第17条:在参数上面迭代时，要多加小心

实验中用的 init.py

15
35
80

def read_visits(data_path):
    with open(data_path) as f:
        for line in f:
            yield int(line)

def normalize_copy(numbers):
    numbers = list(numbers)
    total = sum(numbers)
    result = []
    for value in numbers:
        percent = 100 * value / total
        result.append(percent)
    return result

it = read_visits('__init__.py')
percentages = normalize_copy(it)
print(percentages)

输出: 
[11.538461538461538，26.923076923076923，61.53846153846154]

2.传入函数

def normalize_func(get_iter):
    total = sum(get_iter())
    result = []
    for value in get_iter():#New iterator
        percent = 100 * value / total
        result.append(percent)
    return result
rcentages = normalize_func(lambda: read_visits('__init__.py'))
print(percentages)

2. 类的迭代器

class ReadVisits(object):
    def __init__(self,data_path):
        self.data_path = data_path

    def __iter__(self):
        with open(self.data_path) as f:
            for line in f:
                yield int(line)
visits = ReadVisits('__init__.py')
percentages = list(visits)
print(percentages)
输出:
[15, 35, 80]

3.限定必须传入容器对象

def normalize_defensive(numbers):  
    if iter(numbers) is iter(numbers):  # An iterator -- bad!
        raise TypeError('Must supply a container')
    total = sum(numbers)
    result = []
    for value in numbers:
        percent = 100 * value / total
        result.append(percent)
    return result

visits = [15,35,80]
normalize_defensive(visits)
visits = ReadVisits('__init__.py')
a = normalize_defensive(visits)
print('a',a)
输出:a [11.538461538461538, 26.923076923076923, 61.53846153846154]

• 把_iter._方法实现为生成器，即可定义自己的容器类型。
• 想判断某个值是迭代器还是容器，可以拿该值为参数，两次调用iter函数，若结果相同，则是迭代器，调用内置的next函数，即可令该迭代器前进一步。