6. Expressões
*************

Este capítulo explica o significado dos elementos das expressões em
Python.

**Notas de sintaxe:** Neste e nos capítulos seguintes, a notação BNF
estendida será usada para descrever a sintaxe, não a análise lexical.
Quando (uma alternativa de) uma regra de sintaxe tem a forma

   name ::= othername

e nenhuma semântica é fornecida, a semântica desta forma de "name" é a
mesma que para "othername".


6.1. Conversões aritméticas
===========================

Quando uma descrição de um operador aritmético abaixo usa a frase "os
argumentos numéricos são convertidos em um tipo comum", isso significa
que a implementação do operador para tipos embutidos funciona da
seguinte maneira:

* Se um dos argumentos for um número complexo, o outro será convertido
  em complexo;

* caso contrário, se um dos argumentos for um número de ponto
  flutuante, o outro será convertido em ponto flutuante;

* caso contrário, ambos devem ser inteiros e nenhuma conversão é
  necessária.

Algumas regras adicionais se aplicam a certos operadores (por exemplo,
uma string como um argumento à esquerda para o operador '%'). As
extensões devem definir seu próprio comportamento de conversão.


6.2. Átomos
===========

Os átomos são os elementos mais básicos das expressões. Os átomos mais
simples são identificadores ou literais. As formas entre parênteses,
colchetes ou chaves também são categorizadas sintaticamente como
átomos. A sintaxe para átomos é:

   atom      ::= identifier | literal | enclosure
   enclosure ::= parenth_form | list_display | dict_display | set_display
                 | generator_expression | yield_atom


6.2.1. Identificadores (Nomes)
------------------------------

Um identificador que ocorre como um átomo é um nome. Veja a seção
Identificadores e Keywords para a definição lexical e a seção Nomeação
e ligação para documentação de nomenclatura e ligação.

Quando o nome está vinculado a um objeto, a avaliação do átomo produz
esse objeto. Quando um nome não está vinculado, uma tentativa de
avaliá-lo levanta uma exceção "NameError".

**Mangling de nome privado:** Quando um identificador que ocorre
textualmente em uma definição de classe começa com dois ou mais
caracteres de sublinhado e não termina em dois ou mais sublinhados,
ele é considerado um *nome privado* dessa classe. Os nomes privados
são transformados em um formato mais longo antes que o código seja
gerado para eles. A transformação insere o nome da classe, com
sublinhados à esquerda removidos e um único sublinhado inserido na
frente do nome. Por exemplo, o identificador "__spam" que ocorre em
uma classe chamada "Ham" será transformado em "_Ham__spam". Essa
transformação é independente do contexto sintático em que o
identificador é usado. Se o nome transformado for extremamente longo
(mais de 255 caracteres), poderá ocorrer truncamento definido pela
implementação. Se o nome da classe consistir apenas em sublinhados,
nenhuma transformação será feita.


6.2.2. Literais
---------------

Python oferece suporte a strings e bytes literais e vários literais
numéricos:

   literal ::= stringliteral | bytesliteral
               | integer | floatnumber | imagnumber

A avaliação de um literal produz um objeto do tipo fornecido (string,
bytes, inteiro, número de ponto flutuante, número complexo) com o
valor fornecido. O valor pode ser aproximado no caso de ponto
flutuante e literais imaginários (complexos). Veja a seção Literais
para detalhes.

Todos os literais correspondem a tipos de dados imutáveis e, portanto,
a identidade do objeto é menos importante que seu valor. Múltiplas
avaliações de literais com o mesmo valor (seja a mesma ocorrência no
texto do programa ou uma ocorrência diferente) podem obter o mesmo
objeto ou um objeto diferente com o mesmo valor.


6.2.3. Formas de parênteses
---------------------------

Um formulário entre parênteses é uma lista de expressões opcional
entre parênteses:

   parenth_form ::= "(" [starred_expression] ")"

Uma lista de expressões entre parênteses produz tudo o que aquela
lista de expressões produz: se a lista contiver pelo menos uma
vírgula, ela produzirá uma tupla; caso contrário, produz a única
expressão que compõe a lista de expressões.

Um par de parênteses vazio produz um objeto de tupla vazio. Como as
tuplas são imutáveis, aplicam-se as mesmas regras dos literais (isto
é, duas ocorrências da tupla vazia podem ou não produzir o mesmo
objeto).

Note that tuples are not formed by the parentheses, but rather by use
of the comma operator.  The exception is the empty tuple, for which
parentheses *are* required --- allowing unparenthesized "nothing" in
expressions would cause ambiguities and allow common typos to pass
uncaught.


6.2.4. Sintaxe de criação de listas, conjuntos e dicionários
------------------------------------------------------------

Para construir uma lista, um conjunto ou um dicionário, o Python
fornece uma sintaxe especial chamada "sintaxes de criação" (em inglês,
*displays*), cada uma delas em dois tipos:

* o conteúdo do contêiner é listado explicitamente ou

* eles são calculados por meio de um conjunto de instruções de laço e
  filtragem, chamado de *compreensão*.

Elementos de sintaxe comuns para compreensões são:

   comprehension ::= expression comp_for
   comp_for      ::= ["async"] "for" target_list "in" or_test [comp_iter]
   comp_iter     ::= comp_for | comp_if
   comp_if       ::= "if" expression_nocond [comp_iter]

A compreensão consiste em uma única expressão seguida por pelo menos
uma cláusula "for" e zero ou mais cláusulas "for" ou "if". Neste caso,
os elementos do novo contêiner são aqueles que seriam produzidos
considerando cada uma das cláusulas "for" ou "if" de um bloco,
aninhando da esquerda para a direita, e avaliando a expressão para
produzir um elemento cada vez que o bloco mais interno é alcançado.

No entanto, além da expressão iterável na cláusula "for" mais à
esquerda, a compreensão é executada em um escopo aninhado
implicitamente separado. Isso garante que os nomes atribuídos na lista
de destino não "vazem" para o escopo delimitador.

The iterable expression in the leftmost "for" clause is evaluated
directly in the enclosing scope and then passed as an argument to the
implictly nested scope. Subsequent "for" clauses and any filter
condition in the leftmost "for" clause cannot be evaluated in the
enclosing scope as they may depend on the values obtained from the
leftmost iterable. For example: "[x*y for x in range(10) for y in
range(x, x+10)]".

To ensure the comprehension always results in a container of the
appropriate type, "yield" and "yield from" expressions are prohibited
in the implicitly nested scope (in Python 3.7, such expressions emit
"DeprecationWarning" when compiled, in Python 3.8+ they will emit
"SyntaxError").

Since Python 3.6, in an "async def" function, an "async for" clause
may be used to iterate over a *asynchronous iterator*. A comprehension
in an "async def" function may consist of either a "for" or "async
for" clause following the leading expression, may contain additional
"for" or "async for" clauses, and may also use "await" expressions. If
a comprehension contains either "async for" clauses or "await"
expressions it is called an *asynchronous comprehension*.  An
asynchronous comprehension may suspend the execution of the coroutine
function in which it appears. See also **PEP 530**.

Novo na versão 3.6: Compreensões assíncronas foram introduzidas.

Obsoleto desde a versão 3.7: "yield" and "yield from" deprecated in
the implicitly nested scope.


6.2.5. Sintaxes de criação de lista
-----------------------------------

Uma sintaxe de criação de lista é uma série possivelmente vazia de
expressões entre colchetes:

   list_display ::= "[" [starred_list | comprehension] "]"

Uma sintaxe de criação de lista produz um novo objeto de lista, sendo
o conteúdo especificado por uma lista de expressões ou uma
compreensão. Quando uma lista de expressões separadas por vírgulas é
fornecida, seus elementos são avaliados da esquerda para a direita e
colocados no objeto de lista nessa ordem. Quando uma compreensão é
fornecida, a lista é construída a partir dos elementos resultantes da
compreensão.


6.2.6. Sintaxes de criação de conjunto
--------------------------------------

Uma sintaxe de criação definida é denotada por chaves e distinguível
de sintaxes de criação de dicionário pela falta de caractere de dois
pontos separando chaves e valores:

   set_display ::= "{" (starred_list | comprehension) "}"

Uma sintaxe de criação de conjunto produz um novo objeto de conjunto
mutável, sendo o conteúdo especificado por uma sequência de expressões
ou uma compreensão. Quando uma lista de expressões separadas por
vírgula é fornecida, seus elementos são avaliados da esquerda para a
direita e adicionados ao objeto definido. Quando uma compreensão é
fornecida, o conjunto é construído a partir dos elementos resultantes
da compreensão.

Um conjunto vazio não pode ser construído com "{}"; este literal
constrói um dicionário vazio.


6.2.7. Sintaxes de criação de dicionário
----------------------------------------

Uma sintaxe de criação de dicionário é uma série possivelmente vazia
de pares chave/dado entre chaves:

   dict_display       ::= "{" [key_datum_list | dict_comprehension] "}"
   key_datum_list     ::= key_datum ("," key_datum)* [","]
   key_datum          ::= expression ":" expression | "**" or_expr
   dict_comprehension ::= expression ":" expression comp_for

Uma sintaxe de criação de dicionário produz um novo objeto dicionário.

Se for fornecida uma sequência separada por vírgulas de pares
chave/dado, eles são avaliados da esquerda para a direita para definir
as entradas do dicionário: cada objeto chave é usado como uma chave no
dicionário para armazenar o dado correspondente. Isso significa que
você pode especificar a mesma chave várias vezes na lista de
dados/chave, e o valor final do dicionário para essa chave será o
último dado.

Um asterisco duplo "**" denota *desempacotamento do dicionário*. Seu
operando deve ser um *mapeamento*. Cada item de mapeamento é
adicionado ao novo dicionário. Os valores posteriores substituem os
valores já definidos por pares de dados/chave anteriores e
desempacotamentos de dicionário anteriores.

Novo na versão 3.5: Descompactando em sintaxes de criação de
dicionário, originalmente proposto pela **PEP 448**.

Uma compreensão de dict, em contraste com as compreensões de lista e
conjunto, precisa de duas expressões separadas por dois pontos,
seguidas pelas cláusulas usuais "for" e "if". Quando a compreensão é
executada, os elementos chave e valor resultantes são inseridos no
novo dicionário na ordem em que são produzidos.

Restrições nos tipos de valores de chave são listadas anteriormente na
seção A hierarquia de tipos padrão. (Para resumir, o tipo de chave
deve ser *hasheável*, que exclui todos os objetos mutáveis.) Não são
detectadas colisões entre chaves duplicadas; o último dado
(textualmente mais à direita na sintaxe de criação) armazenado para um
determinado valor de chave prevalece.


6.2.8. Expressões geradoras
---------------------------

Uma expressão geradora é uma notação geradora compacta entre
parênteses:

   generator_expression ::= "(" expression comp_for ")"

Uma expressão geradora produz um novo objeto gerador. Sua sintaxe é a
mesma das compreensões, exceto pelo fato de estar entre parênteses em
vez de colchetes ou chaves.

As variáveis usadas na expressão geradora são avaliadas lentamente
quando o método "__next__()" é chamado para o objeto gerador (da mesma
forma que os geradores normais). No entanto, a expressão iterável na
cláusula "for" mais à esquerda é avaliada imediatamente, de modo que
um erro produzido por ela será emitido no ponto em que a expressão do
gerador é definida, em vez de no ponto em que o primeiro valor é
recuperado. Cláusulas "for" subsequentes e qualquer condição de filtro
na cláusula "for" mais à esquerda não podem ser avaliadas no escopo
delimitador, pois podem depender dos valores obtidos do iterável mais
à esquerda. Por exemplo: "(x*y for x in range(10) for y in range(x,
x+10))".

Os parênteses podem ser omitidos em chamadas com apenas um argumento.
Veja a seção Chamadas para detalhes.

To avoid interfering with the expected operation of the generator
expression itself, "yield" and "yield from" expressions are prohibited
in the implicitly defined generator (in Python 3.7, such expressions
emit "DeprecationWarning" when compiled, in Python 3.8+ they will emit
"SyntaxError").

Se uma expressão geradora contém cláusulas "async for" ou expressões
"await", ela é chamada de *expressão geradora assíncrona*. Uma
expressão geradora assíncrona retorna um novo objeto gerador
assíncrono, que é um iterador assíncrono (consulte Iteradores
Assíncronos).

Novo na versão 3.6: Expressões geradoras assíncronas foram
introduzidas.

Alterado na versão 3.7: Antes do Python 3.7, as expressões geradoras
assíncronas só podiam aparecer em corrotinas "async def". A partir da
versão 3.7, qualquer função pode usar expressões geradoras
assíncronas.

Obsoleto desde a versão 3.7: "yield" and "yield from" deprecated in
the implicitly nested scope.


6.2.9. Expressões yield
-----------------------

   yield_atom       ::= "(" yield_expression ")"
   yield_expression ::= "yield" [expression_list | "from" expression]

The yield expression is used when defining a *generator* function or
an *asynchronous generator* function and thus can only be used in the
body of a function definition.  Using a yield expression in a
function's body causes that function to be a generator, and using it
in an "async def" function's body causes that coroutine function to be
an asynchronous generator. For example:

   def gen():  # defines a generator function
       yield 123

   async def agen(): # defines an asynchronous generator function
       yield 123

Due to their side effects on the containing scope, "yield" expressions
are not permitted as part of the implicitly defined scopes used to
implement comprehensions and generator expressions (in Python 3.7,
such expressions emit "DeprecationWarning" when compiled, in Python
3.8+ they will emit "SyntaxError")..

Obsoleto desde a versão 3.7: Yield expressions deprecated in the
implicitly nested scopes used to implement comprehensions and
generator expressions.

As funções geradoras são descritas abaixo, enquanto as funções
geradoras assíncronas são descritas separadamente na seção Funções
geradoras assíncronas

When a generator function is called, it returns an iterator known as a
generator.  That generator then controls the execution of the
generator function. The execution starts when one of the generator's
methods is called.  At that time, the execution proceeds to the first
yield expression, where it is suspended again, returning the value of
"expression_list" to the generator's caller.  By suspended, we mean
that all local state is retained, including the current bindings of
local variables, the instruction pointer, the internal evaluation
stack, and the state of any exception handling.  When the execution is
resumed by calling one of the generator's methods, the function can
proceed exactly as if the yield expression were just another external
call.  The value of the yield expression after resuming depends on the
method which resumed the execution.  If "__next__()" is used
(typically via either a "for" or the "next()" builtin) then the result
is "None".  Otherwise, if "send()" is used, then the result will be
the value passed in to that method.

Tudo isso torna as funções geradoras bastante semelhantes às
corrotinas; cedem múltiplas vezes, possuem mais de um ponto de entrada
e sua execução pode ser suspensa. A única diferença é que uma função
geradora não pode controlar onde a execução deve continuar após o seu
rendimento; o controle é sempre transferido para o chamador do
gerador.

Expressões yield são permitidas em qualquer lugar em uma construção
"try". Se o gerador não for retomado antes de ser finalizado (ao
atingir uma contagem de referências zero ou ao ser coletado como
lixo), o método "close()" do iterador de gerador será chamado,
permitindo que quaisquer cláusulas "finally" pendentes sejam
executadas.

When "yield from <expr>" is used, it treats the supplied expression as
a subiterator. All values produced by that subiterator are passed
directly to the caller of the current generator's methods. Any values
passed in with "send()" and any exceptions passed in with "throw()"
are passed to the underlying iterator if it has the appropriate
methods.  If this is not the case, then "send()" will raise
"AttributeError" or "TypeError", while "throw()" will just raise the
passed in exception immediately.

Quando o iterador subjacente estiver completo, o atributo "value" da
instância "StopIteration" gerada torna-se o valor da expressão yield.
Ele pode ser definido explicitamente ao levantar "StopIteration" ou
automaticamente quando o subiterador é um gerador (retornando um valor
do subgerador).

   Alterado na versão 3.3: Adicionado "yield from <expr>" para delegar
   o fluxo de controle a um subiterador.

Os parênteses podem ser omitidos quando a expressão yield é a única
expressão no lado direito de uma instrução de atribuição.

Ver também:

  **PEP 255** - Geradores simples
     A proposta para adicionar geradores e a instrução "yield" ao
     Python.

  **PEP 342** - Corrotinas via Geradores Aprimorados
     A proposta de aprimorar a API e a sintaxe dos geradores,
     tornando-os utilizáveis como simples corrotinas.

  **PEP 380** - Sintaxe para Delegar a um Subgerador
     The proposal to introduce the "yield_from" syntax, making
     delegation to subgenerators easy.

  **PEP 525** - Geradores assíncronos
     A proposta que se expandiu em **PEP 492** adicionando recursos de
     gerador a funções de corrotina.


6.2.9.1. Métodos de iterador gerador
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Esta subseção descreve os métodos de um iterador gerador. Eles podem
ser usados para controlar a execução de uma função geradora.

Observe que chamar qualquer um dos métodos do gerador abaixo quando o
gerador já estiver em execução levanta uma exceção "ValueError".

generator.__next__()

   Starts the execution of a generator function or resumes it at the
   last executed yield expression.  When a generator function is
   resumed with a "__next__()" method, the current yield expression
   always evaluates to "None".  The execution then continues to the
   next yield expression, where the generator is suspended again, and
   the value of the "expression_list" is returned to "__next__()"'s
   caller.  If the generator exits without yielding another value, a
   "StopIteration" exception is raised.

   Este método é normalmente chamado implicitamente, por exemplo por
   um laço "for", ou pela função embutida "next()".

generator.send(value)

   Retoma a execução e "envia" um valor para a função geradora. O
   argumento *value* torna-se o resultado da expressão yield atual. O
   método "send()" retorna o próximo valor gerado pelo gerador, ou
   levanta "StopIteration" se o gerador sair sem produzir outro valor.
   Quando "send()" é chamado para iniciar o gerador, ele deve ser
   chamado com "None" como argumento, porque não há nenhuma expressão
   yield que possa receber o valor.

generator.throw(type[, value[, traceback]])

   Raises an exception of type "type" at the point where the generator
   was paused, and returns the next value yielded by the generator
   function.  If the generator exits without yielding another value, a
   "StopIteration" exception is raised.  If the generator function
   does not catch the passed-in exception, or raises a different
   exception, then that exception propagates to the caller.

generator.close()

   Levanta "GeneratorExit" no ponto onde a função geradora foi
   pausada. Se a função geradora sair normalmente, já estiver fechada
   ou levantar "GeneratorExit" (por não capturar a exceção), "close"
   retornará ao seu chamador. Se o gerador produzir um valor, um
   "RuntimeError" é levantado. Se o gerador levantar qualquer outra
   exceção, ela será propagada para o chamador. "close()" não faz nada
   se o gerador já saiu devido a uma exceção ou saída normal.


6.2.9.2. Exemplos
~~~~~~~~~~~~~~~~~

Aqui está um exemplo simples que demonstra o comportamento de
geradores e funções geradoras:

   >>> def echo(value=None):
   ...     print("Execution starts when 'next()' is called for the first time.")
   ...     try:
   ...         while True:
   ...             try:
   ...                 value = (yield value)
   ...             except Exception as e:
   ...                 value = e
   ...     finally:
   ...         print("Don't forget to clean up when 'close()' is called.")
   ...
   >>> generator = echo(1)
   >>> print(next(generator))
   Execution starts when 'next()' is called for the first time.
   1
   >>> print(next(generator))
   None
   >>> print(generator.send(2))
   2
   >>> generator.throw(TypeError, "spam")
   TypeError('spam',)
   >>> generator.close()
   Don't forget to clean up when 'close()' is called.

Para exemplos usando "yield from", consulte a PEP 380: Syntax for
Delegating to a Subgenerator em "O que há de novo no Python."


6.2.9.3. Funções geradoras assíncronas
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The presence of a yield expression in a function or method defined
using "async def" further defines the function as an *asynchronous
generator* function.

When an asynchronous generator function is called, it returns an
asynchronous iterator known as an asynchronous generator object. That
object then controls the execution of the generator function. An
asynchronous generator object is typically used in an "async for"
statement in a coroutine function analogously to how a generator
object would be used in a "for" statement.

Calling one of the asynchronous generator's methods returns an
*awaitable* object, and the execution starts when this object is
awaited on. At that time, the execution proceeds to the first yield
expression, where it is suspended again, returning the value of
"expression_list" to the awaiting coroutine. As with a generator,
suspension means that all local state is retained, including the
current bindings of local variables, the instruction pointer, the
internal evaluation stack, and the state of any exception handling.
When the execution is resumed by awaiting on the next object returned
by the asynchronous generator's methods, the function can proceed
exactly as if the yield expression were just another external call.
The value of the yield expression after resuming depends on the method
which resumed the execution.  If "__anext__()" is used then the result
is "None". Otherwise, if "asend()" is used, then the result will be
the value passed in to that method.

In an asynchronous generator function, yield expressions are allowed
anywhere in a "try" construct. However, if an asynchronous generator
is not resumed before it is finalized (by reaching a zero reference
count or by being garbage collected), then a yield expression within a
"try" construct could result in a failure to execute pending "finally"
clauses.  In this case, it is the responsibility of the event loop or
scheduler running the asynchronous generator to call the asynchronous
generator-iterator's "aclose()" method and run the resulting coroutine
object, thus allowing any pending "finally" clauses to execute.

To take care of finalization, an event loop should define a
*finalizer* function which takes an asynchronous generator-iterator
and presumably calls "aclose()" and executes the coroutine. This
*finalizer* may be registered by calling "sys.set_asyncgen_hooks()".
When first iterated over, an asynchronous generator-iterator will
store the registered *finalizer* to be called upon finalization. For a
reference example of a *finalizer* method see the implementation of
"asyncio.Loop.shutdown_asyncgens" in Lib/asyncio/base_events.py.

The expression "yield from <expr>" is a syntax error when used in an
asynchronous generator function.


6.2.9.4. Asynchronous generator-iterator methods
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This subsection describes the methods of an asynchronous generator
iterator, which are used to control the execution of a generator
function.

coroutine agen.__anext__()

   Returns an awaitable which when run starts to execute the
   asynchronous generator or resumes it at the last executed yield
   expression.  When an asynchronous generator function is resumed
   with an "__anext__()" method, the current yield expression always
   evaluates to "None" in the returned awaitable, which when run will
   continue to the next yield expression. The value of the
   "expression_list" of the yield expression is the value of the
   "StopIteration" exception raised by the completing coroutine.  If
   the asynchronous generator exits without yielding another value,
   the awaitable instead raises a "StopAsyncIteration" exception,
   signalling that the asynchronous iteration has completed.

   This method is normally called implicitly by a "async for" loop.

coroutine agen.asend(value)

   Returns an awaitable which when run resumes the execution of the
   asynchronous generator. As with the "send()" method for a
   generator, this "sends" a value into the asynchronous generator
   function, and the *value* argument becomes the result of the
   current yield expression. The awaitable returned by the "asend()"
   method will return the next value yielded by the generator as the
   value of the raised "StopIteration", or raises "StopAsyncIteration"
   if the asynchronous generator exits without yielding another value.
   When "asend()" is called to start the asynchronous generator, it
   must be called with "None" as the argument, because there is no
   yield expression that could receive the value.

coroutine agen.athrow(value)
coroutine agen.athrow(type[, value[, traceback]])

   Returns an awaitable that raises an exception of type "type" at the
   point where the asynchronous generator was paused, and returns the
   next value yielded by the generator function as the value of the
   raised "StopIteration" exception.  If the asynchronous generator
   exits without yielding another value, a "StopAsyncIteration"
   exception is raised by the awaitable. If the generator function
   does not catch the passed-in exception, or raises a different
   exception, then when the awaitable is run that exception propagates
   to the caller of the awaitable.

coroutine agen.aclose()

   Returns an awaitable that when run will throw a "GeneratorExit"
   into the asynchronous generator function at the point where it was
   paused. If the asynchronous generator function then exits
   gracefully, is already closed, or raises "GeneratorExit" (by not
   catching the exception), then the returned awaitable will raise a
   "StopIteration" exception. Any further awaitables returned by
   subsequent calls to the asynchronous generator will raise a
   "StopAsyncIteration" exception.  If the asynchronous generator
   yields a value, a "RuntimeError" is raised by the awaitable.  If
   the asynchronous generator raises any other exception, it is
   propagated to the caller of the awaitable.  If the asynchronous
   generator has already exited due to an exception or normal exit,
   then further calls to "aclose()" will return an awaitable that does
   nothing.


6.3. Primaries
==============

Primaries represent the most tightly bound operations of the language.
Their syntax is:

   primary ::= atom | attributeref | subscription | slicing | call


6.3.1. Attribute references
---------------------------

An attribute reference is a primary followed by a period and a name:

   attributeref ::= primary "." identifier

The primary must evaluate to an object of a type that supports
attribute references, which most objects do.  This object is then
asked to produce the attribute whose name is the identifier.  This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised.  Otherwise, the type and value of the object produced is
determined by the object.  Multiple evaluations of the same attribute
reference may yield different objects.


6.3.2. Subscriptions
--------------------

A subscription selects an item of a sequence (string, tuple or list)
or mapping (dictionary) object:

   subscription ::= primary "[" expression_list "]"

The primary must evaluate to an object that supports subscription
(lists or dictionaries for example).  User-defined objects can support
subscription by defining a "__getitem__()" method.

For built-in objects, there are two types of objects that support
subscription:

If the primary is a mapping, the expression list must evaluate to an
object whose value is one of the keys of the mapping, and the
subscription selects the value in the mapping that corresponds to that
key.  (The expression list is a tuple except if it has exactly one
item.)

If the primary is a sequence, the expression list must evaluate to an
integer or a slice (as discussed in the following section).

The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a "__getitem__()"
method that interprets negative indices by adding the length of the
sequence to the index (so that "x[-1]" selects the last item of "x").
The resulting value must be a nonnegative integer less than the number
of items in the sequence, and the subscription selects the item whose
index is that value (counting from zero). Since the support for
negative indices and slicing occurs in the object's "__getitem__()"
method, subclasses overriding this method will need to explicitly add
that support.

A string's items are characters.  A character is not a separate data
type but a string of exactly one character.


6.3.3. Slicings
---------------

A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list).  Slicings may be used as expressions or as
targets in assignment or "del" statements.  The syntax for a slicing:

   slicing      ::= primary "[" slice_list "]"
   slice_list   ::= slice_item ("," slice_item)* [","]
   slice_item   ::= expression | proper_slice
   proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
   lower_bound  ::= expression
   upper_bound  ::= expression
   stride       ::= expression

There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing.  Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).

The semantics for a slicing are as follows.  The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows.  If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key.  The conversion of a slice item that is an
expression is that expression.  The conversion of a proper slice is a
slice object (see section A hierarquia de tipos padrão) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.


6.3.4. Chamadas
---------------

A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:

   call                 ::= primary "(" [argument_list [","] | comprehension] ")"
   argument_list        ::= positional_arguments ["," starred_and_keywords]
                       ["," keywords_arguments]
                     | starred_and_keywords ["," keywords_arguments]
                     | keywords_arguments
   positional_arguments ::= ["*"] expression ("," ["*"] expression)*
   starred_and_keywords ::= ("*" expression | keyword_item)
                            ("," "*" expression | "," keyword_item)*
   keywords_arguments   ::= (keyword_item | "**" expression)
                          ("," keyword_item | "," "**" expression)*
   keyword_item         ::= identifier "=" expression

An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable).  All argument expressions are
evaluated before the call is attempted.  Please refer to section
Definições de função for the syntax of formal *parameter* lists.

If keyword arguments are present, they are first converted to
positional arguments, as follows.  First, a list of unfilled slots is
created for the formal parameters.  If there are N positional
arguments, they are placed in the first N slots.  Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on).  If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot).  When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition.  (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don't specify an argument value for the
corresponding slot; this should usually be avoided.)  If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised.  Otherwise, the list of filled slots is used as
the argument list for the call.

**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are 'named' for the purpose of documentation, and which
therefore cannot be supplied by keyword.  In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.

If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.

If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*.  Elements from these iterables are
treated as if they were additional positional arguments.  For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, ...,
*yM*, this is equivalent to a call with M+4 positional arguments *x1*,
*x2*, *y1*, ..., *yM*, *x3*, *x4*.

A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments -- see below).
So:

   >>> def f(a, b):
   ...     print(a, b)
   ...
   >>> f(b=1, *(2,))
   2 1
   >>> f(a=1, *(2,))
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: f() got multiple values for keyword argument 'a'
   >>> f(1, *(2,))
   1 2

It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.

If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments.  If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.

Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.

Alterado na versão 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.

A call always returns some value, possibly "None", unless it raises an
exception.  How this value is computed depends on the type of the
callable object.

If it is---

a user-defined function:
   The code block for the function is executed, passing it the
   argument list.  The first thing the code block will do is bind the
   formal parameters to the arguments; this is described in section
   Definições de função.  When the code block executes a "return"
   statement, this specifies the return value of the function call.

a built-in function or method:
   The result is up to the interpreter; see Funções embutidas for the
   descriptions of built-in functions and methods.

um objeto classe:
   A new instance of that class is returned.

a class instance method:
   The corresponding user-defined function is called, with an argument
   list that is one longer than the argument list of the call: the
   instance becomes the first argument.

a class instance:
   The class must define a "__call__()" method; the effect is then the
   same as if that method was called.


6.4. Await expression
=====================

Suspend the execution of *coroutine* on an *awaitable* object. Can
only be used inside a *coroutine function*.

   await_expr ::= "await" primary

Novo na versão 3.5.


6.5. The power operator
=======================

The power operator binds more tightly than unary operators on its
left; it binds less tightly than unary operators on its right.  The
syntax is:

   power ::= (await_expr | primary) ["**" u_expr]

Thus, in an unparenthesized sequence of power and unary operators, the
operators are evaluated from right to left (this does not constrain
the evaluation order for the operands): "-1**2" results in "-1".

The power operator has the same semantics as the built-in "pow()"
function, when called with two arguments: it yields its left argument
raised to the power of its right argument.  The numeric arguments are
first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless
the second argument is negative; in that case, all arguments are
converted to float and a float result is delivered. For example,
"10**2" returns "100", but "10**-2" returns "0.01".

Raising "0.0" to a negative power results in a "ZeroDivisionError".
Raising a negative number to a fractional power results in a "complex"
number. (In earlier versions it raised a "ValueError".)


6.6. Unary arithmetic and bitwise operations
============================================

All unary arithmetic and bitwise operations have the same priority:

   u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr

The unary "-" (minus) operator yields the negation of its numeric
argument.

The unary "+" (plus) operator yields its numeric argument unchanged.

The unary "~" (invert) operator yields the bitwise inversion of its
integer argument.  The bitwise inversion of "x" is defined as
"-(x+1)".  It only applies to integral numbers.

In all three cases, if the argument does not have the proper type, a
"TypeError" exception is raised.


6.7. Binary arithmetic operations
=================================

The binary arithmetic operations have the conventional priority
levels.  Note that some of these operations also apply to certain non-
numeric types.  Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:

   m_expr ::= u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
              m_expr "//" u_expr | m_expr "/" u_expr |
              m_expr "%" u_expr
   a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr

The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.

The "@" (at) operator is intended to be used for matrix
multiplication.  No builtin Python types implement this operator.

Novo na versão 3.5.

The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments.  The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the 'floor' function applied to the
result.  Division by zero raises the "ZeroDivisionError" exception.

The "%" (modulo) operator yields the remainder from the division of
the first argument by the second.  The numeric arguments are first
converted to a common type.  A zero right argument raises the
"ZeroDivisionError" exception.  The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".)  The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].

The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)".  Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].

In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation).  The syntax for
string formatting is described in the Python Library Reference,
section Formatação de String no Formato printf-style.

The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers.  Instead, convert to a
floating point number using the "abs()" function if appropriate.

The "+" (addition) operator yields the sum of its arguments.  The
arguments must either both be numbers or both be sequences of the same
type.  In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.

The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.


6.8. Shifting operations
========================

The shifting operations have lower priority than the arithmetic
operations:

   shift_expr ::= a_expr | shift_expr ("<<" | ">>") a_expr

These operators accept integers as arguments.  They shift the first
argument to the left or right by the number of bits given by the
second argument.

A right shift by *n* bits is defined as floor division by "pow(2,n)".
A left shift by *n* bits is defined as multiplication with "pow(2,n)".


6.9. Binary bitwise operations
==============================

Each of the three bitwise operations has a different priority level:

   and_expr ::= shift_expr | and_expr "&" shift_expr
   xor_expr ::= and_expr | xor_expr "^" and_expr
   or_expr  ::= xor_expr | or_expr "|" xor_expr

The "&" operator yields the bitwise AND of its arguments, which must
be integers.

The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.

The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.


6.10. Comparações
=================

Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation.  Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:

   comparison    ::= or_expr (comp_operator or_expr)*
   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
                     | "is" ["not"] | ["not"] "in"

Comparisons yield boolean values: "True" or "False".

Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).

Formally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,
*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.

Note that "a op1 b op2 c" doesn't imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).


6.10.1. Value comparisons
-------------------------

The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects.  The objects do not need to have the same type.

Chapter Objetos, valores e tipos states that objects have a value (in
addition to type and identity).  The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object's value.  Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is.  One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.

Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object".  Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Customização básica.

The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects.  Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality.  A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").

A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError".  A motivation for this default behavior
is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality.  Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.

The following list describes the comparison behavior of the most
important built-in types.

* Numbers of built-in numeric types (Tipos Numéricos --- int, float,
  complex) and of the standard library types "fractions.Fraction" and
  "decimal.Decimal" can be compared within and across their types,
  with the restriction that complex numbers do not support order
  comparison.  Within the limits of the types involved, they compare
  mathematically (algorithmically) correct without loss of precision.

  The not-a-number values "float('NaN')" and "decimal.Decimal('NaN')"
  are special.  Any ordered comparison of a number to a not-a-number
  value is false. A counter-intuitive implication is that not-a-number
  values are not equal to themselves.  For example, if "x =
  float('NaN')", "3 < x", "x < 3" and "x == x" are all false, while "x
  != x" is true.  This behavior is compliant with IEEE 754.

* Binary sequences (instances of "bytes" or "bytearray") can be
  compared within and across their types.  They compare
  lexicographically using the numeric values of their elements.

* Strings (instances of "str") compare lexicographically using the
  numerical Unicode code points (the result of the built-in function
  "ord()") of their characters. [3]

  Strings and binary sequences cannot be directly compared.

* Sequences (instances of "tuple", "list", or "range") can be compared
  only within each of their types, with the restriction that ranges do
  not support order comparison.  Equality comparison across these
  types results in inequality, and ordering comparison across these
  types raises "TypeError".

  Sequences compare lexicographically using comparison of
  corresponding elements, whereby reflexivity of the elements is
  enforced.

  In enforcing reflexivity of elements, the comparison of collections
  assumes that for a collection element "x", "x == x" is always true.
  Based on that assumption, element identity is compared first, and
  element comparison is performed only for distinct elements.  This
  approach yields the same result as a strict element comparison
  would, if the compared elements are reflexive.  For non-reflexive
  elements, the result is different than for strict element
  comparison, and may be surprising:  The non-reflexive not-a-number
  values for example result in the following comparison behavior when
  used in a list:

     >>> nan = float('NaN')
     >>> nan is nan
     True
     >>> nan == nan
     False                 <-- the defined non-reflexive behavior of NaN
     >>> [nan] == [nan]
     True                  <-- list enforces reflexivity and tests identity first

  Lexicographical comparison between built-in collections works as
  follows:

  * For two collections to compare equal, they must be of the same
    type, have the same length, and each pair of corresponding
    elements must compare equal (for example, "[1,2] == (1,2)" is
    false because the type is not the same).

  * Collections that support order comparison are ordered the same as
    their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
    has the same value as "x <= y").  If a corresponding element does
    not exist, the shorter collection is ordered first (for example,
    "[1,2] < [1,2,3]" is true).

* Mappings (instances of "dict") compare equal if and only if they
  have equal *(key, value)* pairs. Equality comparison of the keys and
  values enforces reflexivity.

  Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".

* Sets (instances of "set" or "frozenset") can be compared within and
  across their types.

  They define order comparison operators to mean subset and superset
  tests.  Those relations do not define total orderings (for example,
  the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
  another, nor supersets of one another).  Accordingly, sets are not
  appropriate arguments for functions which depend on total ordering
  (for example, "min()", "max()", and "sorted()" produce undefined
  results given a list of sets as inputs).

  Comparison of sets enforces reflexivity of its elements.

* Most other built-in types have no comparison methods implemented, so
  they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:

* Equality comparison should be reflexive. In other words, identical
  objects should compare equal:

     "x is y" implies "x == y"

* Comparison should be symmetric. In other words, the following
  expressions should have the same result:

     "x == y" and "y == x"

     "x != y" and "y != x"

     "x < y" and "y > x"

     "x <= y" and "y >= x"

* Comparison should be transitive. The following (non-exhaustive)
  examples illustrate that:

     "x > y and y > z" implies "x > z"

     "x < y and y <= z" implies "x < z"

* Inverse comparison should result in the boolean negation. In other
  words, the following expressions should have the same result:

     "x == y" and "not x != y"

     "x < y" and "not x >= y" (for total ordering)

     "x > y" and "not x <= y" (for total ordering)

  The last two expressions apply to totally ordered collections (e.g.
  to sequences, but not to sets or mappings). See also the
  "total_ordering()" decorator.

* The "hash()" result should be consistent with equality. Objects that
  are equal should either have the same hash value, or be marked as
  unhashable.

Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.


6.10.2. Membership test operations
----------------------------------

The operators "in" and "not in" test for membership.  "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s".  All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".

For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*.  An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".

For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.

For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z", for which
the expression "x is z or x == z" is true, is produced while iterating
over "y". If an exception is raised during the iteration, it is as if
"in" raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x is y[i] or x == y[i]", and no
lower integer index raises the "IndexError" exception.  (If any other
exception is raised, it is as if "in" raised that exception).

The operator "not in" is defined to have the inverse truth value of
"in".


6.10.3. Identity comparisons
----------------------------

The operators "is" and "is not" test for an object's identity: "x is
y" is true if and only if *x* and *y* are the same object.  An
Object's identity is determined using the "id()" function.  "x is not
y" yields the inverse truth value. [4]


6.11. Boolean operations
========================

   or_test  ::= and_test | or_test "or" and_test
   and_test ::= not_test | and_test "and" not_test
   not_test ::= comparison | "not" not_test

In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets).  All other values are interpreted
as true.  User-defined objects can customize their truth value by
providing a "__bool__()" method.

The operator "not" yields "True" if its argument is false, "False"
otherwise.

The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.

The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.

Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument.  This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value.  Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)


6.12. Conditional expressions
=============================

   conditional_expression ::= or_test ["if" or_test "else" expression]
   expression             ::= conditional_expression | lambda_expr
   expression_nocond      ::= or_test | lambda_expr_nocond

Conditional expressions (sometimes called a "ternary operator") have
the lowest priority of all Python operations.

The expression "x if C else y" first evaluates the condition, *C*
rather than *x*. If *C* is true, *x* is evaluated and its value is
returned; otherwise, *y* is evaluated and its value is returned.

See **PEP 308** for more details about conditional expressions.


6.13. Lambdas
=============

   lambda_expr        ::= "lambda" [parameter_list] ":" expression
   lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond

Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object.  The unnamed object behaves like a function
object defined with:

   def <lambda>(parameters):
       return expression

See section Definições de função for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.


6.14. Expression lists
======================

   expression_list    ::= expression ("," expression)* [","]
   starred_list       ::= starred_item ("," starred_item)* [","]
   starred_expression ::= expression | (starred_item ",")* [starred_item]
   starred_item       ::= expression | "*" or_expr

Except when part of a list or set display, an expression list
containing at least one comma yields a tuple.  The length of the tuple
is the number of expressions in the list.  The expressions are
evaluated from left to right.

An asterisk "*" denotes *iterable unpacking*.  Its operand must be an
*iterable*.  The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.

Novo na versão 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.

The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases.  A single expression
without a trailing comma doesn't create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)


6.15. Evaluation order
======================

Python evaluates expressions from left to right.  Notice that while
evaluating an assignment, the right-hand side is evaluated before the
left-hand side.

In the following lines, expressions will be evaluated in the
arithmetic order of their suffixes:

   expr1, expr2, expr3, expr4
   (expr1, expr2, expr3, expr4)
   {expr1: expr2, expr3: expr4}
   expr1 + expr2 * (expr3 - expr4)
   expr1(expr2, expr3, *expr4, **expr5)
   expr3, expr4 = expr1, expr2


6.16. Operator precedence
=========================

The following table summarizes the operator precedence in Python, from
lowest precedence (least binding) to highest precedence (most
binding).  Operators in the same box have the same precedence.  Unless
the syntax is explicitly given, operators are binary.  Operators in
the same box group left to right (except for exponentiation, which
groups from right to left).

Note that comparisons, membership tests, and identity tests, all have
the same precedence and have a left-to-right chaining feature as
described in the Comparações section.

+-------------------------------------------------+---------------------------------------+
| Operator                                        | Description (descrição)               |
|=================================================|=======================================|
| "lambda"                                        | Lambda expression                     |
+-------------------------------------------------+---------------------------------------+
| "if" -- "else"                                  | Conditional expression                |
+-------------------------------------------------+---------------------------------------+
| "or"                                            | Boolean OR                            |
+-------------------------------------------------+---------------------------------------+
| "and"                                           | Boolean AND                           |
+-------------------------------------------------+---------------------------------------+
| "not" "x"                                       | Boolean NOT                           |
+-------------------------------------------------+---------------------------------------+
| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership     |
| ">=", "!=", "=="                                | tests and identity tests              |
+-------------------------------------------------+---------------------------------------+
| "|"                                             | Bitwise OR                            |
+-------------------------------------------------+---------------------------------------+
| "^"                                             | Bitwise XOR                           |
+-------------------------------------------------+---------------------------------------+
| "&"                                             | Bitwise AND                           |
+-------------------------------------------------+---------------------------------------+
| "<<", ">>"                                      | Shifts                                |
+-------------------------------------------------+---------------------------------------+
| "+", "-"                                        | Addition and subtraction              |
+-------------------------------------------------+---------------------------------------+
| "*", "@", "/", "//", "%"                        | Multiplication, matrix                |
|                                                 | multiplication, division, floor       |
|                                                 | division, remainder [5]               |
+-------------------------------------------------+---------------------------------------+
| "+x", "-x", "~x"                                | Positive, negative, bitwise NOT       |
+-------------------------------------------------+---------------------------------------+
| "**"                                            | Exponentiation [6]                    |
+-------------------------------------------------+---------------------------------------+
| "await" "x"                                     | Await expression                      |
+-------------------------------------------------+---------------------------------------+
| "x[index]", "x[index:index]",                   | Subscription, slicing, call,          |
| "x(arguments...)", "x.attribute"                | attribute reference                   |
+-------------------------------------------------+---------------------------------------+
| "(expressions...)",  "[expressions...]", "{key: | Binding or parenthesized expression,  |
| value...}", "{expressions...}"                  | list display, dictionary display, set |
|                                                 | display                               |
+-------------------------------------------------+---------------------------------------+

-[ Notas de rodapé ]-

[1] While "abs(x%y) < abs(y)" is true mathematically, for floats it
    may not be true numerically due to roundoff.  For example, and
    assuming a platform on which a Python float is an IEEE 754 double-
    precision number, in order that "-1e-100 % 1e100" have the same
    sign as "1e100", the computed result is "-1e-100 + 1e100", which
    is numerically exactly equal to "1e100".  The function
    "math.fmod()" returns a result whose sign matches the sign of the
    first argument instead, and so returns "-1e-100" in this case.
    Which approach is more appropriate depends on the application.

[2] If x is very close to an exact integer multiple of y, it's
    possible for "x//y" to be one larger than "(x-x%y)//y" due to
    rounding.  In such cases, Python returns the latter result, in
    order to preserve that "divmod(x,y)[0] * y + x % y" be very close
    to "x".

[3] The Unicode standard distinguishes between *code points* (e.g.
    U+0041) and *abstract characters* (e.g. "LATIN CAPITAL LETTER A").
    While most abstract characters in Unicode are only represented
    using one code point, there is a number of abstract characters
    that can in addition be represented using a sequence of more than
    one code point.  For example, the abstract character "LATIN
    CAPITAL LETTER C WITH CEDILLA" can be represented as a single
    *precomposed character* at code position U+00C7, or as a sequence
    of a *base character* at code position U+0043 (LATIN CAPITAL
    LETTER C), followed by a *combining character* at code position
    U+0327 (COMBINING CEDILLA).

    The comparison operators on strings compare at the level of
    Unicode code points. This may be counter-intuitive to humans.  For
    example, ""\u00C7" == "\u0043\u0327"" is "False", even though both
    strings represent the same abstract character "LATIN CAPITAL
    LETTER C WITH CEDILLA".

    To compare strings at the level of abstract characters (that is,
    in a way intuitive to humans), use "unicodedata.normalize()".

[4] Due to automatic garbage-collection, free lists, and the dynamic
    nature of descriptors, you may notice seemingly unusual behaviour
    in certain uses of the "is" operator, like those involving
    comparisons between instance methods, or constants.  Check their
    documentation for more info.

[5] The "%" operator is also used for string formatting; the same
    precedence applies.

[6] The power operator "**" binds less tightly than an arithmetic or
    bitwise unary operator on its right, that is, "2**-1" is "0.5".
