Understanding Python Generators and Generator Expressions

Python generators allow functions to produce a sequence of values lazily using the yield statement, which pauses execution and returns a value each time it is iterated.

The following example defines a simple countdown generator and shows how to retrieve values using the built-in next() function or by iterating with a for loop.

def countdown(n):
    print("Counting down from %s" % n)
    while n > 0:
        yield n
        n -= 1
    return
c = countdown(10)
print(c.__next__())  # avoid calling the special method directly
print(next(c))
for i in c:
    print(i, end=' ')

Calling __next__() or next() advances the generator until it reaches a yield statement, at which point execution stops and the yielded value is returned. The generator can be closed with close() when no longer needed.

A modified generator can yield transformed values, as shown in the countdown1 example, where each yielded number is squared.

def countdown1(n):
    print("Counting down from %s" % n)
    while n > 0:
        yield n*n
        n -= 1
    return
c = countdown1(10)
print(next(c))
print(next(c))
print(next(c))
for i in c:
    print(i, end=' ')

While yield and return both produce output, yield creates a generator that can produce multiple values over time, whereas return terminates the function after a single value.

Generator expressions provide a concise way to create generators similar to list comprehensions, using parentheses instead of brackets. They are memory-efficient because they generate items on-the-fly, which is useful for processing large data streams.

print('')
print("生成器表达式:")
b = (x*x for x in range(10) if (x % 2 == 0))
print(next(b))
print(next(b))
for i in b:
    print(i, end=' ')