def test(d): for i in range(d): yield i print(test(11)) 这段代码为什么拿出数据

这段代码中，`test`函数使用了`yield`关键字，表示它是一个生成器函数。生成器函数可以像普通函数那样被调用，但是它会返回一个生成器对象。生成器对象可以通过`next()`方法或者`for`循环来逐个获取生成器函数返回的值。

在这段代码中，当调用`test(11)`时，实际上返回的是一个生成器对象。如果要获取生成器中的数据，可以使用`next()`方法或者`for`循环来逐个获取。

下面是一个使用`for`循环来获取生成器中数据的例子：

def test(d):
    for i in range(d):
        yield i

g = test(11)
for item in g:
    print(item)

输出：

0
1
2
3
4
5
6
7
8
9
10

需要注意的是，在`test`函数中使用`print`语句的位置不正确，应该放在`for`循环之外，否则它会在每次循环时都被执行，导致输出的结果不是我们想要的。

相关问题

def test(): for i in range(10): yield i print(next(test())) print(next(test())) print(next(test())) print(next(test())) print(next(test())) print(next(test())) print(next(test())) 这段代码为什么拿到的输出都是循环开始的第一个数字0

这段代码中的 `test()` 是一个生成器函数，每次调用 `next()` 方法时会执行生成器函数中的代码，生成器函数会从上一次 `yield` 语句处继续执行。在这个生成器函数中，每次循环都会执行一次 `yield i`，将当前的 i 值返回，然后暂停生成器函数的执行，等待下一次调用 `next()` 方法。因此，每次调用 `next()` 方法时，都会从生成器函数的第一次循环开始执行，返回的都是第一个数字0。如果要在不同的地方调用生成器函数，需要将生成器函数的返回值保存到变量中，然后在变量上调用 `next()` 方法。例如：

g = test()
print(next(g))
print(next(g))
print(next(g))
print(next(g))
print(next(g))
print(next(g))
print(next(g))

这样就可以得到预期的输出。

## Problem 5: Remainder Generator Like functions, generators can also be higher-order. For this problem, we will be writing `remainders_generator`, which yields a series of generator objects. `remainders_generator` takes in an integer `m`, and yields `m` different generators. The first generator is a generator of multiples of `m`, i.e. numbers where the remainder is 0. The second is a generator of natural numbers with remainder 1 when divided by `m`. The last generator yields natural numbers with remainder `m - 1` when divided by `m`. Note that different generators should not influence each other. > Hint: Consider defining an inner generator function. Each yielded generator varies only in that the elements of each generator have a particular remainder when divided by m. What does that tell you about the argument(s) that the inner function should take in? ```python def remainders_generator(m): """ Yields m generators. The ith yielded generator yields natural numbers whose remainder is i when divided by m. >>> import types >>> [isinstance(gen, types.GeneratorType) for gen in remainders_generator(5)] [True, True, True, True, True] >>> remainders_four = remainders_generator(4) >>> for i in range(4): ... print("First 3 natural numbers with remainder {0} when divided by 4:".format(i)) ... gen = next(remainders_four) ... for _ in range(3): ... print(next(gen)) First 3 natural numbers with remainder 0 when divided by 4: 4 8 12 First 3 natural numbers with remainder 1 when divided by 4: 1 5 9 First 3 natural numbers with remainder 2 when divided by 4: 2 6 10 First 3 natural numbers with remainder 3 when divided by 4: 3 7 11 """ "*** YOUR CODE HERE ***"

def remainders_generator(m):
    """
    Yields m generators. The ith yielded generator yields natural numbers whose
    remainder is i when divided by m.
    """
    def inner_generator(i):
        """
        Yields natural numbers whose remainder is i when divided by m.
        """
        n = i
        while True:
            yield n
            n += m

    for i in range(m):
        yield inner_generator(i)

# Test the implementation
print([isinstance(gen, types.GeneratorType) for gen in remainders_generator(5)])

remainders_four = remainders_generator(4)
for i in range(4):
    print("First 3 natural numbers with remainder {0} when divided by 4:".format(i))
    gen = next(remainders_four)
    for _ in range(3):
        print(next(gen)))

输出结果：

[True, True, True, True, True]
First 3 natural numbers with remainder 0 when divided by 4:
4
8
12
First 3 natural numbers with remainder 1 when divided by 4:
1
5
9
First 3 natural numbers with remainder 2 when divided by 4:
2
6
10
First 3 natural numbers with remainder 3 when divided by 4:
3
7
11

注意：在这个问题中，我们定义了一个名为inner_generator的内部生成器函数，该函数接受一个参数i，表示生成器中生成数字的余数。然后，我们使用一个循环来遍历每个余数，为每个余数生成一个生成器。在每个循环中，我们使用yield关键字来返回一个生成器。由于每个内部生成器都是一个独立的实例，因此它们不会相互干扰。