Generators and Iterators

Introduction

Generators provide a memory-efficient way to create iterators using yield statements.

Creating Generators

# Generator function
def count_up_to(n):
    count = 1
    while count <= n:
        yield count
        count += 1

# Generator expression
squares = (x**2 for x in range(1000000))

# Using generator
for num in count_up_to(5):
    print(num)

Why Use Generators?

# Memory comparison
import sys

# List (stores everything in memory)
numbers_list = [x**2 for x in range(10000)]
print(sys.getsizeof(numbers_list))  # ~80KB

# Generator (computes on demand)
numbers_gen = (x**2 for x in range(10000))
print(sys.getsizeof(numbers_gen))  # ~200 bytes

Generator Methods

def infinite_counter():
    count = 0
    while True:
        yield count
        count += 1

counter = infinite_counter()
next(counter)  # 0
next(counter)  # 1
print(counter.send(10))  # 11 (send value to yield)
counter.close()  # Stop generator

Practice Problems

1. Create Fibonacci generator
2. Generate prime numbers indefinitely
3. Read large file line by line
4. Implement lazy evaluation of expressions
5. Use send() to control generator flow