Open xxleyi opened 5 years ago
When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type,” this means that the operator implementation for built-in types works as follows:
If either argument is a complex number, the other is converted to complex;
otherwise, if either argument is a floating point number, the other is converted to floating point;
otherwise, both must be integers and no conversion is necessary.
Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:
atom ::= identifier | literal | enclosure
enclosure ::= parenth_form | list_display | dict_display | set_display
| generator_expression | yield_atom
An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError
exception.
Private name mangling: When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class.
Python supports string and bytes literals and various numeric literals:
literal ::= stringliteral | bytesliteral
| integer | floatnumber | imagnumber
Evaluation of a literal yields an object of the given type (string, bytes, integer, floating point number, complex number) with the given value.
All literals correspond to immutable data types, and hence the object’s identity is less important than its value.
A parenthesized form is an optional expression list enclosed in parentheses:
parenth_form ::= "(" [starred_expression] ")"
A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.
An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).
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.
For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:
either the container contents are listed explicitly, or
they are computed via a set of looping and filtering instructions, called a comprehension.
Common syntax elements for comprehensions are:
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]
Aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.
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.
asynchronous comprehension 是一个值得注意的点,或者说,自从 python3.6 以后,异步协程编程变得越来越普遍。
A generator expression is a compact generator notation in parentheses:
generator_expression ::= "(" expression comp_for ")"
A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.
yield_atom ::= "(" yield_expression ")"
yield_expression ::= "yield" [expression_list | "from" expression]
def gen(): # defines a generator function
yield 123
async def agen(): # defines an asynchronous generator function
yield 123
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.
All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.
When yield from <expr>
is used, it treats the supplied expression as a subiterator.
This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.
Note that calling any of the generator methods below when the generator is already executing raises a ValueError
exception.
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.
This method is normally called implicitly, e.g. by a for
loop, or by the built-in next()
function.
Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send()
method returns the next value yielded by the generator, or raises StopIteration
if the generator exits without yielding another value. When send()
is called to start the generator, it must be called with None
as the argument, because there is no yield expression that could receive the value.
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.
Raises a GeneratorExit
at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit
(by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError
is raised. If the generator raises any other exception, it is propagated to the caller. close()
does nothing if the generator has already exited due to an exception or normal exit.
Here is a simple example that demonstrates the behavior of generators and generator functions:
>>> 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.
yield from:
In [19]: def tt():
...: yield from [1, 2, 3]
In [20]: list(tt())
Out[20]: [1, 2, 3]
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. Thisfinalizer 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 ofasyncio.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.
This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.
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.
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
(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.
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.
Primaries represent the most tightly bound operations of the language. Their syntax is:
primary ::= atom | attributeref | subscription | slicing | call
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.
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.
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 The standard type hierarchy) 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.
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 Function definitions 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 callf(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.
Changed in version 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.
>>> print(*[1], *[2], 3)
1 2 3
>>> dict(**{'x': 1}, y=2, **{'z': 3})
{'x': 1, 'y': 2, 'z': 3}
>>> *range(4), 4
(0, 1, 2, 3, 4)
>>> [*range(4), 4]
[0, 1, 2, 3, 4]
>>> {*range(4), 4}
{0, 1, 2, 3, 4}
>>> {'x': 1, **{'y': 2}}
{'x': 1, 'y': 2}
>>> {'x': 1, **{'x': 2}}
{'x': 2}
>>> {**{'x': 2}, 'x': 1}
{'x': 1}
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 Function definitions. 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 Built-in Functions for the descriptions of built-in functions and methods.
a class object:
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.
总结一下,首先重视表达式这一概念,然后知道表达式由操作符和操作数组成。
针对 Python 来说,最重要也最有用的有:
__getattr__, __setattr__, __getitem__, __setitem__
表达式是任何一门编程语言的基本构件,甚至可以说是最最重要的构件,毕竟 lambda calculus 可以推演一切呢。