A Quick Note on Python Iterators

An iterator is an object that contains a countable number of values. In Python, an iterator is an object that can be iterated upon, meaning that you can traverse through all the values. Technically, in Python, an iterator is an object which implements the iterator protocol, which consists of the methods __iter__() and __next__().

Iterables vs. Iterators

An iterable is any object that can be looped over, like a list, tuple, or string. You can get an iterator from an iterable by using the iter() function.

An iterator is an object that does the iterating. It produces the next value in the sequence when you call the next() function on it.

The iter() and next() Functions

Let’s see how iter() and next() work together.

my_list = ["apple", "banana", "cherry"]
my_iterator = iter(my_list)

print(next(my_iterator))  # 'apple'
print(next(my_iterator))  # 'banana'
print(next(my_iterator))  # 'cherry'

# This would raise a StopIteration exception
# print(next(my_iterator))

Looping Through an Iterator

The for loop in Python automatically creates an iterator from an iterable and calls next() on it until a StopIteration exception is raised.

my_list = ["apple", "banana", "cherry"]

for fruit in my_list:
    print(fruit)

This is what happens behind the scenes in a for loop.

Creating a Custom Iterator

To create your own iterator, you need to implement the __iter__() and __next__() methods in your class.

class MyNumbers:
    def __iter__(self):
        self.a = 1
        return self

    def __next__(self):
        if self.a <= 5:
            x = self.a
            self.a += 1
            return x
        else:
            raise StopIteration

my_class = MyNumbers()
my_iter = iter(my_class)

for x in my_iter:
    print(x)

Conclusion

Iterators are a fundamental concept in Python that provide a clean and efficient way to work with sequences of data. They are the underlying mechanism that powers for loops and other iterable operations. By understanding how iterators work, you can write more memory-efficient and Pythonic code, especially when dealing with large datasets.