Doc/glossary.rst

.. _glossary:

********
Glossary
********

.. 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.
    
   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
      Any class which does not inherit from :class:`object`.  See
      :term:`new-style class`.
    
   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.
    
   descriptor
      Any *new-style* 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.

   extension module
      A module written in C, using Python's C API to interact with the core and
      with user code.
    
   __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 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 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`.

   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`.
    
   mutable
      Mutable objects can change their value but keep their :func:`id`.  See
      also :term:`immutable`.
    
   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 that inherits from :class:`object`.  This includes all built-in
      types like :class:`list` and :class:`dict`.  Only new-style classes can
      use Python's newer, versatile features like :attr:`__slots__`,
      descriptors, properties, :meth:`__getattribute__`, class methods, and
      static methods.

      More information can be found in :ref:`newstyle`.
    
   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.)

   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 :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__` 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.

   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.