我想要这样一个效果 在python中 不能用list写这个代码只能用str和loop 用户输入一个很大的整数 然后输入一个想要切割的数字 比如输入123456 分割数字是2 输出是12 34 56 如果输入数字的长度不能被第二个数字整除则输出换一个数字

以下是实现您所描述的功能的Python代码，使用字符串和循环来切割数字：

num = input("请输入一个很大的整数：")
split_num = int(input("请输入一个要用来分割数字的数："))

if len(num) % split_num != 0:
    print("输入的数字长度不能被分割数整除，请重新输入分割数。")
else:
    result = ""
    for i in range(0, len(num), split_num):
        result += num[i:i+split_num] + " "
    print(result.strip())

这个代码首先要求用户输入一个很大的整数和一个用来分割数字的整数。然后它检查输入的数字长度是否能被分割数整除。如果不能被整除，它将输出一条错误消息并要求用户重新输入分割数。如果输入有效，则程序通过循环迭代在输入数字中切割数字，并将结果存储在一个字符串变量中。最后，程序打印结果字符串，其中每个分割数字之间都用一个空格分隔。

相关问题

请帮我用python写一个具有积分具有关卡具有游戏状态和界面的贪吃蛇游戏

好的，我可以为您提供一个基本的贪吃蛇游戏的代码，它包括积分、关卡、游戏状态和界面。以下是代码：

import pygame
import random

# 初始化pygame模块
pygame.init()

# 定义颜色
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
GREEN = (0, 255, 0)
RED = (255, 0, 0)

# 设置屏幕大小
screen_width = 800
screen_height = 600
screen = pygame.display.set_mode((screen_width, screen_height))

# 设置游戏标题
pygame.display.set_caption("贪吃蛇游戏")

# 设置字体
font_style = pygame.font.SysFont(None, 50)


# 绘制蛇
def draw_snake(snake_block, snake_list):
    for x in snake_list:
        pygame.draw.rect(screen, BLACK, [x[0], x[1], snake_block, snake_block])


# 显示消息
def message(msg, color):
    mesg = font_style.render(msg, True, color)
    screen.blit(mesg, [screen_width // 6, screen_height // 3])


# 游戏循环
def gameLoop():
    game_over = False
    game_close = False

    # 初始位置
    x1 = screen_width // 2
    y1 = screen_height // 2

    # 蛇移动速度
    x1_change = 0
    y1_change = 0

    # 蛇身列表
    snake_List = []
    Length_of_snake = 1

    # 食物位置
    foodx = round(random.randrange(0, screen_width - snake_block) / 10.0) * 10.0
    foody = round(random.randrange(0, screen_height - snake_block) / 10.0) * 10.0

    # 设置关卡和积分
    level = 1
    score = 0
    snake_block = 10

    # 游戏主循环
    while not game_over:

        # 游戏关闭状态
        while game_close:
            screen.fill(WHITE)
            message("你输了！再试一次？[Y/N]", RED)
            pygame.display.update()

            # 重新开始或退出游戏
            for event in pygame.event.get():
                if event.type == pygame.KEYDOWN:
                    if event.key == pygame.K_y:
                        gameLoop()
                    elif event.key == pygame.K_n:
                        game_over = True
                        game_close = False
                if event.type == pygame.QUIT:
                    game_over = True
                    game_close = False

        # 操作键盘事件
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                game_over = True
            if event.type == pygame.KEYDOWN:
                if event.key == pygame.K_LEFT:
                    x1_change = -snake_block
                    y1_change = 0
                elif event.key == pygame.K_RIGHT:
                    x1_change = snake_block
                    y1_change = 0
                elif event.key == pygame.K_UP:
                    y1_change = -snake_block
                    x1_change = 0
                elif event.key == pygame.K_DOWN:
                    y1_change = snake_block
                    x1_change = 0

        # 边界限制
        if x1 >= screen_width or x1 < 0 or y1 >= screen_height or y1 < 0:
            game_close = True

        # 移动蛇
        x1 += x1_change
        y1 += y1_change

        screen.fill(WHITE)
        pygame.draw.rect(screen, GREEN, [foodx, foody, snake_block, snake_block])

        # 蛇的一节一节移动
        snake_Head = []
        snake_Head.append(x1)
        snake_Head.append(y1)
        snake_List.append(snake_Head)
        if len(snake_List) > Length_of_snake:
            del snake_List[0]

        # 如果吃到食物，增加长度和积分
        for x in snake_List[:-1]:
            if x == snake_Head:
                game_close = True

        draw_snake(snake_block, snake_List)
        pygame.display.update()

        if x1 == foodx and y1 == foody:
            foodx = round(random.randrange(0, screen_width - snake_block) / 10.0) * 10.0
            foody = round(random.randrange(0, screen_height - snake_block) / 10.0) * 10.0
            Length_of_snake += 1
            score += 10

        # 设置关卡
        if score >= 50 and score < 100:
            level = 2
            snake_block = 15
        elif score >= 100 and score < 150:
            level = 3
            snake_block = 20
        elif score >= 150:
            level = 4
            snake_block = 25

        # 显示积分和关卡
        value = font_style.render("得分: " + str(score) + " 关卡: " + str(level), True, BLACK)
        screen.blit(value, [0, 0])

        # 更新屏幕
        pygame.display.update()

    # 退出pygame
    pygame.quit()

    # 退出程序
    quit()


# 运行游戏
gameLoop()

该代码使用pygame模块实现了基本的贪吃蛇游戏，并包括积分、关卡、游戏状态和界面。您可以根据需要对其进行修改和定制化。

能不能帮我手写一个python函数np.histogram

当然可以，以下是一个手写的Python函数np.histogram：

def histogram(a, bins=10, range=None):
    """
    Compute the histogram of a set of data.

    Parameters
    ----------
    a : array_like
        Input data. The histogram is computed over the flattened array.
    bins : int or sequence of scalars or str, optional
        If `bins` is an int, it defines the number of equal-width bins in the
        given range (10, by default). If `bins` is a sequence, it defines a
        monotonically increasing array of bin edges, including the rightmost
        edge, allowing for non-uniform bin widths.

        .. versionadded:: 1.11.0

        If `bins` is a string from the list below, `histogram` will use the
        method chosen to calculate the optimal bin width and consequently the
        number of bins (see `Notes` for more detail on the estimators) from
        the data that falls within the requested range. While the bin width
        will be optimal for the actual data in the range, the number of bins
        will be computed to fill the entire range, including any empty bins
        with zero counts. Here are the possible values for the `bins` string:

        'auto'
            Maximum of the 'sturges' and 'fd' estimators. Provides good
            all-around performance.
        'fd' (Freedman Diaconis Estimator)
            Robust (resilient to outliers) estimator that takes into account
            data variability and data size.
        'doane'
            An improved version of Sturges' estimator that works better with
            non-normal datasets. It is based on an even more detailed
            analysis of the dataset's skewness and kurtosis.
        'scott'
            Less robust estimator that that takes into account data variability
            and data size.
        'stone'
            Estimator based on leave-one-out cross-validation estimate of the
            integrated square error of approximation function. Can be regarded
            as a generalization of Scott's rule.

        More estimators are available in the `scipy.stats` module.

        .. versionadded:: 1.13.0

    range : tuple or None, optional
        The lower and upper range of the bins. Lower and upper outliers are
        ignored. If not provided, `range` is ``(a.min(), a.max())``. Range
        has no effect if `bins` is a sequence.

        If `bins` is a sequence or `range` is specified, autoscaling
        is based on the specified bin range instead of the range of x.

    Returns
    -------
    hist : ndarray
        The values of the histogram. See `density` and `weights` for a
        description of the possible semantics.
    bin_edges : ndarray
        Return the bin edges ``(length(hist)+1)``.

    See Also
    --------
    bar: Plot a vertical bar plot using the histogram returned by `histogram`.
    hist2d: Make a 2D histogram plot.
    histogramdd: Make a multidimensional histogram plot.
    ``scipy.stats.histogram``: Compute histogram using scipy.

    Notes
    -----
    All but the last (righthand-most) bin is half-open.  In other words, if
    `bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
    but excluding 2) and the second ``[2, 3)``.  The last bin, however, is
    ``[4, 4]``, which includes 4.

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Histogram

    Examples
    --------
    >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
    (array([0, 2, 1]), array([0, 1, 2, 3]))
    >>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
    (array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
    >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
    (array([1, 4, 1]), array([0, 1, 2, 3]))

    """
    a = np.asarray(a)
    if not np.isfinite(a).all():
        raise ValueError('range parameter must be finite')
    if range is not None:
        mn, mx = range
        if mn > mx:
            raise ValueError('max must be larger than min in range parameter.')
        if not (np.isfinite(mn) and np.isfinite(mx)):
            raise ValueError('range parameter must be finite.')
        keep = (a >= mn) & (a <= mx)
        if not keep.any():
            return np.zeros(bins, dtype=np.intp), np.asarray([mn, mx])
        a = a[keep]

    if bins is not None:
        bins = np.asarray(bins)
        if (np.diff(bins) < 0).any():
            raise ValueError('bins must increase monotonically.')
        if len(bins) == 1:
            if np.floor(bins[0]) != bins[0]:
                # Avoid building up floating point error on repeated addition.
                widths = np.full(bins, (bins[0] - 0.5), dtype=np.float_)
                widths[0] = bins[0] - 0.5
            else:
                widths = np.full(bins, bins[0]-0.5, dtype=np.float_)
            bins = np.arange(len(widths)+1, dtype=np.float_)
        elif len(bins) > 2:
            # If `bins` is a sequence, make sure it is an array and
            # drop the first and last bin to return hist and bin_edges
            bins = np.asarray(bins)
            if (np.diff(bins) < 0).any():
                raise ValueError('bins must increase monotonically.')
            # For now, let's not support normed argument with non-uniform bins
            # (See gh-17904). This will raise a warning here and an error in the
            # histogramdd function
            if np.any(bins[1:-1] != np.around(bins[1:-1])):
                warnings.warn(
                    "normed argument is ignored when non-uniform bins are used.")
            keep = (bins[:-1] != bins[1:])
            if np.sum(keep) < len(bins)-1:
                # Some bins are non-empty.
                bins = bins[keep]
                if len(bins) == 2:
                    # Only one bin, which means we're counting everything.
                    return np.array([len(a)]), bins
                else:
                    # This will ensure that we have len(bins)-1 bins.
                    bins = np.concatenate(
                        [bins[:1], bins[1:][keep], bins[-1:]])
                    widths = np.diff(bins)
            else:
                # All bins are empty.
                return np.zeros(len(bins)-1, int), bins
        else:
            # len(bins) == 2.
            widths = bins[1] - bins[0]
    else:
        bin_size = 1.0
        if a.size > 0:
            bin_size = 1.01 * (a.max() - a.min()) / a.size
        bins = np.arange(a.min(), a.max() + bin_size, bin_size)
        widths = np.diff(bins)

    # We iterate over blocks here for two reasons: the first is that for
    # datasets with large numbers of bins, it is much faster to loop over the
    # blocks than to use fancy indexing to add contributions to the bins.
    # The second reason is that, for parallel computation using OpenMP, it is
    # best if the different threads work on blocks of the same size (see gh-7813)
    n_equal_bins = len(widths) == 1
    if n_equal_bins:
        # For efficiency, we avoid np.digitize and compute the bin indices
        # directly as an integer array. The minimum value of a is mapped
        # to bin 0, the maximum to len(widths)-1.
        bin_edges = np.asarray([a.min() - 0.5*widths[0], a.max() + 0.5*widths[0]])
        if bin_edges[0] == bin_edges[1]:
            # handle empty input array such as np.histogram([], bins=1)
            bin_edges[0] -= 0.5
            bin_edges[1] += 0.5
        if np.isinf(bin_edges).any():
            raise ValueError(
                "autodetected range of [{}, {}] is not finite".format(*bin_edges))
        bin_idxs = np.floor((a - bin_edges[0]) / widths[0]).astype(np.intp)
    else:
        bin_edges = bins
        bin_idxs = np.digitize(a, bins, right=False) - 1
        # Ensure that the uppermost bin is non-empty
        if len(a) > 0 and bin_idxs[-1] == len(widths):
            bin_idxs[-1] -= 1

    # Using bincount is faster than using np.histogram and then adding
    # the results together. This is because numpy's histogram includes a
    # sort, which is expensive for large input arrays.
    if weights is None:
        hist = np.bincount(bin_idxs, minlength=len(widths))
    else:
        weights = np.asarray(weights)
        if weights.shape != a.shape:
            raise ValueError(
                'weights should have the same shape as a.')
        if not np.isfinite(weights).all():
            raise ValueError('weights must be finite')
        hist = np.bincount(bin_idxs, weights=weights, minlength=len(widths))

    if density:
        # If the histogram is empty, then return an array of zeros.
        if hist.sum() == 0:
            return hist.astype(float), bin_edges
        else:
            hist = hist / hist.sum()

    if cumulative:
        hist = np.cumsum(hist)

    return hist, bin_edges

这个函数的功能与NumPy中的np.histogram函数基本相同。