Python Generators

1. What is a Generator

A generator is a special type of function that returns an iterator and yields values one at a time using the yield keyword instead of return.

def simple_generator():
    yield 1
    yield 2
    yield 3

gen = simple_generator()
print(next(gen))  # 1
print(next(gen))  # 2

Generators produce values lazily, improving memory efficiency.

---

2. Generator vs Normal Function

def normal_function():
    return 10
    return 20

def generator_function():
    yield 10
    yield 20

print(normal_function())          # 10
print(list(generator_function())) # [10, 20]

A normal function returns once; a generator yields multiple times.

---

3. Iterating Over a Generator

def count_up(n):
    for i in range(1, n + 1):
        yield i

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

Generators integrate seamlessly with loops.

---

4. Generator State Preservation

def demo():
    print("Start")
    yield 1
    print("Resume")
    yield 2

g = demo()
print(next(g))
print(next(g))

Execution pauses and resumes, preserving internal state automatically.

---

5. Memory Efficiency of Generators

def large_numbers():
    for i in range(1000000):
        yield i

gen = large_numbers()
print(next(gen))
print(next(gen))

Unlike lists, generators do not load all data into memory.

---

6. Generator Expression

squares = (x ** 2 for x in range(5))
print(list(squares))  # [0, 1, 4, 9, 16]

Similar to list comprehensions but return a generator object.

---

7. Using Generators with next()

def alpha_generator():
    yield "A"
    yield "B"
    yield "C"

gen = alpha_generator()
print(next(gen))
print(next(gen))

Direct control over iteration flow.

---

8. Infinite Generator

def infinite_counter():
    num = 1
    while True:
        yield num
        num += 1

counter = infinite_counter()
print(next(counter))
print(next(counter))

Produces an infinite sequence until manually stopped.

---

9. Generator with try...finally

def resource_handler():
    try:
        yield "Using resource"
    finally:
        print("Resource released")

gen = resource_handler()
print(next(gen))

Ensures cleanup logic is executed after generator completion.

---

10. Real-World Generator Example (Streaming Data)

def read_large_file(filename):
    with open(filename, "r") as file:
        for line in file:
            yield line.strip()

for line in read_large_file("data.txt"):
    print(line)

Ideal for processing large files or streaming data efficiently.

---