mirror of https://github.com/python/cpython.git
306 lines
16 KiB
ReStructuredText
306 lines
16 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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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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byte code
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The internal representation of a Python program in the interpreter. The
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byte code is also cached in ``.pyc`` and ``.pyo`` files so that executing
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the same file is faster the second time (recompilation from source to byte
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code can be avoided). This "intermediate language" is said to run on a
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"virtual machine" that calls the subroutines corresponding to each
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bytecode.
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classic class
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One of the two flavors of classes in earlier Python versions. Since
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Python 3.0, there are no classic classes anymore.
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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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descriptor
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An object that defines the methods :meth:`__get__`, :meth:`__set__`, or
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:meth:`__delete__`. When a class attribute is a descriptor, its special
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binding behavior is triggered upon attribute lookup. Normally, writing
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*a.b* looks up the object *b* in the class dictionary for *a*, but if *b*
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is a descriptor, the defined 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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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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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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__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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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*. When
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dividing two integers the outcome will always be another integer (having
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the floor function applied to it). However, if the operands types are
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different, one of them will be converted to the other's type. For
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example, an integer divided by a float will result in a float value,
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possibly with a decimal fraction. Integer division can be forced by using
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the ``//`` operator instead of the ``/`` operator. See also
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: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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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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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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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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Old name for the flavor of classes now used for all class objects. In
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earlier Python versions, only new-style classes could use Python's newer,
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versatile features like :attr:`__slots__`, descriptors, properties,
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:meth:`__getattribute__`, class methods, and static methods.
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Python 3000
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Nickname for the next major Python version, 3.0 (coined long ago when the
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release of version 3 was something in the distant future.)
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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 class that saves memory by pre-declaring space for
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instance attributes and eliminating instance dictionaries. Though
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popular, the technique is somewhat tricky to get right and is best
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reserved for rare cases where there are large numbers of instances in a
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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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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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