Python iterator Generator

Python iterator Generator
1. The iterator is a way to access collection elements. The iterator object is accessed from the first element of the Set, knowing that all elements have been accessed. The iterator can only move forward without moving back, but there is nothing to do with it, because people seldom go back on the way of iteration. 1.1 The Advantages of the iterator are not advantageous for data structures (such as tuple and list) that support random access from the native. The iterator has no advantage over index access from the classic for loop, instead, the index value is lost (you can use the built-in function enumerate () to retrieve the index value ). However, for data structures (such as set) that cannot be accessed randomly, the iterator is the only way to access elements. In addition, the major advantage of the iterator is that it is not required to prepare all elements in the entire iteration process in advance. The iterator calculates an element only when it iterates to an element. Before or after this, the element may not exist or be destroyed. This feature makes it especially suitable for Traversing large or infinite sets, such as several G files or Fibonacci series. The primary benefit of the iterator is that it provides a unified interface for accessing the set. As long as the _ iter _ () method object is defined, the iterator can be used for access. The iterator has two basic methods: the next method: return the next element of the iterator _ iter _ method: return the iterator object itself. The following uses the generation of the Fibonacci series as an example, explain why to use iterator code 1 to copy code def fab (max): n, a, B = 0, 0, 1 while n <max: print B a, B = B, a + B n = n + 1 copying Code directly using print in the function fab (max) will lead to poor reusability of the function, because the fab returns None. Other functions cannot obtain the series returned by the fab function. Code 2 copy the code def fab (max): L = [] n, a, B = 0, 0, 1 while n <max: L. append (B) a, B = B, a + B n = n + 1 return L copy Code 2 meets reusability requirements, but occupies the memory space, it is best not. Code 3 compares for I in range (1000): pass for I in xrange (1000): the first one returns a list of 1000 elements, and the other one returns an element in each iteration, therefore, the iterator can be used to solve the problem of reusing the space occupied. Copy the code class Fab (object): def _ init _ (self, max): self. max = max self. n, self. a, self. B = 0, 0, 1 def _ iter _ (self): return self def next (self): if self. n <self. max: r = self. B self. a, self. B = self. b, self. a + self. B self. n = self. n + 1 return r raise StopIteration () Execution >>> for key In Fabs (5): the print key Fabs class continuously returns the next number of columns through next (), and the memory usage is always constant 1.2. Use the iterator to use the built-in factory function iter (iterable) you can get the iterator object: >>> lst = range (5) >>> it = iter (lst) >>>> it <listiterator object at 0x01A63110> use next () method to access the next element: >>> it. next () >>> it. next () >>> it. next () python throws a StopIteration exception when handling the iterator out of bounds> it. next () >>> it. next <method-wrapper 'Next' of listiterator object at 0x01A63110 >>> it. next () >>> it. next () Traceback (Most recent call last): File "<pyshell #27>", line 1, in <module> it. next () StopIteration understand StopIteration. You can use the iterator to traverse the copy code lst = range (5) it = iter (lst) try: while True: val = it. next () print valexcept StopIteration: pass to copy the code result >>> in fact, because the iterator is so common, python specifically makes the iterator syntax sugar for the for keyword. In the for loop, Python will automatically call the factory function iter () to obtain the iterator, call next () to obtain the element, and check for StopIteration exceptions. Copy the following code >>> a = (1, 2, 3, 4) >>> for key in: print key copy code first, python calls the iter function iterator for the object after the keyword in, and then calls the next method of the iterator to obtain the element until a StopIteration exception is thrown. 1.3 define the next example of the iterator-the Fibonacci series copy Code #-*-coding: cp936-*-class Fabs (object): def _ init _ (self, max ): self. max = max self. n, self. a, self. B = 0, 0, 1 # Special Note: 0th items are 0, 1st items are the first 1. the entire series starts from 1 def _ iter _ (self): return self def next (self): if self. n <self. max: r = self. B self. a, self. B = self. b, self. a + self. B self. n = self. n + 1 return r raise StopIteration () print Fabs (5) for key in Fabs (5): print key copy code result <__ Main __. fabs object at 0x01A63090> 2. functions with yield in the iterator are called generator (generator) in Python. Several examples show that code 3 is far less concise than Code 1, the generator (yield) not only keeps code 1 concise, but also keeps code 3 effective. Code 4 def fab (max): n, a, B = 0, 0, 1 while n <max: yield B a, B = B, a + B n = 1 run >>> for n in fab (5): print n in simple words, yield is used to convert a function into a generator. A function with yield is no longer a common function. The Python interpreter regards it as a generator and calls fab (5) instead of executing the fab function, an iterable object is returned! During for loop execution, code in the fab function is executed in each loop. When yield B is executed, the fab function returns an iteration value. During the next iteration, the Code continues to run from the next statement of yield B, and the local variable of the function looks exactly the same as before the last interrupted execution, so the function continues to run until yield is encountered again. It seems that a function is interrupted several times by yield during normal execution, and the current iteration value is returned through yield for each interruption. You can also manually call the next () method of fab (5) (because fab (5) is a generator object and this object has the next () method ), in this way, we can see more clearly the fab Execution Process: >>> f = fab (3) >>> f. next () 1 >>> f. next () 1 >>> f. next () 2> f. next () Traceback (most recent call last): File "<pyshell #62>", line 1, in <module> f. next () StopIterationreturn is used in a generator. If no return is returned, the function is executed by default. If return is returned during execution, the StopIteration terminate iteration is directly thrown. For example, >>> s = fab (5) >>> s. next () 1 >>> s. next () Traceback (most recent call last): File "<pyshell #66>", line 1, in <module> s. next () StopIteration Code 5 file read copy code def read_file (fpath): BLOCK_SIZE = 1024 with open (fpath, 'rb') as f: while True: block = f. read (BLOCK_SIZE) if block: yield block else: return copy the Code. if you call the read () method directly on the file object, unpredictable memory usage may occur. A good way is to use a fixed-length buffer to constantly read the file content. With yield, you can easily read files without having to write iteration classes for reading files.