语法 - python的生成器和迭代器问题

Answer 1

Iterator and Generator are two concepts. Conceptually, they have no overlap, that is, there is no similarity. 迭代器与生成器是两个概念，在概念上，他们没有重叠，也就是说，没有相同点。

我简单概述一下：

• 迭代器，其实就是可以用来迭代的容器，比如，一个list，tuple等.

• 生成器，这里的生成指得是它可以产生迭代器.

下面是demo:

foods = ['banana', 'hot dog', 'dumpling']
for food in foods:
    print food

这里的foods就是一个迭代器.

def send_file(file_path, size):
    with open(file_path) as f:
        block = f.read(BLOCK_SIZE)
        while block:
            yield block
            block = f.read(BLOCK_SIZE)

这是一个生成器，注意，生成器的重点就是yield，这里的block就是生成器每次生成的值，可以暂时理解为相当于迭代器中的单个元素，比如foods中的banana元素，而生成器的主要作用就是把大内存占用数据分次写入内存

Let me give a brief overview:

• An iterator is actually a container that can be used to iterate, such as a list, tuple, etc.🎜
• 🎜Generator, here Generate means that it can generate Iterator.🎜

🎜Here is the demo:🎜 rrreee 🎜foods here is an iterator.🎜 rrreee 🎜This is a generator. Note that the focus of generator is yield, and the block here is the generator. The value generated each time can be temporarily understood as equivalent to a single element in Iterator, such as the banana element in foods, and the generator The main function is to write large memory occupied data into the memory in batches. The advantage of this is that it does not need to read all the data into the program at once to occupy a large amount of memory, and it can be used without affecting the use. In this case, it prevents memory leaks, thereby giving program users a better experience and making your code more elegant (ge).🎜 🎜As for the usage scenarios, it has actually been mentioned above. If you want to know more specifically, then like me:)🎜

Answer 2

In python, you can place the iterator in the for statement and add the iterator feature to an object to meet the above requirements; here is an example of mine. We usually need to use memory mapped files to open a large file. However, the standard mmap object does not support iteration, which means that the following code cannot be executed:

for line in mmap.open(file_name):
    print line

If you want to achieve this effect, you need to use iterators, see the iter and next functions below;

class Mmap(object):

    __slots__ = ['os', 'mmap', 'handler', 'mhandler']

    def __init__(self, fname):
        self.os = __import__('os', fromlist=['path'])
        self.mmap = __import__('mmap')
        assert self.os.path.exists(fname), 'File(%s) Not Exists !' % fname
        self.handler = open(fname)

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.mhandler.close()
        self.handler.close()
        del self.os
        del self.mmap
        
class Reader(Mmap):
    '''使用mmap打开文件，并添加iterable特性'''

    def __init__(self, fname):
        super(Reader, self).__init__(fname)
        self.mhandler = self.mmap.mmap(self.handler.fileno(), 0, prot=self.mmap.PROT_READ)


    def __iter__(self):
        return self

    def next(self):
        line = self.mhandler.readline()
        if line:
            return line
        else:
            raise StopIteration

if __name__ == '__main__':
    with Reader('./demo/simple.dat') as f:  #现在就可以了
        for line in f:
            print line.strip() 

Regarding generators, you need to use yield or generator expressions;

The application scenarios of generators are mainly in lazy evaluation and data generation. Assume that in a certain logic, you need 0~3 million random numbers, you have two options:

1. Generate 3 million random numbers at one time and place them in a list or somewhere.

2. Temporarily generate one each time you need (use generator)

def next_random_num():
    while True:
        yield random.randint()
        
for line in open('txtfile'):
    print next_random_num + int(line.strip())

In the above code, how many random numbers are temporarily generated according to the number of lines in the file;