Control Flow: Iterators, List Comprehensions & Generator Expressions

1Concept

List comprehensions [x for x in seq] provide concise mapping/filtering. Generators (using yield or (x for x in seq)) create lazy iterators evaluated on-demand in O(1) memory.

2Architecture Diagram

List Comprehension [x*2 for x in seq]   ──► Allocates full list in RAM immediately
Generator Expression (x*2 for x in seq) ──► Lazily yields items 1-by-1 (O(1) memory!)

3Code Example

# List comprehension (Eager)
squares = [x ** 2 for x in range(1, 6) if x % 2 == 1]
print(f"Odd squares list: {squares}")

# Generator function (Lazy)
def fibonacci(limit):
    a, b = 0, 1
    for _ in range(limit):
        yield a
        a, b = b, a + b

print("Fibonacci generator sequence:")
for num in fibonacci(7):
    print(num, end=" ")
print()

4Expected Output

Odd squares list: [1, 9, 25]
Fibonacci generator sequence:
0 1 1 2 3 5 8 

5Key Takeaways

• ✓Use generator expressions for large datasets to prevent RAM exhaustion.
• ✓for-loops in Python support an optional 'else:' block that executes if no break occurred.
• ✓yield pauses execution and preserves function stack frame state.