Summary  
This chapter covers how to implement infinite generators using `yield` inside a `while True` loop to produce unbounded sequences of values on demand and control their retrieval with `next()` or iteration constructs.  

General domain of usage  
Data streaming

Recall that `yield` pauses a generator and resumes it on the next `next()` call. So far, the generators you have seen had a clear stopping point. But generators become especially powerful when combined with an **infinite loop** – producing values indefinitely without ever storing them in memory.

## Infinite Generators with `while True`

Using a `while True` loop inside a generator creates an **infinite sequence** – something that would be impossible to store as a list. The generator only produces the next value when `next()` is called:


def counter():
    count = 1
    while True:       # Runs forever
        yield count   # Return current value and pause
        count += 1    # Resume from here on the next call

gen = counter()

print(next(gen))  # 1
print(next(gen))  # 2
print(next(gen))  # 3

No matter how many times you call `next()`, the generator will always have a new value ready – it never runs ahead or stores values in memory.

## Controlling an Infinite Generator

Since the generator runs forever, you need to control how many values you retrieve. The most common way is using a `for` loop with `range()`:


def counter():
    count = 1
    while True:
        yield count
        count += 1

gen = counter()

for _ in range(5):
    print(next(gen))  # 1, 2, 3, 4, 5

Never iterate over an infinite generator with a plain `for` loop without a stopping condition – it will run forever and crash your program.

Note

import unittest
import types

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestIdGenerator(unittest.TestCase):

    def test_id_generator_is_generator(self):
        import user_code
        try:
            result = user_code.id_generator()
            condition = isinstance(result, types.GeneratorType)
            failure_message = "Expected `id_generator()` to return a generator object."
        except AttributeError:
            condition = False
            failure_message = "The function `id_generator` is not defined."
        _dynamic_test(self, condition, "The `id_generator()` returns a generator object.", failure_message)

    def test_first_five_ids(self):
        import user_code
        try:
            gen = user_code.id_generator()
            result = [next(gen) for _ in range(5)]
            expected = ["ID_1", "ID_2", "ID_3", "ID_4", "ID_5"]
            condition = result == expected
            failure_message = f"Expected `{expected}`, but got `{result}`."
        except AttributeError:
            condition = False
            failure_message = "The function `id_generator` is not defined."
        _dynamic_test(self, condition, f"The `id_generator` correctly generates `['ID_1', 'ID_2', 'ID_3', 'ID_4', 'ID_5']`.", failure_message)

    def test_id_format(self):
        import user_code
        try:
            gen = user_code.id_generator()
            result = [next(gen) for _ in range(5)]
            condition = all(id.startswith("ID_") for id in result)
            failure_message = f"Expected all IDs to follow the format `ID_N`, but got `{result}`."
        except AttributeError:
            condition = False
            failure_message = "The function `id_generator` is not defined."
        _dynamic_test(self, condition, "All generated IDs follow the format `ID_N`.", failure_message)

    def test_ids_are_sequential(self):
        import user_code
        try:
            gen = user_code.id_generator()
            result = [next(gen) for _ in range(5)]
            numbers = [int(id.split("_")[1]) for id in result]
            condition = numbers == list(range(1, 6))
            failure_message = f"Expected sequential IDs starting from 1, but got `{result}`."
        except (AttributeError, ValueError):
            condition = False
            failure_message = "Could not verify sequential IDs — make sure the format is `ID_N`."
        _dynamic_test(self, condition, "The generated IDs are sequential starting from `ID_1`.", failure_message)

if __name__ == '__main__':
    unittest.main()

test_main.py

Adquirir una comprensión sólida de cómo las funciones estructuran la programación en Python. Dominar la definición y llamada de funciones, el trabajo con argumentos, la gestión de valores de retorno y la creación de código flexible y eficiente utilizando recursión y expresiones lambda.

Explora qué son las funciones y por qué son esenciales en Python. Aprende a crear funciones, definir argumentos, manejar valores de retorno y utilizar funciones integradas de manera efectiva.

Aprende cómo funcionan los argumentos posicionales y opcionales en Python. Crea funciones adaptables que gestionen diferentes entradas y mejoren la flexibilidad en tu código.

Comprender cómo trabajar con argumentos arbitrarios y de palabras clave para manejar tamaños de entrada variables. Aprender cómo estas técnicas hacen que tus funciones sean más dinámicas y organizadas.

Descubre cómo los valores de retorno representan la salida de una función. Aprende a retornar valores únicos o múltiples, utilizar None y explorar generadores para un control de flujo de datos más avanzado.

Descubre conceptos avanzados de funciones con recursión y funciones lambda. Aprende cómo la recursión resuelve problemas repetitivos y cómo las expresiones lambda crean funciones concisas de una sola línea.

Infinite Generators

Infinite Generators with while True

Controlling an Infinite Generator

Solución

Infinite Generators with `while True`