:`abstract base classes <abstract-base-classes>`. Examples include
      :class:`dict`, :class:`collections.defaultdict`,
      :class:`collections.OrderedDict` and :class:`collections.Counter`.

      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`.

      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 objects can change their value but keep their :func:`id`.  See
      also :term:`immutable`.

   named tuple
      Any tuple-like class 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')``.

      The place where a variable is stored.  Namespaces are implemented as
      dictionaries.  There are the local, global and built-in namespaces as well
      as nested namespaces in objects (in methods).  Namespaces support
      modularity by preventing naming conflicts.  For instance, the functions
      :func:`` and :func:`` 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`.

      Any data with state (attributes or value) and defined behavior
      (methods).  Also the ultimate base class of any :term:`new-style

   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

   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".

      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.

      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.

      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.

      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__`).

   special method
      A method that is called implicitly by Python to execute a certain
      operation on a type, such as addition.  Such methods have names starting
      and ending with double underscores.  Special methods are documented in

      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.

      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)``.

      The objects returned from :meth:`dict.viewkeys`, :meth:`dict.viewvalues`,
      and :meth:`dict.viewitems` are called dictionary views.  They are lazy
      sequences that will see changes in the underlying dictionary.  To force
      the dictionary view to become a full list use ``list(dictview)``.  See

   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.