python iterable iterator iter()

iterable:
An object capable of returning its members one at a time. Examples of iterables include all sequence types (such as list, str, and tuple) and some non-sequence types like dict 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:

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.

More information can be found in Iterator Types.

iter(o[, sentinel])

Return an iterator object. The first argument is interpreted very differently depending on the presence of the second argument. Without a second argument, o must be a collection object which supports the iteration protocol (the __iter__()method), or it must support the sequence protocol (the __getitem__() method with integer arguments starting at 0). If it does not support either of those protocols, TypeError is raised. If the second argument, sentinel, is given, then o must be a callable object. The iterator created in this case will call o with no arguments for each call to its next()method; if the value returned is equal to sentinel, StopIteration will be raised, otherwise the value will be returned.

One useful application of the second form of iter() is to read lines of a file until a certain line is reached. The following example reads a file until the readline() method returns an empty string:

with open('mydata.txt') as fp:
    for line in iter(fp.readline, ''=>sentinel):
        process_line(line)

New in version 2.2

 

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

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

>>>

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

yield

The yield statement is only used when defining a generator function, and is only used in the body of the generator function. Using a yield statement in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.

When a generator function is called, it returns an iterator known as a generator iterator, or more commonly, a generator. The body of the generator function is executed by calling the generator’s next()[ every iterator has a next() function ] method repeatedly until it raises an exception.

When a yield statement is executed, the state of the generator is frozen and the value of expression_list is returned tonext()‘s caller. By “frozen” we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, and the internal evaluation stack: enough information is saved so that the next time next() is invoked, the function can proceed exactly as if the yield statement were just another external call.

As of Python version 2.5, the yield statement is now allowed in the try clause of a try ... finally construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

For full details of yield semantics, refer to the Yield expressions section.

Note

In Python 2.2, the yield statement was only allowed when the generators feature has been enabled. This __future__import statement was used to enable the feature:

from __future__ import generators

 

 

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so:

(1). so iterator represent an stream object, every iterator has a "next()" function

(2). iter() is a build-in function that return an iterator object, whose parameters must be an iterable object(implement the interating protocal(like: _iter__, _getitem_)), or a callable function(the ge above)

(3). generator is a function, and also return an iterator(the iterator also have a next() function) known as the "generator iterator"

(4).every generator function has a yield statement => every function that has a yield statement, makes this function to be a generator,=> yield statement makes an normal function to a generator function that returns iterator object