Generators and Iterators in Python

Iterators and Generators in Python are used to iterate over data efficiently. They help in handling large datasets by producing values one at a time instead of storing everything in memory.

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What is an Iterator?

An iterator is an object that contains a countable number of values and can be traversed one by one.

An iterator must implement two methods:

• __iter__()
• __next__()

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Iterator Example

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

print(next(iterator))
print(next(iterator))
print(next(iterator))

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StopIteration Exception

When there are no more values, Python raises a StopIteration exception.

print(next(iterator))  # Raises StopIteration

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Creating a Custom Iterator

class Count:
    def __init__(self, max):
        self.num = 1
        self.max = max

    def __iter__(self):
        return self

    def __next__(self):
        if self.num <= self.max:
            value = self.num
            self.num += 1
            return value
        else:
            raise StopIteration

counter = Count(3)

for i in counter:
    print(i)

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What is a Generator?

A generator is a special type of iterator created using a function and the yield keyword.

Generators automatically handle __iter__() and __next__().

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Generator Function Example

def my_generator():
    yield 1
    yield 2
    yield 3

gen = my_generator()

print(next(gen))
print(next(gen))
print(next(gen))

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Generator with Loop

def count_up(max):
    num = 1
    while num <= max:
        yield num
        num += 1

for i in count_up(5):
    print(i)

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Difference Between Iterator and Generator

Iterator	Generator
Uses __iter__() and __next__()	Uses yield keyword
More code required	Less and cleaner code
Manual implementation	Automatic implementation

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Generator Expression

Generator expressions are similar to list comprehensions but use parentheses instead of square brackets.

gen = (x * x for x in range(5))

for i in gen:
    print(i)

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Memory Efficiency

# List comprehension
list_data = [x for x in range(1000000)]

# Generator expression
gen_data = (x for x in range(1000000))

Generators consume much less memory than lists.

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Generators and Iterators in Automation Testing

def test_data():
    yield ("user1", "pass1")
    yield ("user2", "pass2")

for username, password in test_data():
    print(username, password)

Used in:

• Reading large test data
• API pagination handling
• Log file processing

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Best Practices

• Use generators for large datasets
• Prefer generators over lists when possible
• Handle StopIteration properly

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Common Interview Questions

• What is an iterator?
• What is a generator?
• Difference between yield and return?
• What is StopIteration?
• Generator vs list comprehension?

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Conclusion

Generators and iterators help write memory-efficient and scalable Python code. They are widely used in data processing, automation testing, and real-world applications.

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