549 lines
27 KiB
ReStructuredText
549 lines
27 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 default Python prompt of the interactive shell. Often seen for code
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examples which can be executed interactively in the interpreter.
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``...``
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The default Python prompt of the interactive shell when entering code for
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an indented code block or within a pair of matching left and right
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delimiters (parentheses, square brackets or curly braces).
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2to3
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A tool that tries to convert Python 2.x code to Python 3.x code by
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handling most of the incompatibilites which can be detected by parsing the
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source and traversing the parse tree.
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2to3 is available in the standard library as :mod:`lib2to3`; a standalone
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entry point is provided as :file:`Tools/scripts/2to3`. See
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:ref:`2to3-reference`.
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abstract base class
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Abstract Base Classes (abbreviated ABCs) complement :term:`duck-typing` by
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providing a way to define interfaces when other techniques like
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:func:`hasattr` would be clumsy. Python comes with many built-in ABCs for
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data structures (in the :mod:`collections` module), numbers (in the
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:mod:`numbers` module), and streams (in the :mod:`io` module). You can
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create your own ABC with the :mod:`abc` module.
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argument
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A value passed to a function or method, assigned to a named local
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variable in the function body. A function or method may have both
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positional arguments and keyword arguments in its definition.
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Positional and keyword arguments may be variable-length: ``*`` accepts
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or passes (if in the function definition or call) several positional
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arguments in a list, while ``**`` does the same for keyword arguments
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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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attribute
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A value associated with an object which is referenced by name using
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dotted expressions. For example, if an object *o* has an attribute
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*a* it would be referenced as *o.a*.
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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 CPython interpreter. The bytecode is also
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cached in ``.pyc`` and ``.pyo`` files so that executing the same file is
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faster the second time (recompilation from source to bytecode can be
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avoided). This "intermediate language" is said to run on a
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:term:`virtual machine` that executes the machine code corresponding to
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each bytecode. Do note that bytecodes are not expected to work between
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different Python virtual machines, nor to be stable between Python
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releases.
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A list of bytecode instructions can be found in the documentation for
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:ref:`the dis module <bytecodes>`.
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class
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A template for creating user-defined objects. Class definitions
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normally contain method definitions which operate on instances of the
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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``. 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 built-in 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 object which 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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CPython
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The canonical implementation of the Python programming language. The
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term "CPython" is used in contexts when necessary to distinguish this
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implementation from others such as Jython or IronPython.
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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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The same concept exists for classes, but is less commonly used there. See
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the documentation for :ref:`function definitions <function>` and
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:ref:`class definitions <class>` for more about decorators.
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descriptor
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Any object which 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, using
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*a.b* to get, set or delete an attribute looks up the object named *b* in
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the class dictionary for *a*, but if *b* is a descriptor, the respective
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descriptor method gets called. Understanding descriptors is a key to a
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deep understanding of Python because they are the basis for many features
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including functions, methods, properties, class methods, static methods,
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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` closely resembles that for :class:`list`, but the keys can
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be any object with a :meth:`__hash__` function, not just integers.
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Called a hash in Perl.
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docstring
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A string literal which appears as the first expression in a class,
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function or module. While ignored when the suite is executed, it is
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recognized by the compiler and put into the :attr:`__doc__` attribute
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of the enclosing class, function or module. Since it is available via
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introspection, it is the canonical place for documentation of the
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object.
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duck-typing
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A programming style which does not look at an object's type to determine
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if it has the right interface; instead, the method or attribute is simply
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called or used ("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`. (Note, however, that duck-typing can be complemented
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with abstract base classes.) Instead, it typically employs :func:`hasattr`
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tests or :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
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common to 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,
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names, attribute access, operators or function calls which all return a
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value. In contrast to many other languages, not all language constructs
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are expressions. There are also :term:`statement`\s which cannot be used
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as expressions, such as :keyword:`if`. Assignments are also statements,
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not expressions.
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extension module
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A module written in C or C++, using Python's C API to interact with the core and
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with user code.
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finder
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An object that tries to find the :term:`loader` for a module. It must
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implement a method named :meth:`find_module`. See :pep:`302` for
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details and :class:`importlib.abc.Finder` for an
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:term:`abstract base class`.
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floor division
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Mathematical division discarding any remainder. The floor division
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operator is ``//``. For example, the expression ``11//4`` evaluates to
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``2`` in contrast to the ``2.75`` returned by float true division.
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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.
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By importing the :mod:`__future__` module and evaluating its variables,
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you can see when a new feature was first added to the language and when it
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becomes the 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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.. index:: single: generator
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generator
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A function which 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 which
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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 an iterator. 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
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executes in the :term:`CPython` :term:`virtual machine` at a time.
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This simplifies the CPython implementation by assuring that no two
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processes can access the same memory at the same time. Locking the
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entire interpreter makes it easier for the interpreter to be
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multi-threaded, at the expense of much of the parallelism afforded by
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multi-processor machines. Efforts have been made in the past to
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create a "free-threaded" interpreter (one which locks shared data at a
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much finer granularity), but so far none have been successful because
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performance suffered in the common single-processor case.
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hashable
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An object is *hashable* if it has a hash value which 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__` method). Hashable objects which
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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 no mutable
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containers (such as lists or dictionaries) are. Objects which 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 which 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 a fixed value. Immutable objects include numbers, strings and
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tuples. 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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importer
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An object that both finds and loads a module; both a
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:term:`finder` and :term:`loader` object.
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interactive
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Python has an interactive interpreter which means you can enter
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statements and expressions at the interpreter prompt, immediately
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execute them and see their results. Just launch ``python`` with no
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arguments (possibly by selecting it from your computer's main
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menu). It is a very powerful way to test out new ideas or inspect
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modules and packages (remember ``help(x)``).
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interpreted
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Python is an interpreted language, as opposed to a compiled one,
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though the distinction can be blurry because of the presence of the
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bytecode compiler. This means that source files can be run directly
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without explicitly creating an executable which is then run.
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Interpreted languages typically have a shorter development/debug cycle
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than compiled ones, though their programs generally also run more
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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 built-in 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__` (or passing it to the built-in function :func:`next`)
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method return successive items in the stream. When no more data are
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available a :exc:`StopIteration` exception is raised instead. At this
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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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which 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
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A built-in Python :term:`sequence`. Despite its name it is more akin
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to an array in other languages than to a linked list since access to
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elements are O(1).
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list comprehension
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A compact way to process all or part of the elements in a sequence and
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return a list with the results. ``result = ['{:#04x}'.format(x) for x in
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range(256) if x % 2 == 0]`` generates a list of strings containing
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even hex numbers (0x..) in the range from 0 to 255. The :keyword:`if`
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clause is optional. If omitted, all elements in ``range(256)`` are
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processed.
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loader
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An object that loads a module. It must define a method named
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:meth:`load_module`. A loader is typically returned by a
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:term:`finder`. See :pep:`302` for details and
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:class:`importlib.abc.Loader` for an :term:`abstract base class`.
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mapping
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A container object (such as :class:`dict`) which 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 which 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-like class whose indexable elements are also accessible using
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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 built-in 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:`builtins.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 by default work
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only for reference and not for assignment. Local variables both read and
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write in the innermost scope. Likewise, global variables read and write
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to the global namespace. The :keyword:`nonlocal` allows writing to outer
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scopes.
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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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object
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Any data with state (attributes or value) and defined behavior
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(methods). Also the ultimate base class of any :term:`new-style
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class`.
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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 Python 3.x release line (coined long ago when the release
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of version 3 was something in the distant future.) This is also
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abbreviated "Py3k".
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Pythonic
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An idea or piece of code which closely follows the most common idioms
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of the Python language, rather than implementing code using concepts
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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:`~sys.getrefcount` function that programmers can call to return the
|
|
reference count for a particular object.
|
|
|
|
__slots__
|
|
A declaration inside a class that saves memory by pre-declaring space for
|
|
instance attributes and eliminating instance dictionaries. Though
|
|
popular, the technique is somewhat tricky to get right and is best
|
|
reserved for rare cases where there are large numbers of instances in a
|
|
memory-critical application.
|
|
|
|
sequence
|
|
An :term:`iterable` which supports efficient element access using integer
|
|
indices via the :meth:`__getitem__` special method and defines a
|
|
:meth:`len` method that returns the length of the sequence.
|
|
Some built-in sequence types are :class:`list`, :class:`str`,
|
|
:class:`tuple`, and :class:`bytes`. Note that :class:`dict` also
|
|
supports :meth:`__getitem__` and :meth:`__len__`, but is considered a
|
|
mapping rather than a sequence because the lookups use arbitrary
|
|
:term:`immutable` keys rather than integers.
|
|
|
|
slice
|
|
An object usually containing a portion of a :term:`sequence`. A slice is
|
|
created using the subscript notation, ``[]`` with colons between numbers
|
|
when several are given, such as in ``variable_name[1:3:5]``. The bracket
|
|
(subscript) notation uses :class:`slice` objects internally.
|
|
|
|
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
|
|
:ref:`specialnames`.
|
|
|
|
statement
|
|
A statement is part of a suite (a "block" of code). A statement is either
|
|
an :term:`expression` or a one of several constructs with a keyword, such
|
|
as :keyword:`if`, :keyword:`while` or :keyword:`for`.
|
|
|
|
triple-quoted string
|
|
A string which is bound by three instances of either a quotation mark
|
|
(") or an apostrophe ('). While they don't provide any functionality
|
|
not available with single-quoted strings, they are useful for a number
|
|
of reasons. They allow you to include unescaped single and double
|
|
quotes within a string and they can span multiple lines without the
|
|
use of the continuation character, making them especially useful when
|
|
writing docstrings.
|
|
|
|
type
|
|
The type of a Python object determines what kind of object it is; every
|
|
object has a type. An object's type is accessible as its
|
|
:attr:`__class__` attribute or can be retrieved with ``type(obj)``.
|
|
|
|
view
|
|
The objects returned from :meth:`dict.keys`, :meth:`dict.values`, and
|
|
:meth:`dict.items` 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
|
|
:ref:`dict-views`.
|
|
|
|
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.
|