454 lines
23 KiB
ReStructuredText
454 lines
23 KiB
ReStructuredText
.. _glossary:
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********
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Glossary
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********
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.. if you add new entries, keep the alphabetical sorting!
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.. glossary::
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``>>>``
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The typical Python prompt of the interactive shell. Often seen for code
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examples that can be tried right away in the interpreter.
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``...``
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The typical Python prompt of the interactive shell when entering code for
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an indented code block.
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argument
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A value passed to a function or method, assigned to a name local to
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the body. A function or method may have both positional arguments and
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keyword arguments in its definition. Positional and keyword arguments
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may be variable-length: ``*`` accepts or passes (if in the function
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definition or call) several positional arguments in a list, while ``**``
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does the same for keyword arguments in a dictionary.
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Any expression may be used within the argument list, and the evaluated
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value is passed to the local variable.
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BDFL
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Benevolent Dictator For Life, a.k.a. `Guido van Rossum
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<http://www.python.org/~guido/>`_, Python's creator.
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bytecode
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Python source code is compiled into bytecode, the internal representation
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of a Python program in the interpreter. The bytecode is also cached in
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``.pyc`` and ``.pyo`` files so that executing the same file is faster the
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second time (recompilation from source to bytecode can be avoided). This
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"intermediate language" is said to run on a "virtual machine" that calls
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the subroutines corresponding to each bytecode.
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classic class
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Any class which does not inherit from :class:`object`. See
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:term:`new-style class`.
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coercion
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The implicit conversion of an instance of one type to another during an
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operation which involves two arguments of the same type. For example,
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``int(3.15)`` converts the floating point number to the integer ``3``, but
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in ``3+4.5``, each argument is of a different type (one int, one float),
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and both must be converted to the same type before they can be added or it
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will raise a ``TypeError``. Coercion between two operands can be
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performed with the ``coerce`` builtin function; thus, ``3+4.5`` is
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equivalent to calling ``operator.add(*coerce(3, 4.5))`` and results in
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``operator.add(3.0, 4.5)``. Without coercion, all arguments of even
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compatible types would have to be normalized to the same value by the
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programmer, e.g., ``float(3)+4.5`` rather than just ``3+4.5``.
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complex number
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An extension of the familiar real number system in which all numbers are
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expressed as a sum of a real part and an imaginary part. Imaginary
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numbers are real multiples of the imaginary unit (the square root of
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``-1``), often written ``i`` in mathematics or ``j`` in
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engineering. Python has builtin support for complex numbers, which are
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written with this latter notation; the imaginary part is written with a
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``j`` suffix, e.g., ``3+1j``. To get access to complex equivalents of the
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:mod:`math` module, use :mod:`cmath`. Use of complex numbers is a fairly
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advanced mathematical feature. If you're not aware of a need for them,
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it's almost certain you can safely ignore them.
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context manager
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An objects that controls the environment seen in a :keyword:`with`
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statement by defining :meth:`__enter__` and :meth:`__exit__` methods.
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See :pep:`343`.
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decorator
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A function returning another function, usually applied as a function
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transformation using the ``@wrapper`` syntax. Common examples for
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decorators are :func:`classmethod` and :func:`staticmethod`.
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The decorator syntax is merely syntactic sugar, the following two
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function definitions are semantically equivalent::
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def f(...):
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...
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f = staticmethod(f)
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@staticmethod
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def f(...):
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...
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descriptor
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Any *new-style* object that defines the methods :meth:`__get__`,
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:meth:`__set__`, or :meth:`__delete__`. When a class attribute is a
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descriptor, its special binding behavior is triggered upon attribute
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lookup. Normally, using *a.b* to get, set or delete an attribute looks up
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the object named *b* in the class dictionary for *a*, but if *b* is a
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descriptor, the respective descriptor method gets called. Understanding
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descriptors is a key to a deep understanding of Python because they are
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the basis for many features including functions, methods, properties,
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class methods, static methods, and reference to super classes.
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For more information about descriptors' methods, see :ref:`descriptors`.
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dictionary
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An associative array, where arbitrary keys are mapped to values. The use
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of :class:`dict` much resembles that for :class:`list`, but the keys can
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be any object with a :meth:`__hash__` function, not just integers starting
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from zero. Called a hash in Perl.
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duck-typing
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Pythonic programming style that determines an object's type by inspection
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of its method or attribute signature rather than by explicit relationship
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to some type object ("If it looks like a duck and quacks like a duck, it
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must be a duck.") By emphasizing interfaces rather than specific types,
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well-designed code improves its flexibility by allowing polymorphic
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substitution. Duck-typing avoids tests using :func:`type` or
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:func:`isinstance`. Instead, it typically employs :func:`hasattr` tests or
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:term:`EAFP` programming.
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EAFP
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Easier to ask for forgiveness than permission. This common Python coding
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style assumes the existence of valid keys or attributes and catches
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exceptions if the assumption proves false. This clean and fast style is
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characterized by the presence of many :keyword:`try` and :keyword:`except`
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statements. The technique contrasts with the :term:`LBYL` style that is
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common in many other languages such as C.
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expression
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A piece of syntax which can be evaluated to some value. In other words,
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an expression is an accumulation of expression elements like literals, names,
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attribute access, operators or function calls that all return a value.
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In contrast to other languages, not all language constructs are expressions,
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but there are also :term:`statement`\s that cannot be used as expressions,
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such as :keyword:`print` or :keyword:`if`. Assignments are also not
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expressions.
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extension module
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A module written in C, using Python's C API to interact with the core and
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with user code.
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function
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A series of statements which returns some value to a caller. It can also
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be passed zero or more arguments which may be used in the execution of
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the body. See also :term:`argument` and :term:`method`.
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__future__
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A pseudo module which programmers can use to enable new language features
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which are not compatible with the current interpreter. For example, the
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expression ``11/4`` currently evaluates to ``2``. If the module in which
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it is executed had enabled *true division* by executing::
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from __future__ import division
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the expression ``11/4`` would evaluate to ``2.75``. By importing the
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:mod:`__future__` module and evaluating its variables, you can see when a
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new feature was first added to the language and when it will become the
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default::
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>>> import __future__
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>>> __future__.division
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_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
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garbage collection
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The process of freeing memory when it is not used anymore. Python
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performs garbage collection via reference counting and a cyclic garbage
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collector that is able to detect and break reference cycles.
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generator
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A function that returns an iterator. It looks like a normal function
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except that values are returned to the caller using a :keyword:`yield`
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statement instead of a :keyword:`return` statement. Generator functions
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often contain one or more :keyword:`for` or :keyword:`while` loops that
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:keyword:`yield` elements back to the caller. The function execution is
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stopped at the :keyword:`yield` keyword (returning the result) and is
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resumed there when the next element is requested by calling the
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:meth:`next` method of the returned iterator.
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.. index:: single: generator expression
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generator expression
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An expression that returns a generator. It looks like a normal expression
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followed by a :keyword:`for` expression defining a loop variable, range,
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and an optional :keyword:`if` expression. The combined expression
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generates values for an enclosing function::
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>>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81
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285
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GIL
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See :term:`global interpreter lock`.
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global interpreter lock
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The lock used by Python threads to assure that only one thread can be run
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at a time. This simplifies Python by assuring that no two processes can
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access the same memory at the same time. Locking the entire interpreter
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makes it easier for the interpreter to be multi-threaded, at the expense
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of some parallelism on multi-processor machines. Efforts have been made
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in the past to create a "free-threaded" interpreter (one which locks
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shared data at a much finer granularity), but performance suffered in the
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common single-processor case.
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hashable
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An object is *hashable* if it has a hash value that never changes during
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its lifetime (it needs a :meth:`__hash__` method), and can be compared to
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other objects (it needs an :meth:`__eq__` or :meth:`__cmp__` method).
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Hashable objects that compare equal must have the same hash value.
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Hashability makes an object usable as a dictionary key and a set member,
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because these data structures use the hash value internally.
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All of Python's immutable built-in objects are hashable, while all mutable
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containers (such as lists or dictionaries) are not. Objects that are
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instances of user-defined classes are hashable by default; they all
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compare unequal, and their hash value is their :func:`id`.
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IDLE
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An Integrated Development Environment for Python. IDLE is a basic editor
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and interpreter environment that ships with the standard distribution of
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Python. Good for beginners, it also serves as clear example code for
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those wanting to implement a moderately sophisticated, multi-platform GUI
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application.
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immutable
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An object with fixed value. Immutable objects are numbers, strings or
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tuples (and more). Such an object cannot be altered. A new object has to
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be created if a different value has to be stored. They play an important
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role in places where a constant hash value is needed, for example as a key
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in a dictionary.
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integer division
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Mathematical division discarding any remainder. For example, the
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expression ``11/4`` currently evaluates to ``2`` in contrast to the
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``2.75`` returned by float division. Also called *floor division*.
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When dividing two integers the outcome will always be another integer
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(having the floor function applied to it). However, if one of the operands
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is another numeric type (such as a :class:`float`), the result will be
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coerced (see :term:`coercion`) to a common type. For example, an integer
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divided by a float will result in a float value, possibly with a decimal
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fraction. Integer division can be forced by using the ``//`` operator
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instead of the ``/`` operator. See also :term:`__future__`.
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interactive
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Python has an interactive interpreter which means that you can try out
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things and immediately see their results. Just launch ``python`` with no
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arguments (possibly by selecting it from your computer's main menu). It is
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a very powerful way to test out new ideas or inspect modules and packages
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(remember ``help(x)``).
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interpreted
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Python is an interpreted language, as opposed to a compiled one. This
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means that the source files can be run directly without first creating an
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executable which is then run. Interpreted languages typically have a
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shorter development/debug cycle than compiled ones, though their programs
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generally also run more slowly. See also :term:`interactive`.
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iterable
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A container object capable of returning its members one at a
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time. Examples of iterables include all sequence types (such as
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:class:`list`, :class:`str`, and :class:`tuple`) and some non-sequence
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types like :class:`dict` and :class:`file` and objects of any classes you
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define with an :meth:`__iter__` or :meth:`__getitem__` method. Iterables
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can be used in a :keyword:`for` loop and in many other places where a
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sequence is needed (:func:`zip`, :func:`map`, ...). When an iterable
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object is passed as an argument to the builtin function :func:`iter`, it
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returns an iterator for the object. This iterator is good for one pass
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over the set of values. When using iterables, it is usually not necessary
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to call :func:`iter` or deal with iterator objects yourself. The ``for``
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statement does that automatically for you, creating a temporary unnamed
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variable to hold the iterator for the duration of the loop. See also
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:term:`iterator`, :term:`sequence`, and :term:`generator`.
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iterator
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An object representing a stream of data. Repeated calls to the iterator's
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:meth:`next` method return successive items in the stream. When no more
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data is available a :exc:`StopIteration` exception is raised instead. At
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this point, the iterator object is exhausted and any further calls to its
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:meth:`next` method just raise :exc:`StopIteration` again. Iterators are
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required to have an :meth:`__iter__` method that returns the iterator
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object itself so every iterator is also iterable and may be used in most
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places where other iterables are accepted. One notable exception is code
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that attempts multiple iteration passes. A container object (such as a
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:class:`list`) produces a fresh new iterator each time you pass it to the
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:func:`iter` function or use it in a :keyword:`for` loop. Attempting this
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with an iterator will just return the same exhausted iterator object used
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in the previous iteration pass, making it appear like an empty container.
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More information can be found in :ref:`typeiter`.
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keyword argument
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Arguments which are preceded with a ``variable_name=`` in the call.
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The variable name designates the local name in the function to which the
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value is assigned. ``**`` is used to accept or pass a dictionary of
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keyword arguments. See :term:`argument`.
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lambda
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An anonymous inline function consisting of a single :term:`expression`
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which is evaluated when the function is called. The syntax to create
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a lambda function is ``lambda [arguments]: expression``
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LBYL
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Look before you leap. This coding style explicitly tests for
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pre-conditions before making calls or lookups. This style contrasts with
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the :term:`EAFP` approach and is characterized by the presence of many
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:keyword:`if` statements.
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list comprehension
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A compact way to process all or a subset of elements in a sequence and
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return a list with the results. ``result = ["0x%02x" % x for x in
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range(256) if x % 2 == 0]`` generates a list of strings containing hex
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numbers (0x..) that are even and in the range from 0 to 255. The
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:keyword:`if` clause is optional. If omitted, all elements in
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``range(256)`` are processed.
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mapping
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A container object (such as :class:`dict`) that supports arbitrary key
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lookups using the special method :meth:`__getitem__`.
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metaclass
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The class of a class. Class definitions create a class name, a class
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dictionary, and a list of base classes. The metaclass is responsible for
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taking those three arguments and creating the class. Most object oriented
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programming languages provide a default implementation. What makes Python
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special is that it is possible to create custom metaclasses. Most users
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never need this tool, but when the need arises, metaclasses can provide
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powerful, elegant solutions. They have been used for logging attribute
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access, adding thread-safety, tracking object creation, implementing
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singletons, and many other tasks.
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More information can be found in :ref:`metaclasses`.
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method
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A function that is defined inside a class body. If called as an attribute
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of an instance of that class, the method will get the instance object as
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its first :term:`argument` (which is usually called ``self``).
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See :term:`function` and :term:`nested scope`.
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mutable
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Mutable objects can change their value but keep their :func:`id`. See
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also :term:`immutable`.
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named tuple
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Any tuple subclass whose indexable fields are also accessible with
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named attributes (for example, :func:`time.localtime` returns a
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tuple-like object where the *year* is accessible either with an
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index such as ``t[0]`` or with a named attribute like ``t.tm_year``).
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A named tuple can be a built-in type such as :class:`time.struct_time`,
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or it can be created with a regular class definition. A full featured
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named tuple can also be created with the factory function
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:func:`collections.namedtuple`. The latter approach automatically
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provides extra features such as a self-documenting representation like
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``Employee(name='jones', title='programmer')``.
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namespace
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The place where a variable is stored. Namespaces are implemented as
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dictionaries. There are the local, global and builtin namespaces as well
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as nested namespaces in objects (in methods). Namespaces support
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modularity by preventing naming conflicts. For instance, the functions
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:func:`__builtin__.open` and :func:`os.open` are distinguished by their
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namespaces. Namespaces also aid readability and maintainability by making
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it clear which module implements a function. For instance, writing
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:func:`random.seed` or :func:`itertools.izip` makes it clear that those
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functions are implemented by the :mod:`random` and :mod:`itertools`
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modules respectively.
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nested scope
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The ability to refer to a variable in an enclosing definition. For
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instance, a function defined inside another function can refer to
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variables in the outer function. Note that nested scopes work only for
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reference and not for assignment which will always write to the innermost
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scope. In contrast, local variables both read and write in the innermost
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scope. Likewise, global variables read and write to the global namespace.
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new-style class
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Any class that inherits from :class:`object`. This includes all built-in
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types like :class:`list` and :class:`dict`. Only new-style classes can
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use Python's newer, versatile features like :attr:`__slots__`,
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descriptors, properties, :meth:`__getattribute__`, class methods, and
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static methods.
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More information can be found in :ref:`newstyle`.
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positional argument
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The arguments assigned to local names inside a function or method,
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determined by the order in which they were given in the call. ``*`` is
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used to either accept multiple positional arguments (when in the
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definition), or pass several arguments as a list to a function. See
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:term:`argument`.
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Python 3000
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Nickname for the next major Python version, 3.0 (coined long ago
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when the release of version 3 was something in the distant future.) This
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is also abbreviated "Py3k".
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Pythonic
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An idea or piece of code which closely follows the most common idioms of
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the Python language, rather than implementing code using concepts common
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in other languages. For example, a common idiom in Python is the :keyword:`for`
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loop structure; other languages don't have this easy keyword, so people
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use a numerical counter instead::
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for i in range(len(food)):
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print food[i]
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As opposed to the cleaner, Pythonic method::
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for piece in food:
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print piece
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reference count
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The number of places where a certain object is referenced to. When the
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reference count drops to zero, an object is deallocated. While reference
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counting is invisible on the Python code level, it is used on the
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implementation level to keep track of allocated memory.
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__slots__
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A declaration inside a :term:`new-style class` that saves memory by
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pre-declaring space for instance attributes and eliminating instance
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dictionaries. Though popular, the technique is somewhat tricky to get
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right and is best reserved for rare cases where there are large numbers of
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instances in a memory-critical application.
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sequence
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An :term:`iterable` which supports efficient element access using integer
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indices via the :meth:`__getitem__` and :meth:`__len__` special methods.
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Some built-in sequence types are :class:`list`, :class:`str`,
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:class:`tuple`, and :class:`unicode`. Note that :class:`dict` also
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supports :meth:`__getitem__` and :meth:`__len__`, but is considered a
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mapping rather than a sequence because the lookups use arbitrary
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:term:`immutable` keys rather than integers.
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slice
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An object usually containing a portion of a :term:`sequence`. A slice is
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created using the subscript notation, ``[]`` with colons between numbers
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when several are given, such as in ``variable_name[1:3:5]``. The bracket
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(subscript) notation uses :class:`slice` objects internally (or in older
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versions, :meth:`__getslice__` and :meth:`__setslice__`).
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statement
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A statement is part of a suite (a "block" of code). A statement is either
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an :term:`expression` or a one of several constructs with a keyword, such
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as :keyword:`if`, :keyword:`while` or :keyword:`print`.
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type
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The type of a Python object determines what kind of object it is; every
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object has a type. An object's type is accessible as its
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:attr:`__class__` attribute or can be retrieved with ``type(obj)``.
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Zen of Python
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Listing of Python design principles and philosophies that are helpful in
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understanding and using the language. The listing can be found by typing
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"``import this``" at the interactive prompt.
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