Python Iterators and Generators

What is an Iterator in Python?

In Python, an iterator is an object that allows sequential access to elements in a collection without using indexing. An object is an iterator if it implements the following two methods:

• __iter__() → returns the iterator object itself
• __next__() → returns the next item or raises StopIteration when items are exhausted

Example: Custom Iterator in Python

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:
            num = self.current
            self.current += 1
            return num
        else:
            raise StopIteration

counter = CountUpTo(3)

for num in counter:
    print(num)

Output:

1
2
3

Built-in Iterators

Many built-in types like lists, tuples, sets, strings, etc., are iterable and can be converted to iterators using the iter() function.

my_list = [10, 20, 30]
it = iter(my_list)

print(next(it))  # 10
print(next(it))  # 20
print(next(it))  # 30
# print(next(it))  # Raises StopIteration

What is a Generator in Python?

A generator is a simplified iterator. It allows you to create an iterator in a much more concise way using the yield keyword. Generators maintain their state between calls and do not store the entire sequence in memory — making them memory-efficient.

Example: Generator Function in Python

def count_up_to(max):
    current = 1
    while current <= max:
        yield current
        current += 1

gen = count_up_to(3)

for num in gen:
    print(num)

Output:

1
2
3

This behaves like an iterator without needing __iter__() and __next__() methods.

Generator vs Iterator in Python

Generator Expression (Like List Comprehension)

Just like list comprehensions, you can create a generator in one line using parentheses ():

gen_exp = (x*x for x in range(4))

for num in gen_exp:
    print(num)

Output:

0
1
4
9

Advantages of Generators

• Lazy evaluation (values are computed on demand)
• Low memory footprint
• Ideal for processing large data streams
• Easily pause and resume execution

Real-World Example: Reading Large File Using Generator

def read_large_file(file_path):
    with open(file_path, "r") as file:
        for line in file:
            yield line.strip()

# for line in read_large_file("bigdata.txt"):
#     process(line)