Das Schlüsselwort yield

Einführung

Was ist yield?

def count_up(n):
    for i in range(n):
        yield i
for x in count_up(3):
    print(x)

Generatorfunktion im Vergleich zur normalen Funktion

def gen():
    print('start')
    yield 1
    print('middle')
    yield 2
g = gen()  # no print yet
print(next(g))  # 'start', then 1

Unendlicher Generator

def naturals(n=0):
    while True:
        yield n
        n += 1
import itertools
print(list(itertools.islice(naturals(), 5)))

yield from

def chain(*iters):
    for it in iters:
        yield from it
print(list(chain([1,2], [3,4], [5])))

Werte mit send() senden

def accumulator():
    total = 0
    while True:
        n = yield total
        if n is None: break
        total += n
g = accumulator()
next(g)  # prime
print(g.send(10))
print(g.send(5))

Generatorzustand

def stateful():
    seen = set()
    while True:
        item = yield
        seen.add(item)
        yield len(seen)
g = stateful()
next(g); g.send('a'); print(next(g)); g.send('b'); print(next(g))

Einen Generator schließen

def resource_gen():
    try:
        while True:
            yield
    finally:
        print('cleaned up')
g = resource_gen()
next(g)
g.close()

StopIteration und return

def gen_with_return():
    yield 1
    yield 2
    return 'done'
g = gen_with_return()
try:
    while True: print(next(g))
except StopIteration as e:
    print('returned:', e.value)

Fibonacci-Generator

def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b
import itertools
print(list(itertools.islice(fibonacci(), 10)))

Generatoren im Vergleich zu Klassen mit __iter__

# Generator is simpler than:
# class Counter:
#     def __iter__(self): return self
#     def __next__(self): ...
print('generators simplify iterators')

Wissenscheck

Zusammenfassung

Weiter