Aprenda Introduction to Generators: Yielding Values for Efficient Iteration | Mastering Iterators and Generators in Python

Generators simplify lazy evaluation by providing a concise and readable way to create iterators. While an iterator is implemented as a class with the __iter__() and __next__() methods, a generator is implemented as a function that uses the yield keyword to produce values one at a time. Generators maintain their state automatically between calls, making them more intuitive and efficient for many use cases.

A generator is a special type of function that:

Uses the yield keyword instead of return;
Pauses execution and retains its state when yield is called;
Resumes execution from where it left off when the generator is called again.


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def example_generator():
    yield "First value"
    yield "Second value"
    yield "Third value"

gen = example_generator()
print(next(gen))  # Output: First value
print(next(gen))  # Output: Second value
print(next(gen))  # Output: Third value


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import random

def limited_dice_roller(num_rolls):
    for _ in range(num_rolls):
        yield random.randint(1, 6)

# Using the limited dice roller
print("Rolling the dice:")
for roll in limited_dice_roller(5):
    print(f"Rolled: {roll}")

Differences Between Iterator and Generator

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In the previous task, you implemented an infinite dice roller using a custom iterator class. Now, you will simplify the same functionality by using a generator function. Generators provide a concise and readable way to produce values lazily using the yield keyword.

Define the generator function. Use the yield keyword inside the dice_roller function to produce random dice rolls between 1 and 6. Use the random.randint() function to simulate each roll.
Call the dice_roller() function to create a generator object and assign it to the variable dice_generator.
Use a for loop with enumerate() to iterate over the generator. Stop the iteration after 10 rolls using an if condition and the break statement.

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