generators.py

# Generators — memory-efficient iterables using the yield keyword.
#
# A normal function builds and returns a complete list.
# A generator function yields values one at a time, generating each on demand.
# This avoids storing the entire sequence in memory.


# --- Normal function returning a list ---

print('Normal function:')

def squares():
    result = []
    for x in range(10):
        result.append(x ** 2)
    return result

print(squares())


# --- Generator function using yield ---

print('\nGenerator function:')

def squares_gen():
    for x in range(10):
        yield x ** 2

print(squares_gen())    # <generator object> — not the list itself

for x in squares_gen():
    print(x, end=' ')
print()


# --- Generator expression ---
# Like a list comprehension but with round brackets.
# (Round brackets are not used for tuple comprehensions — they produce generators.)

print('\nGenerator expression:')

squares_exp = (x ** 2 for x in range(10))
print(squares_exp)    # <generator object>

for x in squares_exp:
    print(x, end=' ')
print()


# --- Processing a large range ---
# A list of 1,000,000 items uses significant memory.
# A generator processes each value without storing the sequence.

print('\nLarge range (first 10):')

def large_range_gen():
    for x in range(1_000_000):
        yield x ** 2

for x in large_range_gen():
    if x >= 100:
        break
    print(x, end=' ')
print()


# --- Infinite sequence ---
# Impossible with a list — a generator has no problem.

print('\nInfinite sequence (first 10):')

def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

for i, x in enumerate(infinite_sequence()):
    print(x, end=' ')
    if i >= 9:
        break
print()


# --- Infinite Fibonacci sequence ---

print('\nFibonacci (first 10):')

def fibonacci_gen():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

fib = fibonacci_gen()
for _ in range(10):
    print(next(fib), end=' ')
print()