Python max n*n重塑2d numpy数组函数，该函数丢弃最小值并返回列平均值之和？

我正在为我的数据科学课程研究这个问题： 编写一个接受整数的函数，并执行以下操作： 创建从0到输入整数的数字数组 将其重塑为最大的n*n数组，丢弃任何多余的元素（即，如果要制作10 x 10，但有102个元素，则丢弃最后2个） 返回列平均值的累积和

到目前为止，我有下面的代码用于矩阵重塑，但是对于大的数字它会超时。任何关于如何完成此问题第一步的建议都将不胜感激

    import numpy as np
def ranged_arr(n):
    ranged_arr = np.arange(0,n+1,1)
    if len(ranged_arr)%int(len(ranged_arr)**0.5)== 0:
        array = ranged_arr.reshape(int(len(ranged_arr)**0.5),int(len(ranged_arr)**0.5))
        return array
    else:
        len(ranged_arr)%int(len(ranged_arr)**0.5)!= 0
        idx = 0
        new_arr = np.arange(0,n-idx,1)
        while len(new_arr)%int(len(new_arr)**0.5)!= 0:
            idx +=1
        q = new_arr.reshape(int(len(new_arr)**0.5),int(len(new_arr)**0.5))
        return q

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让我们保持甜蜜和简单：）

首先，让我们分解您的问题，您必须：

一,。创建一个从0到输入整数的数字数组

二,。将其重塑为最大的m x m阵列

2.1。查找最大尺寸（m）

现在让我们编写Python函数

def ranged_arr(n):
    #Creates an array of the numbers from 0 up to that inputted integer 
    ranged_arr = np.arange(0,n+1)

    #Reshapes it to be the largest n * n array that it could be

    #Find the largest dim
    largest_dim = math.floor(math.sqrt(n+1))
    #Find the size of the array that will be reshaped
    last_index = largest_dim**2
    #Take the slice of the original array that could be reshaped
    fitted_ranged_arr = ranged_arr[:last_index]
    #Finally, reshape it!
    reshaped_range_arr = fitted_ranged_arr.reshape((largest_dim,largest_dim))

    #Return everything, so you can check the steps
    return ranged_arr,largest_dim,fitted_ranged_arr,reshaped_range_arr

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我把它上传到我的Github，这样你就可以通过@Alber8295启动的代码中的一些测试来检查它，剩下的问题是：

def ranged_arr(n):
    #Creates an array of the numbers from 0 up to that inputted integer 
    ranged_arr = np.arange(0,n+1)

    #Reshapes it to be the largest n * n array that it could be

    #Find the largest dim
    largest_dim = math.floor(math.sqrt(n+1))
    #Find the size of the array that will be reshaped
    last_index = largest_dim**2
    #Take the slice of the original array that could be reshaped
    fitted_ranged_arr = ranged_arr[:last_index]
    #Finally, reshape it!
    reshaped_range_arr = fitted_ranged_arr.reshape((largest_dim,largest_dim))

    # get the sum of the col means 

    #get the means of each col
    col_means = np.mean(reshaped_range_arr,axis=0)
    # get the sum of the means 
    sum_of_means = col_means.sum()
    #Return everything, so you can check the steps
    return ranged_arr,largest_dim,fitted_ranged_arr,reshaped_range_arr,col_means, sum_of_means

    print(sum_of_means)

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非常感谢，这真的简化了问题！很高兴看到你完美地解决了剩下的练习^^