Python Generators

What is a Generator?

A generator is a special type of iterator — created using a function with the yield keyword instead of return. It was introduced to make creating iterators simpler and more memory-efficient.

The yield Keyword

• Like return, it sends a value back to the caller
• Unlike return, it pauses the function and preserves its internal state
• When next() is called again, execution resumes from where it left off

def gen():
    yield 1
    yield 2
    yield 3

itr = gen()             # returns a generator object (not the result)

print(type(gen))        # <class 'function'>
print(type(itr))        # <class 'generator'>

print(next(itr))        # 1
print(next(itr))        # 2
print(next(itr))        # 3
print(next(itr))        # ❌ StopIteration

Execution flow: 1. First next() → runs until yield 1, pauses, returns 1 2. Second next() → resumes, runs until yield 2, pauses, returns 2 3. Third next() → resumes, runs until yield 3, pauses, returns 3 4. Fourth next() → no more yield → raises StopIteration

Looping Through a Generator

Since a generator is an iterator, you can use it in a for loop:

for i in gen():
    print(i)   # 1  2  3

Practical Example — Fibonacci Sequence

Traditional approach (stores everything in a list ⚠️)

fib_numbers = [1, 1]
for i in range(2, 10):
    last = fib_numbers[i - 1]
    second_last = fib_numbers[i - 2]
    fib_numbers.append(last + second_last)

print(fib_numbers)

Problem: stores every number in memory. For 10 million terms, the list becomes huge.

Generator approach (stores only what's needed ✅)

def fib(n):
    last = 1
    second_last = 1
    current = 3          # first two terms (1, 1) are already known

    while current <= n:
        num = last + second_last
        yield num
        second_last = last
        last = num
        current += 1

for val in fib(10):
    print(val)   # 2 3 5 8 13 21 34 ...

Only two variables (last, second_last) are stored at any time — not the whole sequence.

Step-by-step for the first two iterations:

Why Use a Generator?

Key insight: A generator doesn't store the whole sequence — it computes the next value on demand. This makes it ideal for infinite or very large sequences.

Key Takeaways & Recap