Siebel EIM-

Siebel Articles Python Perl 5 Documentation Manuel PHP7 français

Java Das PHP7 Handbuch PHP7 Manual English Linux and Unix News

Glossary
********

">>>"
The default Python prompt of the interactive shell. Often seen for
code examples which can be executed interactively in the
interpreter.

"..."
The default Python prompt of the interactive shell when entering
code for an indented code block or within a pair of matching left
and right delimiters (parentheses, square brackets or curly
braces).

2to3
A tool that tries to convert Python 2.x code to Python 3.x code by
handling most of the incompatibilities which can be detected by
parsing the source and traversing the parse tree.

2to3 is available in the standard library as "lib2to3"; a
standalone entry point is provided as "Tools/scripts/2to3". See
2to3 - Automated Python 2 to 3 code translation.

abstract base class
Abstract base classes complement *duck-typing* by providing a way
to define interfaces when other techniques like "hasattr()" would
be clumsy or subtly wrong (for example with magic methods). ABCs
introduce virtual subclasses, which are classes that don’t inherit
from a class but are still recognized by "isinstance()" and
"issubclass()"; see the "abc" module documentation. Python comes
with many built-in ABCs for data structures (in the
"collections.abc" module), numbers (in the "numbers" module),
streams (in the "io" module), import finders and loaders (in the
"importlib.abc" module). You can create your own ABCs with the
"abc" module.

argument
A value passed to a *function* (or *method*) when calling the
function. There are two kinds of argument:

* *keyword argument*: an argument preceded by an identifier (e.g.
"name=") in a function call or passed as a value in a dictionary
preceded by "**". For example, "3" and "5" are both keyword
arguments in the following calls to "complex()":

complex(real=3, imag=5)
complex(**{'real': 3, 'imag': 5})

* *positional argument*: an argument that is not a keyword
argument. Positional arguments can appear at the beginning of an
argument list and/or be passed as elements of an *iterable*
preceded by "*". For example, "3" and "5" are both positional
arguments in the following calls:

complex(3, 5)
complex(*(3, 5))

Arguments are assigned to the named local variables in a function
body. See the Calls section for the rules governing this
assignment. Syntactically, any expression can be used to represent
an argument; the evaluated value is assigned to the local variable.

See also the *parameter* glossary entry, the FAQ question on the
difference between arguments and parameters, and **PEP 362**.

asynchronous context manager
An object which controls the environment seen in an "async with"
statement by defining "__aenter__()" and "__aexit__()" methods.
Introduced by **PEP 492**.

asynchronous generator
A function which returns an *asynchronous generator iterator*. It
looks like a coroutine function defined with "async def" except
that it contains "yield" expressions for producing a series of
values usable in an "async for" loop.

Usually refers to a asynchronous generator function, but may refer
to an *asynchronous generator iterator* in some contexts. In cases
where the intended meaning isn’t clear, using the full terms avoids
ambiguity.

An asynchronous generator function may contain "await" expressions
as well as "async for", and "async with" statements.

asynchronous generator iterator
An object created by a *asynchronous generator* function.

This is an *asynchronous iterator* which when called using the
"__anext__()" method returns an awaitable object which will execute
that the body of the asynchronous generator function until the next
"yield" expression.

Each "yield" temporarily suspends processing, remembering the
location execution state (including local variables and pending
try-statements). When the *asynchronous generator iterator*
effectively resumes with another awaitable returned by
"__anext__()", it picks-up where it left-off. See **PEP 492** and
**PEP 525**.

asynchronous iterable
An object, that can be used in an "async for" statement. Must
return an *asynchronous iterator* from its "__aiter__()" method.
Introduced by **PEP 492**.

asynchronous iterator
An object that implements "__aiter__()" and "__anext__()" methods.
"__anext__" must return an *awaitable* object. "async for" resolves
awaitable returned from asynchronous iterator’s "__anext__()"
method until it raises "StopAsyncIteration" exception. Introduced
by **PEP 492**.

attribute
A value associated with an object which is referenced by name using
dotted expressions. For example, if an object *o* has an attribute
*a* it would be referenced as *o.a*.

awaitable
An object that can be used in an "await" expression. Can be a
*coroutine* or an object with an "__await__()" method. See also
**PEP 492**.

BDFL
Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python’s
creator.

binary file
A *file object* able to read and write *bytes-like objects*.
Examples of binary files are files opened in binary mode ("'rb'",
"'wb'" or "'rb+'"), "sys.stdin.buffer", "sys.stdout.buffer", and
instances of "io.BytesIO" and "gzip.GzipFile".

See also: A *text file* reads and writes "str" objects.

bytes-like object
An object that supports the Buffer Protocol and can export a
C-*contiguous* buffer. This includes all "bytes", "bytearray", and
"array.array" objects, as well as many common "memoryview" objects.
Bytes-like objects can be used for various operations that work
with binary data; these include compression, saving to a binary
file, and sending over a socket.

Some operations need the binary data to be mutable. The
documentation often refers to these as “read-write bytes-like
objects”. Example mutable buffer objects include "bytearray" and a
"memoryview" of a "bytearray". Other operations require the binary
data to be stored in immutable objects (“read-only bytes-like
objects”); examples of these include "bytes" and a "memoryview" of
a "bytes" object.

bytecode
Python source code is compiled into bytecode, the internal
representation of a Python program in the CPython interpreter. The
bytecode is also cached in ".pyc" files so that executing the same
file is faster the second time (recompilation from source to
bytecode can be avoided). This “intermediate language” is said to
run on a *virtual machine* that executes the machine code
corresponding to each bytecode. Do note that bytecodes are not
expected to work between different Python virtual machines, nor to
be stable between Python releases.

A list of bytecode instructions can be found in the documentation
for the dis module.

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

coercion
The implicit conversion of an instance of one type to another
during an operation which involves two arguments of the same type.
For example, "int(3.15)" converts the floating point number to the
integer "3", but in "3+4.5", each argument is of a different type
(one int, one float), and both must be converted to the same type
before they can be added or it will raise a "TypeError". Without
coercion, all arguments of even compatible types would have to be
normalized to the same value by the programmer, e.g.,
"float(3)+4.5" rather than just "3+4.5".

complex number
An extension of the familiar real number system in which all
numbers are expressed as a sum of a real part and an imaginary
part. Imaginary numbers are real multiples of the imaginary unit
(the square root of "-1"), often written "i" in mathematics or "j"
in engineering. Python has built-in support for complex numbers,
which are written with this latter notation; the imaginary part is
written with a "j" suffix, e.g., "3+1j". To get access to complex
equivalents of the "math" module, use "cmath". Use of complex
numbers is a fairly advanced mathematical feature. If you’re not
aware of a need for them, it’s almost certain you can safely ignore
them.

context manager
An object which controls the environment seen in a "with" statement
by defining "__enter__()" and "__exit__()" methods. See **PEP
343**.

contiguous
A buffer is considered contiguous exactly if it is either
*C-contiguous* or *Fortran contiguous*. Zero-dimensional buffers
are C and Fortran contiguous. In one-dimensional arrays, the items
must be laid out in memory next to each other, in order of
increasing indexes starting from zero. In multidimensional
C-contiguous arrays, the last index varies the fastest when
visiting items in order of memory address. However, in Fortran
contiguous arrays, the first index varies the fastest.

coroutine
Coroutines is a more generalized form of subroutines. Subroutines
are entered at one point and exited at another point. Coroutines
can be entered, exited, and resumed at many different points. They
can be implemented with the "async def" statement. See also **PEP
492**.

coroutine function
A function which returns a *coroutine* object. A coroutine
function may be defined with the "async def" statement, and may
contain "await", "async for", and "async with" keywords. These
were introduced by **PEP 492**.

CPython
The canonical implementation of the Python programming language, as
distributed on python.org. The term “CPython” is used when
necessary to distinguish this implementation from others such as
Jython or IronPython.

decorator
A function returning another function, usually applied as a
function transformation using the "@wrapper" syntax. Common
examples for decorators are "classmethod()" and "staticmethod()".

The decorator syntax is merely syntactic sugar, the following two
function definitions are semantically equivalent:

def f(...):
...
f = staticmethod(f)

@staticmethod
def f(...):
...

The same concept exists for classes, but is less commonly used
there. See the documentation for function definitions and class
definitions for more about decorators.

descriptor
Any object which defines the methods "__get__()", "__set__()", or
"__delete__()". When a class attribute is a descriptor, its
special binding behavior is triggered upon attribute lookup.
Normally, using *a.b* to get, set or delete an attribute looks up
the object named *b* in the class dictionary for *a*, but if *b* is
a descriptor, the respective descriptor method gets called.
Understanding descriptors is a key to a deep understanding of
Python because they are the basis for many features including
functions, methods, properties, class methods, static methods, and
reference to super classes.

For more information about descriptors’ methods, see Implementing
Descriptors.

dictionary
An associative array, where arbitrary keys are mapped to values.
The keys can be any object with "__hash__()" and "__eq__()"
methods. Called a hash in Perl.

dictionary view
The objects returned from "dict.keys()", "dict.values()", and
"dict.items()" are called dictionary views. They provide a dynamic
view on the dictionary’s entries, which means that when the
dictionary changes, the view reflects these changes. To force the
dictionary view to become a full list use "list(dictview)". See
Dictionary view objects.

docstring
A string literal which appears as the first expression in a class,
function or module. While ignored when the suite is executed, it
is recognized by the compiler and put into the "__doc__" attribute
of the enclosing class, function or module. Since it is available
via introspection, it is the canonical place for documentation of
the object.

duck-typing
A programming style which does not look at an object’s type to
determine if it has the right interface; instead, the method or
attribute is simply called or used (“If it looks like a duck and
quacks like a duck, it must be a duck.”) By emphasizing interfaces
rather than specific types, well-designed code improves its
flexibility by allowing polymorphic substitution. Duck-typing
avoids tests using "type()" or "isinstance()". (Note, however,
that duck-typing can be complemented with *abstract base classes*.)
Instead, it typically employs "hasattr()" tests or *EAFP*
programming.

EAFP
Easier to ask for forgiveness than permission. This common Python
coding style assumes the existence of valid keys or attributes and
catches exceptions if the assumption proves false. This clean and
fast style is characterized by the presence of many "try" and
"except" statements. The technique contrasts with the *LBYL* style
common to many other languages such as C.

expression
A piece of syntax which can be evaluated to some value. In other
words, an expression is an accumulation of expression elements like
literals, names, attribute access, operators or function calls
which all return a value. In contrast to many other languages, not
all language constructs are expressions. There are also
*statement*s which cannot be used as expressions, such as "if".
Assignments are also statements, not expressions.

extension module
A module written in C or C++, using Python’s C API to interact with
the core and with user code.

f-string
String literals prefixed with "'f'" or "'F'" are commonly called
“f-strings” which is short for formatted string literals. See also
**PEP 498**.

file object
An object exposing a file-oriented API (with methods such as
"read()" or "write()") to an underlying resource. Depending on the
way it was created, a file object can mediate access to a real on-
disk file or to another type of storage or communication device
(for example standard input/output, in-memory buffers, sockets,
pipes, etc.). File objects are also called *file-like objects* or
*streams*.

There are actually three categories of file objects: raw *binary
files*, buffered *binary files* and *text files*. Their interfaces
are defined in the "io" module. The canonical way to create a file
object is by using the "open()" function.

file-like object
A synonym for *file object*.

finder
An object that tries to find the *loader* for a module that is
being imported.

Since Python 3.3, there are two types of finder: *meta path
finders* for use with "sys.meta_path", and *path entry finders* for
use with "sys.path_hooks".

See **PEP 302**, **PEP 420** and **PEP 451** for much more detail.

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

function
A series of statements which returns some value to a caller. It can
also be passed zero or more *arguments* which may be used in the
execution of the body. See also *parameter*, *method*, and the
Function definitions section.

function annotation
An arbitrary metadata value associated with a function parameter or
return value. Its syntax is explained in section Function
definitions. Annotations may be accessed via the "__annotations__"
special attribute of a function object.

Python itself does not assign any particular meaning to function
annotations. They are intended to be interpreted by third-party
libraries or tools. See **PEP 3107**, which describes some of
their potential uses.

__future__
A pseudo-module which programmers can use to enable new language
features which are not compatible with the current interpreter.

By importing the "__future__" module and evaluating its variables,
you can see when a new feature was first added to the language and
when it becomes the default:

>>> import __future__
>>> __future__.division
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)

garbage collection
The process of freeing memory when it is not used anymore. Python
performs garbage collection via reference counting and a cyclic
garbage collector that is able to detect and break reference
cycles. The garbage collector can be controlled using the "gc"
module.

generator
A function which returns a *generator iterator*. It looks like a
normal function except that it contains "yield" expressions for
producing a series of values usable in a for-loop or that can be
retrieved one at a time with the "next()" function.

Usually refers to a generator function, but may refer to a
*generator iterator* in some contexts. In cases where the intended
meaning isn’t clear, using the full terms avoids ambiguity.

generator iterator
An object created by a *generator* function.

Each "yield" temporarily suspends processing, remembering the
location execution state (including local variables and pending
try-statements). When the *generator iterator* resumes, it picks-
up where it left-off (in contrast to functions which start fresh on
every invocation).

generator expression
An expression that returns an iterator. It looks like a normal
expression followed by a "for" expression defining a loop variable,
range, and an optional "if" expression. The combined expression
generates values for an enclosing function:

>>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81
285

generic function
A function composed of multiple functions implementing the same
operation for different types. Which implementation should be used
during a call is determined by the dispatch algorithm.

See also the *single dispatch* glossary entry, the
"functools.singledispatch()" decorator, and **PEP 443**.

GIL
See *global interpreter lock*.

global interpreter lock
The mechanism used by the *CPython* interpreter to assure that only
one thread executes Python *bytecode* at a time. This simplifies
the CPython implementation by making the object model (including
critical built-in types such as "dict") implicitly safe against
concurrent access. Locking the entire interpreter makes it easier
for the interpreter to be multi-threaded, at the expense of much of
the parallelism afforded by multi-processor machines.

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

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

hashable
An object is *hashable* if it has a hash value which never changes
during its lifetime (it needs a "__hash__()" method), and can be
compared to other objects (it needs an "__eq__()" method).
Hashable objects which compare equal must have the same hash value.

Hashability makes an object usable as a dictionary key and a set
member, because these data structures use the hash value
internally.

All of Python’s immutable built-in objects are hashable; mutable
containers (such as lists or dictionaries) are not. Objects which
are instances of user-defined classes are hashable by default.
They all compare unequal (except with themselves), and their hash
value is derived from their "id()".

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

immutable
An object with a fixed value. Immutable objects include numbers,
strings and tuples. Such an object cannot be altered. A new
object has to be created if a different value has to be stored.
They play an important role in places where a constant hash value
is needed, for example as a key in a dictionary.

import path
A list of locations (or *path entries*) that are searched by the
*path based finder* for modules to import. During import, this list
of locations usually comes from "sys.path", but for subpackages it
may also come from the parent package’s "__path__" attribute.

importing
The process by which Python code in one module is made available to
Python code in another module.

importer
An object that both finds and loads a module; both a *finder* and
*loader* object.

interactive
Python has an interactive interpreter which means you can enter
statements and expressions at the interpreter prompt, immediately
execute them and see their results. Just launch "python" with no
arguments (possibly by selecting it from your computer’s main
menu). It is a very powerful way to test out new ideas or inspect
modules and packages (remember "help(x)").

interpreted
Python is an interpreted language, as opposed to a compiled one,
though the distinction can be blurry because of the presence of the
bytecode compiler. This means that source files can be run
directly without explicitly creating an executable which is then
run. Interpreted languages typically have a shorter
development/debug cycle than compiled ones, though their programs
generally also run more slowly. See also *interactive*.

interpreter shutdown
When asked to shut down, the Python interpreter enters a special
phase where it gradually releases all allocated resources, such as
modules and various critical internal structures. It also makes
several calls to the *garbage collector*. This can trigger the
execution of code in user-defined destructors or weakref callbacks.
Code executed during the shutdown phase can encounter various
exceptions as the resources it relies on may not function anymore
(common examples are library modules or the warnings machinery).

The main reason for interpreter shutdown is that the "__main__"
module or the script being run has finished executing.

iterable
An object capable of returning its members one at a time. Examples
of iterables include all sequence types (such as "list", "str", and
"tuple") and some non-sequence types like "dict", *file objects*,
and objects of any classes you define with an "__iter__()" method
or with a "__getitem__()" method that implements *Sequence*
semantics.

Iterables can be used in a "for" loop and in many other places
where a sequence is needed ("zip()", "map()", …). When an iterable
object is passed as an argument to the built-in function "iter()",
it returns an iterator for the object. This iterator is good for
one pass over the set of values. When using iterables, it is
usually not necessary to call "iter()" or deal with iterator
objects yourself. The "for" statement does that automatically for
you, creating a temporary unnamed variable to hold the iterator for
the duration of the loop. See also *iterator*, *sequence*, and
*generator*.

iterator
An object representing a stream of data. Repeated calls to the
iterator’s "__next__()" method (or passing it to the built-in
function "next()") return successive items in the stream. When no
more data are available a "StopIteration" exception is raised
instead. At this point, the iterator object is exhausted and any
further calls to its "__next__()" method just raise "StopIteration"
again. Iterators are required to have an "__iter__()" method that
returns the iterator object itself so every iterator is also
iterable and may be used in most places where other iterables are
accepted. One notable exception is code which attempts multiple
iteration passes. A container object (such as a "list") produces a
fresh new iterator each time you pass it to the "iter()" function
or use it in a "for" loop. Attempting this with an iterator will
just return the same exhausted iterator object used in the previous
iteration pass, making it appear like an empty container.

More information can be found in Iterator Types.

key function
A key function or collation function is a callable that returns a
value used for sorting or ordering. For example,
"locale.strxfrm()" is used to produce a sort key that is aware of
locale specific sort conventions.

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

There are several ways to create a key function. For example. the
"str.lower()" method can serve as a key function for case
insensitive sorts. Alternatively, a key function can be built from
a "lambda" expression such as "lambda r: (r[0], r[2])". Also, the
"operator" module provides three key function constructors:
"attrgetter()", "itemgetter()", and "methodcaller()". See the
Sorting HOW TO for examples of how to create and use key functions.

keyword argument
See *argument*.

lambda
An anonymous inline function consisting of a single *expression*
which is evaluated when the function is called. The syntax to
create a lambda function is "lambda [arguments]: expression"

LBYL
Look before you leap. This coding style explicitly tests for pre-
conditions before making calls or lookups. This style contrasts
with the *EAFP* approach and is characterized by the presence of
many "if" statements.

In a multi-threaded environment, the LBYL approach can risk
introducing a race condition between “the looking” and “the
leaping”. For example, the code, "if key in mapping: return
mapping[key]" can fail if another thread removes *key* from
*mapping* after the test, but before the lookup. This issue can be
solved with locks or by using the EAFP approach.

list
A built-in Python *sequence*. Despite its name it is more akin to
an array in other languages than to a linked list since access to
elements are O(1).

list comprehension
A compact way to process all or part of the elements in a sequence
and return a list with the results. "result = ['{:#04x}'.format(x)
for x in range(256) if x % 2 == 0]" generates a list of strings
containing even hex numbers (0x..) in the range from 0 to 255. The
"if" clause is optional. If omitted, all elements in "range(256)"
are processed.

loader
An object that loads a module. It must define a method named
"load_module()". A loader is typically returned by a *finder*. See
**PEP 302** for details and "importlib.abc.Loader" for an *abstract
base class*.

mapping
A container object that supports arbitrary key lookups and
implements the methods specified in the "Mapping" or
"MutableMapping" abstract base classes. Examples include "dict",
"collections.defaultdict", "collections.OrderedDict" and
"collections.Counter".

meta path finder
A *finder* returned by a search of "sys.meta_path". Meta path
finders are related to, but different from *path entry finders*.

See "importlib.abc.MetaPathFinder" for the methods that meta path
finders implement.

metaclass
The class of a class. Class definitions create a class name, a
class dictionary, and a list of base classes. The metaclass is
responsible for taking those three arguments and creating the
class. Most object oriented programming languages provide a
default implementation. What makes Python special is that it is
possible to create custom metaclasses. Most users never need this
tool, but when the need arises, metaclasses can provide powerful,
elegant solutions. They have been used for logging attribute
access, adding thread-safety, tracking object creation,
implementing singletons, and many other tasks.

More information can be found in Metaclasses.

method
A function which is defined inside a class body. If called as an
attribute of an instance of that class, the method will get the
instance object as its first *argument* (which is usually called
"self"). See *function* and *nested scope*.

method resolution order
Method Resolution Order is the order in which base classes are
searched for a member during lookup. See The Python 2.3 Method
Resolution Order for details of the algorithm used by the Python
interpreter since the 2.3 release.

module
An object that serves as an organizational unit of Python code.
Modules have a namespace containing arbitrary Python objects.
Modules are loaded into Python by the process of *importing*.