对于您的情况，还可以查看numpy.bincount

In [56]: a = np.array([0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1])

In [57]: np.bincount(a)
Out[57]: array([8, 4])  #count of zeros is at index 0, i.e. 8
                        #count of ones is at index 1, i.e. 4

一个普遍而简单的答案是:

numpy.sum(MyArray==x)   # sum of a binary list of the occurence of x (=0 or 1) in MyArray

这将导致这完整的代码作为例子

import numpy
MyArray=numpy.array([0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1])  # array we want to search in
x=0   # the value I want to count (can be iterator, in a list, etc.)
numpy.sum(MyArray==0)   # sum of a binary list of the occurence of x in MyArray

现在，如果MyArray是多维的，你想要计算值在直线(= pattern以后)上分布的次数。

MyArray=numpy.array([[6, 1],[4, 5],[0, 7],[5, 1],[2, 5],[1, 2],[3, 2],[0, 2],[2, 5],[5, 1],[3, 0]])
x=numpy.array([5,1])   # the value I want to count (can be iterator, in a list, etc.)
temp = numpy.ascontiguousarray(MyArray).view(numpy.dtype((numpy.void, MyArray.dtype.itemsize * MyArray.shape[1])))  # convert the 2d-array into an array of analyzable patterns
xt=numpy.ascontiguousarray(x).view(numpy.dtype((numpy.void, x.dtype.itemsize * x.shape[0])))  # convert what you search into one analyzable pattern
numpy.sum(temp==xt)  # count of the searched pattern in the list of patterns

我会使用np.where:

how_many_0 = len(np.where(a==0.)[0])
how_many_1 = len(np.where(a==1.)[0])

这里我有一些东西，通过它你可以计算特定数字的出现次数: 根据你的代码

count_of_zero=list(y[y==0]).count(0) 

print(count_of_zero)

// according to the match there will be boolean values and according
// to True value the number 0 will be return.

如果你确切地知道你要找的数字，你可以使用下面的方法;

lst = np.array([1,1,2,3,3,6,6,6,3,2,1])
(lst == 2).sum()

返回2在数组中出现的次数。

这个函数返回变量在数组中出现的次数:

def count(array,variable):
    number = 0
    for i in range(array.shape[0]):
        for j in range(array.shape[1]):
            if array[i,j] == variable:
                number += 1
    return number