Iterators

Iterator is an object that can iterate (in loop) through object like lists, tuples, dicts, and sets.

Iterate over an object means traverse through the values of these object.

In python, iterator object implements in iterator protocol, which have __iter__() and __next__() (next in Python 2).

If you want any object to be an iterator. Then you must implement following method.

• __iter__: __iter__ method is called the initializer of an iterator object. It return object that has
  
  __next__ (nextin Python 2) method.

• __next__: __next__ method return the next value for iterable. For loop implicitly call next on iterator object.
  
  This method should raise a StopIteration to signal the end of the iteration.

iterable is an object which one iterate over. It generates an iterator when passed to iter() method.
iterator is an object, which is used to iterate over iterable object using next method.

Iterator have __next__ which return the next value of the iterable object.

name_tuple = ('John', 'Doe', 'Marry')
name_iterator = iter(name_tuple)

print(next(name_iterator))
print(next(name_iterator))
print(next(name_iterator))

Output

John
Doe
Marry

We create aiterator type that iterates from 0 to limit. For example, if we set limit 5 then it prints 1, 2, 3, 4, 5.

class CustomIterator:  
    def __init__(self, limit):  
        self.limit = limit  

      # called when iteration is initialized  
    def __iter__(self):  
        self.x = 0  
        return self  

      # move next value  
    def __next__(self):  
        x = self.x  
        if x > self.limit:  
            raise StopIteration  
        self.x = x + 1  
        return x  


for i in CustomIterator(5):  
    print(i)

Output

0
1
2
3
4
5

myclass = CustomIterator(5)
myitr = iter(myclass)

print(next(myitr))
print(next(myitr))
print(next(myitr))
print(next(myitr))
print(next(myitr))

Output

0
1
2
3
4

Some of built in iterations: lists, sets, dicts, and tuples.

Generators

Generator is a simpler way to create iterators. Iterators methods automatically handled by generators in python.

Generator returns an object(iterator) which we can iterate over (one value at a time).

We can create generator by defining a normal function with yield statement istead of return statement.

return statement terminate function entirely, yeild statement pause the functional and saving all states and later continues from there succescive calls.

def infinite_seq():
    num  = 0
    while True:
        yield num
        num += 1

x = infinite_seq()
print(x.__next__())
print(x.__next__())
print(x.__next__())
print(x.__next__())
print(x.__next__())

Output

0
1
2
3
4

yeild statement iterates where a value is sent back to the caller, but unlike return, you don't exit function afterward.

The state of generator function is remembered. When __next__ object called for generator object, the previously yielded variable num is incremented and then yeilded again.

Generator expressions creates an anonymous generator function.

Generator expression as follows

my_list = [1, 2, 3]
>>> (x**2 for x in my_list)
<generator object <genexpr> at 0x03DE6488>
>>>  sum((x**2 for x in my_list))
14

Generator Performance

>>> import sys
>>> num_list = [i**2 for i in range(100000)]
>>> sys.getsizeof(num_list)
412228
>>> num_list = (i**2 for i in range(100000))
>>> print(sys.getsizeof(num_list))
56

Generator is useful for reading large files. It's memory efficient.