ExamAdept

What happens if a generator function contains a return value statement followed by a yield statement?

A:

It raises a GeneratorExit exception

B:

It raises a StopIteration exception

C:

It returns the specified value and stops the generator

D:

It is a syntax error; return and yield cannot be used together

Answer: C

If a generator function contains both return value and yield statements, it returns the specified value and stops the generator. -PROGCODE-

def my_generator():
    return 42
    yield  # This statement is never reached

# Example usage
gen = my_generator()
result = next(gen, None)
print(result)  # Output: 42

-PROGCODE-END-

📖

How does the generator.throw(StopIteration) method differ from raise StopIteration when used with a generator?

A:

They have the same effect on the generator

B:

generator.throw(StopIteration) stops the generator and raises a StopIteration exception, while raise StopIteration is used outside the generator

C:

raise StopIteration stops the generator and raises a StopIteration exception, while generator.throw(StopIteration) is used outside the generator

D:

generator.throw(StopIteration) raises a ValueError

Answer: B

generator.throw(StopIteration) is used to stop the generator and raise a StopIteration exception inside the generator. raise StopIteration is used outside the generator to signal the end of the iteration.

📖

How does the itertools.islice() function differ from itertools.takewhile() when used with generators?

A:

They are equivalent in functionality

B:

itertools.islice() allows for arbitrary slicing, while itertools.takewhile() stops based on a condition

C:

itertools.takewhile() allows for arbitrary slicing, while itertools.islice() stops based on a condition

D:

itertools.islice() modifies the original generator, while itertools.takewhile() creates a new generator

Answer: B

itertools.islice() allows for arbitrary slicing by specifying start, stop, and step parameters, while itertools.takewhile() stops yielding elements when a specified condition becomes false. -PROGCODE-

import itertools

def my_generator():
    for i in range(10):
        yield i

sliced_iterable = itertools.islice(my_generator(), 2, 7)
takewhile_iterable = itertools.takewhile(lambda x: x < 5, my_generator())

print(list(sliced_iterable))      # Output: [2, 3, 4, 5, 6]
print(list(takewhile_iterable))   # Output: [0, 1, 2, 3, 4]

-PROGCODE-END-

📖

What is the purpose of the itertools.accumulate() function when used with generators?

A:

It generates the cumulative sum of elements in the generator

B:

It filters elements based on a specified condition

C:

It stops the generator after one iteration

D:

It interleaves values from different generators

Answer: A

itertools.accumulate() generates the cumulative sum of elements in the generator, creating an iterator that yields the accumulated results.

📖

How does the itertools.filterfalse() function differ from filter() when used with generators?

A:

They are equivalent in functionality

B:

itertools.filterfalse() filters elements based on a false condition, while filter() filters based on a true condition

C:

filter() filters elements based on a false condition, while itertools.filterfalse() filters based on a true condition

D:

itertools.filterfalse() raises a ValueError if the filtering function is not provided

Answer: B

itertools.filterfalse() filters elements from the generator for which the filtering function returns false, while filter() filters based on a true condition.

📖

Questions for: Generators