Glosario Python (en construcción)

>>>

La línea de comandos por defecto de la shell interactiva. A menudo es usada para ejemplos de código que pueden ser ejecutados interactivamente en el intérprete.

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

...

La línea de comandos por defecto de la shell interactiva cuando se introduce código para una pieza de código indentado o dentro de delimitadores (paréntesis (), corchetes [] o llaves {}).

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

Es una herramienta para convertir código desde Python 2.x a Python 3.x manejando la mayoría de incompatibilidades que pueden ser detectadas analizando sintácticamente el código y cruzando el árbol del analisis sintáctico.

2to3 está disponible en la librería estándar como lib2to3; un acceso autónomo está disponible en Tools/scripts/2to3 (en el directorio raiz de tu instalación de Python). Ver 2to3 traducción automática de código Python 2 a 3.

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][2]; a standalone entry point is provided as Tools/scripts/2to3. See [_2to3 - Automated Python 2 to 3 code translation_][3].

abstract base class (clase base abstracta)

''ABCs - abstract base classes'' complementa al ''duck-typing'' proporcionando una manera de definir interfaces cuando otras técnicas como hasattr() podrían ser más ineficaces. Python viene con muchas built-in ABCs para estructuras de datos (en el módulo collections), números (en el módulo numbers ) y streams/transferencias de datos (en el módulo io). Podrías crear tu propia ABC con el módulo abc.

''ABCs - abstract base classes'' complement ''duck-typing'' by providing a way to define interfaces when other techniques like hasattr() would be clumsy. Python comes with many built-in ABCs for data structures (in the collections module), numbers (in the numbers module), and streams (in the io module). You can create your own ABC with the abc module.

argument (argumento)

Es un valor pasado a una función o método, asignado a una variable local nombrada en el cuerpo de la función. Una función o un método podría tener tanto argumentos posicionales como argumentos clave en su definición. Los argumentos posicionales y clave pueden tener longitud variable: * acepta o pasa varios argumentos posicionales en una lista, mientras que ** hace lo mismo para los argumentos clave en un diccionario. Cualquier expresión podría ser usada dentro de la lista de argumentos y el valor evaluado se pasaría a la variable local.

A value passed to a function or method, assigned to a named local variable in the function body. A function or method may have both positional arguments and keyword arguments in its definition. Positional and keyword arguments may be variable-length: * accepts or passes (if in the function definition or call) several positional arguments in a list, while ** does the same for keyword arguments in a dictionary.</p> <p>Any expression may be used within the argument list, and the evaluated value is passed to the local variable.

attribute (atributo)

Un valor asociado con un objeto el cual es referenciado por un nombre usando espresiones con un punto. Por ejemplo, si un objeto o posee un atributo a podría ser referenciado como o.a.

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.

BDFL


Benévolo Dictador Vitalicio (Benevolent Dictator For Life), que es como se conoce informalmente a Guido van Rossum, el creador de Python.

bytecode

El código fuente Python se compila a bytecode, la representación interna de un programa Python en el intérprete. El bytecode también se ''cachea'' en ficheros .pyc y .pyo para que al acceder al mismo fichero una segunda vez la ejecución sea más rápida (la recompilación desde el código fuente a bytecode se puede evitar). Este 'lenguaje intermedio' se dice que corre en una ''virtual machine'' o máquina virtual que ejecuta el código máquina correspondiente a cada bytecode.

Una lista de instrucciones bytecodese puede encontrar en el módulo ''dis''.

Python source code is compiled into bytecode, the internal representation of a Python program in the interpreter. The bytecode is also cached in .pyc and .pyo files so that executing the same file is faster the second time (recompilation from source to bytecode can be avoided). This “intermediate language” is said to run on a ''virtual machine'' that executes the machine code corresponding to each bytecode.

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

class (clase)

Es una plantilla para crear objetos definidos por el usuario. Las definiciones de clases normalmente contienen definiciones de métodos que funcionan en las instancias de la clase.

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

classic class (clases clásicas)

Cualquier clase que no hereda de object. Ver ''new-style class''. Las clases clásicas han sido eliminadas a partir de Python 3.0.

Any class which does not inherit from object. See ''new-style class''. Classic classes will be removed in Python 3.0.

coercion (coacción)

La conversión implicita de una instancia de un tipo a otro durante una operación que involucra a dos argumentos del mismo tipo. Por ejemplo, int(3.15) convierte el número decimal 3.15 al entero 3, pero en 3 + 4.5, cada argumento es de un tipo diferente (uno es entero y el otro es decimal) y ambos deben ser convertidos al mismo tipo antes de que puedan ser sumados o tendremos un TypeError. La Coercion (coacción) entre los dos operandos puede ser ejecutada con la función built-in coerce; de esta forma, 3 + 4.5 es equivalente a usar operator.add(*coerce(3 + 4.5)) y a los resultados en operator.add(3, 4.5). Sin la Coercion (coacción), todos los argumentos, incluso para tipos compatibles, deberían ser normalizados al mismo typo por el programador, e.g., float(3) + 4.5 en lugar de solo usar 3 + 4.5.

The implicit conversion of an instance of one type to another during an operation which involves two arguments of the same type. For example, int(3.15) converts the floating point number to the integer 3, but in 3+4.5, each argument is of a different type (one int, one float), and both must be converted to the same type before they can be added or it will raise a TypeError. Coercion between two operands can be performed with the coerce built-in function; thus, 3+4.5 is equivalent to calling operator.add(*coerce(3, 4.5)) and results in operator.add(3.0, 4.5). Without coercion, all arguments of even compatible types would have to be normalized to the same value by the programmer, e.g., float(3)+4.5 rather than just 3+4.5.

complex number (número complejo)

Es una extensión del sistema de números reales donde los números se expresan como la suma de una parte real y una parte imaginaria. Los números imaginarios son múltiples reales de la unidad imaginaria (la raiz cuadrada de -1), a menudo escrita i en matemáticas o j en ingeniería. Python tiene soporte de serie para los números complejos, los cuales son escritos con la notación usada en ingeniería; la parte imaginaria se escribe con el sufijo j, e.g., 3+1j. Para acceder a los equivalentes complejos del módulo math puedes usar la librería cmath. El uso de números complejos es una característica matemática bastante avanzada. Si no eres consciente de necesitarlos, es casi seguro que los podrás ignorar.

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 [http://www.python.org/dev/peps/pep-0343 PEP 343].

CPython


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

decorator


<dd><p>A function returning another function, usually applied as a function transformation using the @wrapper syntax. Common examples for decorators are classmethod() and staticmethod().</p> <p>The decorator syntax is merely syntactic sugar, the following two function definitions are semantically equivalent:</p> <pre>def f(...):

f = staticmethod(f)

@staticmethod def f(...):

<p>See ''the documentation for function definition'' for more about decorators.</p></dd>

descriptor


<dd><p>Any new-style 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.</p> <p>For more information about descriptors’ methods, see ''Implementing Descriptors''.</p></dd>

dictionary


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

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 class''es.) 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 print or 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.
finder


An object that tries to find the ''loader'' for a module. It must implement a method named find_module(). See [http://www.python.org/dev/peps/pep-0302 PEP 302] for details.

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 [http://www.python.org/dev/peps/pep-0238 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 ''argument'' and ''method''.

__future__


<dd><p>A pseudo-module which programmers can use to enable new language features which are not compatible with the current interpreter. For example, the expression 11/4 currently evaluates to 2. If the module in which it is executed had enabled true division by executing:</p> <pre>from future import division </pre> <p>the expression 11/4 would evaluate to 2.75. 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 will become the default:</p> <pre>>>> import future

>>> future.division _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)</pre></dd>

garbage collection


The process of freeing memory when it is not used anymore. Python performs garbage collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles.
generator


A function which returns an iterator. It looks like a normal function except that it contains yield statements for producing a series a values usable in a for-loop or that can be retrieved one at a time with the next() function. Each yield temporarily suspends processing, remembering the location execution state (including local variables and pending try-statements). When the generator resumes, it picks-up where it left-off (in contrast to functions which start fresh on every invocation).

generator expression


<dd><p>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:</p> <pre>>>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81

285</pre></dd>

GIL


See ''global interpreter lock''.

global interpreter lock


<dd><p>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.</p> <p>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.</p> <p>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.</p></dd>

hashable


<dd><p>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__() or __cmp__() method). Hashable objects which compare equal must have the same hash value.</p> <p>Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally.</p> <p>All of Python’s immutable built-in objects are hashable, while no mutable containers (such as lists or dictionaries) are. Objects which are instances of user-defined classes are hashable by default; they all compare unequal, and their hash value is their id().</p></dd>

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.
integer division


Mathematical division discarding any remainder. For example, the expression 11/4 currently evaluates to 2 in contrast to the 2.75 returned by float division. Also called floor division. When dividing two integers the outcome will always be another integer (having the floor function applied to it). However, if one of the operands is another numeric type (such as a float), the result will be coerced (see ''coercion'') to a common type. For example, an integer divided by a float will result in a float value, possibly with a decimal fraction. Integer division can be forced by using the // operator instead of the / operator. See also ''__future__''.

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''.

iterable


A container 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 and file and objects of any classes you define with an __iter__() or __getitem__() method. 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


<dd><p>An object representing a stream of data. Repeated calls to the iterator’s next() method 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.</p> <p>More information can be found in ''Iterator Types''.</p></dd>

key function


<dd><p>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.</p> <p>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.nsmallest(), heapq.nlargest(), and itertools.groupby().</p> <p>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, an ad-hoc 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 constuctors: attrgetter(), itemgetter(), and methodcaller(). See the ''Sorting HOW TO'' for examples of how to create and use key functions.</p></dd>

keyword argument


Arguments which are preceded with a variable_name= in the call. The variable name designates the local name in the function to which the value is assigned. ** is used to accept or pass a dictionary of keyword arguments. 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.

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 = ["0x%02x" % 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 [http://www.python.org/dev/peps/pep-0302 PEP 302] for details.

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.

metaclass


<dd><p>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.</p> <p>More information can be found in ''Customizing class creation''.</p></dd>

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''.

mutable


Mutable objects can change their value but keep their id(). See also ''immutable''.

named tuple


<dd><p>Any tuple-like class whose indexable elements are also accessible using named attributes (for example, time.localtime() returns a tuple-like object where the year is accessible either with an index such as t[0] or with a named attribute like t.tm_year).</p> <p>A named tuple can be a built-in type such as time.struct_time, or it can be created with a regular class definition. A full featured named tuple can also be created with the factory function collections.namedtuple(). The latter approach automatically provides extra features such as a self-documenting representation like Employee(name='jones', title='programmer').</p></dd>

namespace


The place where a variable is stored. Namespaces are implemented as dictionaries. There are the local, global and built-in namespaces as well as nested namespaces in objects (in methods). Namespaces support modularity by preventing naming conflicts. For instance, the functions builtin.open() and os.open() are distinguished by their namespaces. Namespaces also aid readability and maintainability by making it clear which module implements a function. For instance, writing random.seed() or itertools.izip() makes it clear that those functions are implemented by the random and itertools modules, respectively.

nested scope


The ability to refer to a variable in an enclosing definition. For instance, a function defined inside another function can refer to variables in the outer function. Note that nested scopes work only for reference and not for assignment which will always write to the innermost scope. In contrast, local variables both read and write in the innermost scope. Likewise, global variables read and write to the global namespace.

new-style class

<dd><p>Any class which inherits from object. This includes all built-in types like list and dict. Only new-style classes can use Python’s newer, versatile features like __slots__, descriptors, properties, and __getattribute__().</p> <p>More information can be found in ''New-style and classic classes''.</p></dd>

object


Any data with state (attributes or value) and defined behavior (methods). Also the ultimate base class of any ''new-style class''.

positional argument


The arguments assigned to local names inside a function or method, determined by the order in which they were given in the call. * is used to either accept multiple positional arguments (when in the definition), or pass several arguments as a list to a function. See ''argument''.

Python 3000


Nickname for the next major Python version, 3.0 (coined long ago when the release of version 3 was something in the distant future.) This is also abbreviated “Py3k”.
Pythonic


<dd><p>An idea or piece of code which closely follows the most common idioms of the Python language, rather than implementing code using concepts common to other languages. For example, a common idiom in Python is to loop over all elements of an iterable using a for statement. Many other languages don’t have this type of construct, so people unfamiliar with Python sometimes use a numerical counter instead:</p> <pre>for i in range(len(food)):

<p>As opposed to the cleaner, Pythonic method:</p> <pre>for piece in food:

virtual machine

A computer defined entirely in software. Python’s virtual machine executes the ''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.</dl>

Glosario (last edited 02-06-2011 20:01:52 by Kiko Correoso)