Python Iterator | Docs With Examples

Iterators in Python provide a way to traverse through a sequence (such as lists, tuples, or strings) one element at a time. They are a fundamental part of Python’s iteration protocol.

What is an Iterator?

An iterator is an object in Python that implements the __iter__() and __next__() methods.

• __iter__() returns the iterator object itself.
• __next__() returns the next element in the sequence and raises StopIteration when there are no more elements.

Creating an Iterator

You can create an iterator by defining a class with __iter__() and __next__() methods.

class Counter:
    def __init__(self, start, end):
        self.current = start
        self.end = end
    
    def __iter__(self):
        return self
    
    def __next__(self):
        if self.current > self.end:
            raise StopIteration
        value = self.current
        self.current += 1
        return value

counter = Counter(1, 5)
for num in counter:
    print(num)

Output:

1
2
3
4
5

Using Built-in Iterators

Python provides built-in iterators for iterable objects like lists, tuples, and strings.

my_list = [10, 20, 30]
iterator = iter(my_list)
print(next(iterator))  # 10
print(next(iterator))  # 20
print(next(iterator))  # 30

Iterators vs. Iterables

• An iterable is an object that can return an iterator (e.g., lists, tuples, sets).
• An iterator is an object that keeps state and produces the next value when next() is called.

You can check if an object is an iterator using iter(), and get the next value using next().

Generator Functions (Simpler Iterators)

Generators simplify iterator creation using yield instead of return.

def count_up(start, end):
    while start <= end:
        yield start
        start += 1

for num in count_up(1, 5):
    print(num)

Key Takeaways

• Iterators allow sequential data traversal in your Python projects using __iter__() and __next__().
• Python’s built-in objects like lists and tuples support iteration.
• Generators provide a simpler way to create iterators using yield.

Practice Exercise

Here's a simple challenge, open up your Python editor and try to create a custom iterator that iterates through even numbers:

class EvenNumbers:
    def __init__(self, start, end):
        self.current = start if start % 2 == 0 else start + 1
        self.end = end
    
    def __iter__(self):
        return self
    
    def __next__(self):
        if self.current > self.end:
            raise StopIteration
        value = self.current
        self.current += 2
        return value

for num in EvenNumbers(2, 10):
    print(num)

Wrapping Up

Understanding iterators is key to writing efficient loops and handling large datasets. Whether using built-in iterators or creating custom ones, mastering iteration helps streamline data processing. Happy coding!