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
next in Python 2).
If you want any object to be an iterator. Then you must implement following method.
__iter__method is called the initializer of an iterator object. It return object that has
nextin Python 2) method.
__next__method return the next value for iterable. For loop implicitly call
nexton iterator object.
This method should raise a
StopIterationto signal the end of the iteration.
iterable is an object which one iterate over. It generates an iterator when passed to
iterator is an object, which is used to iterate over iterable object using
__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))
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)
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))
0 1 2 3 4
Some of built in iterations: lists, sets, dicts, and tuples.
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 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__())
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
>>> 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.