Iterators and Iterables

Iterators and iterables are fundamental concepts in Python that allow you to work with sequences of data. They are the foundation of loops, comprehensions, and generators.

In this lesson, you’ll learn how iterators and iterables work and how to create your own iterators.

An iterable is any object in Python that can be looped over using a for loop. Examples include lists, tuples, sets, and dictionaries.

# Example of an iterable
my_list = [1, 2, 3]
for item in my_list:
    print(item)  # Outputs: 1, 2, 3

An iterator is an object that represents a stream of data. It implements the __iter__() and __next__() methods.

# Example of an iterator
my_list = [1, 2, 3]
iterator = iter(my_list)
print(next(iterator))  # Outputs: 1
print(next(iterator))  # Outputs: 2
print(next(iterator))  # Outputs: 3

Iterables can be looped over (e.g., lists), while iterators produce one item at a time using the next() method. An iterable becomes an iterator when passed to the iter() function.

You can create custom iterators by defining a class with __iter__() and __next__() methods:

# Custom iterator example
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
        self.current += 1
        return self.current - 1

counter = Counter(1, 5)
for number in counter:
    print(number)  # Outputs: 1, 2, 3, 4, 5

The iter() function returns an iterator object from an iterable:

# Using iter() on a list
my_list = [1, 2, 3]
iterator = iter(my_list)
print(next(iterator))  # Outputs: 1

When an iterator is exhausted, it raises a StopIteration exception. This is how Python signals the end of a sequence.

# Handling StopIteration
my_list = [1, 2, 3]
iterator = iter(my_list)

while True:
    try:
        print(next(iterator))
    except StopIteration:
        break

Generators are a simpler way to create iterators. They automatically implement the __iter__() and __next__() methods.

# Example of a generator as an iterator
def my_generator():
    yield 1
    yield 2
    yield 3

for value in my_generator():
    print(value)  # Outputs: 1, 2, 3

Here are some mistakes to avoid:

• Forgetting to implement __iter__() and __next__() in custom iterators.
• Not handling StopIteration exceptions properly.
• Confusing iterators with iterables.

In this lesson, you learned:

• The difference between iterables and iterators.
• How to use __iter__() and __next__() to create custom iterators.
• How to use the iter() function and handle StopIteration exceptions.
• How generators simplify the creation of iterators.

Great job! Let’s move to the next topic.