Iterators and Generators

 In Python, iterators and generators are tools for working with sequences of data one item at a time, which is very memory-efficient—especially for large data sets.

What is an iterator?

An iterator is an object that implements:

• __iter__() → returns the iterator itself

• __next__() → returns the next item or raises StopIteration when done

nums = [1, 2, 3]

it = iter(nums)

print(next(it))  # 1

print(next(it))  # 2

print(next(it))  # 3

print(next(it))  # Raises StopIteration

# The StopIteration exception is raised when there are no more items to iterate over.

# This is a built-in exception in Python that indicates the end of an iterator.

class CountUpTo:

    def __init__(self, max):

        self.max = max

        self.current = 1

    def __iter__(self):

        return self

    def __next__(self):

        if self.current > self.max:

            raise StopIteration

        num = self.current

        self.current += 1

        return num

counter = CountUpTo(3)

for num in counter:

    print(num)

# # This is a custom iterator that counts up to a specified maximum value.

# # The __iter__ method returns the iterator object itself, and the __next__ method

# # returns the next value in the sequence.

# # When the maximum value is reached, a StopIteration exception is raised.

# # This is a common pattern in Python for creating custom iterators.

What is GENERATORS

A generator is a simpler way to create an iterator using:

• A function with yield (instead of return)

• It automatically creates an iterator object

def count_up_to(max):

    current = 1

    while current <= max:

        yield current

        current += 1

for num in count_up_to(3):

    print(num)