Module 14 - Iterators & Generators

Iterators and generators are powerful Python concepts for efficient iteration and lazy evaluation. Understanding them is key to writing memory-efficient, Pythonic code.

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1. Understanding Iteration

1.1 Iterables vs Iterators

# Iterable: Object that can be iterated over
my_list = [1, 2, 3]  # list is iterable

# Iterator: Object that implements __iter__() and __next__()
my_iter = iter(my_list)

# Manual iteration
print(next(my_iter))  # 1
print(next(my_iter))  # 2
print(next(my_iter))  # 3
# print(next(my_iter))  # StopIteration error

1.2 Creating Custom Iterators

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

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

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2. Generators

2.1 Generator Functions

def countdown(n):
    """Generator that counts down from n to 1"""
    while n > 0:
        yield n
        n -= 1

# Usage
for num in countdown(5):
    print(num)

# Generators are iterators
gen = countdown(3)
print(next(gen))  # 3
print(next(gen))  # 2

2.2 Generator Expressions

# List comprehension (creates full list in memory)
squares_list = [x**2 for x in range(1000000)]

# Generator expression (lazy evaluation)
squares_gen = (x**2 for x in range(1000000))

# Memory efficient iteration
for square in squares_gen:
    if square > 100:
        break

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Summary

✅ Iterators implement __iter__() and __next__()
✅ Generators use yield for lazy evaluation
✅ Generator expressions save memory
✅ Use generators for large data sets

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Practice

1. Create a Fibonacci generator
2. Build an infinite sequence generator
3. Write a generator that reads large files line by line