Effective Python 31 - 35

Click here for the first post, which contains the context of this series.

Item #31: Be defensive when iterating over arguments.

Consider

def normalize(X):

    s = sum(X)

    return [x / s for x in X]

normalize works as expected if X is a container and does not work as expected if X is a generator; this is because sum(X) exhausts the generator. Address this by checking whether X is a generator with iter(X) == X or isinstance(X, Iterator), where Iterator is imported from collections.abc.

Item #32: Consider generator expressions for large list comprehensions.

Let X be an extraordinarily large iterable. Then

for y in [f(x) for x in X]: pass

will load an extraordinarily large object into memory. On the other hand,

for y in (f(x) for x in X): pass

does not have this problem.

Item #33: Compose multiple generators with yield from.

def my_gen():

    yield from gen_1()

    yield from gen_2()

    yield from gen_3()

is shorthand for and performs better than

def my_gen():

    for i in gen_1():

        yield i

    for i in gen_2():

        yield i

    for i in gen_3():

        yield i

Item #34: Avoid injecting data into generators with send.

Consider

def double_inputs():

    while True:

        x = yield

        yield x * 2

gen = double_inputs()

next(gen)

print(gen.send(10))

next(gen)

print(gen.send(6))

next(gen)

print(gen.send(94.3))

>>>

20

12

188.6

Avoid doing this.

Item #35: Avoid causing state transitions in generators with throw.

Consider

def my_gen():

    i = 0

    while i < 10:

        try:

            i += 1

            yield i

        except GeneratorExit:

            return

        except BaseException:

            i = -1

it = my_gen()

print(next(it))

print(next(it))

print(next(it))

it.throw(BaseException())

print(next(it))

>>>

1

2

3

1

Avoid doing this.