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The iterator pattern is a classic software design pattern. Now many programming languages have built-in this design pattern. Among Python's primitive data types, those that can be used for for loops are iterable types. Of course, you can also use the iter function to obtain the corresponding iterator and then traverse the object. For example, the following code:
l = [1, 3] # 可迭代对象 __iter__t = iter(l) #获取迭代器对象print(t.__next__()) print(t.__next__())# print(t.__next__()) # 报异常复制代码
To implement an iterable object, you must first implement the corresponding iterator object. To implement an iterator in Python, you only need to implement the __next__ method. However, the Iterator class in the collections package defines the __next__ method as an abstract method. The author believes that in view of the readability of the program, you may wish to inherit the Iterator class when implementing an iterator.
from random import samplefrom collections import Iterable, Iteratorclass WeatherIterator(Iterator): def __init__(self, cities): self.cities = cities self.index = 0 def getWeather(self, city): return (city, sample(['sun','wind','yu'], 1)[0]) def __next__(self): if self.index == len(self.cities): raise StopIteration city = self.cities[self.index] self.index += 1 return self.getWeather(city)复制代码
To implement an iterable object, you only need to implement the __iter__ method. Similarly, the Iterable class in the collections package also defines the __iter__ method as an abstract class.
from collections import Iterableclass WeatherIterable(Iterable): def __init__(self, cities): self.cities = cities self.index = 0 def __iter__(self): return WeatherIterator(self.cities)复制代码
This way you can use a for loop to iterate.
for weather in WeatherIterable(['北京', '上海', '广州']): print(weather)复制代码
First look at the following code:
def gen(): print("step 1") yield 1 print("step 2") yield 2 print("step 3") yield 3复制代码
The return value of the above gen function is a generator object.
g = gen() g.__next__() print(g.__next__()) print(g.__next__())复制代码
As shown in the above code, every time the __next__ method of the generator is called, it will execute a gen function until it encounters the yield keyword, and return what follows. Therefore, a generator can be understood as a function that can be interrupted.
Note: Generator objects are also iterable objects.
for x in g: print(x)复制代码
By implementing the __iter__ method as a generator function, you can implement an iterable object.
class PrimeNumbers: def __init__(self, start, end): self.start = start self.end = end def isPrimeNum(self, k): #判断是否是素数 if k < 2: return False for i in range(2, k): if k % i == 0: return False return True def __iter__(self): for k in range(self.start, self.end + 1): if self.isPrimeNum(k): yield kfor num in PrimeNumbers(2, 100): print(num)复制代码
The iter function can obtain the forward iterator of the iterable object, and the reversed function can obtain the reverse iterator of the iterable object. iterator.
l = [1, 2, 3, 4, 5]for x in reversed(l): print(x)复制代码
To implement reverse iteration, just implement the __reversed__ method.
class FloatRange: def __init__(self, start, end, step=0.1): self.start = start self.end = end self.step = step def __iter__(self): t = self.start while t <= self.end: yield t t += self.step def __reversed__(self): t = self.end while t >= self.start: yield t t -= self.stepfor x in FloatRange(1.0, 4.0, 0.5): print(x)for x in reversed(FloatRange(1.0, 4.0, 0.5)): print(x)复制代码
The islice function in the itertools package can perform slicing operations on iterable objects.
from itertools import islicefor x in islice(FloatRange(1.0, 4.0, 0.5), 2, 5): print(x)复制代码
Use the zip method to form a tuple of corresponding elements.
for w, e, m in zip([1, 2, 3, 4], ('a', 'b', 'c','d'), [5, 6, 7, 8]): print(w, e, m)复制代码
Use the chain function in the itertools package to concatenate multiple iterable objects. Use the zip method to form a tuple of corresponding elements.
from itertools import chainfor x in chain([1, 2, 3, 4], ('a', 'b', 'c','d')): print(x)复制代码
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