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数字在 numpy 数组中出现的次数

[英]How many times a number appears in a numpy array

I need to find a way to count how many times each number from 0 to 9 appears in a random matrix created using np.random.randint()我需要找到一种方法来计算使用np.random.randint()创建的随机矩阵中从 0 到 9 的每个数字出现的np.random.randint()

import numpy as np
p = int(input("Length of matrix: "))
m = np.random.randint(0,9,(p,p))
print(m)

For example if length of matrix = 4例如,如果矩阵长度 = 4

  • [[3 4 6 5] [3 4 4 3] [4 2 4 8] [6 8 2 7]] [[3 4 6 5] [3 4 4 3] [4 2 4 8] [6 8 2 7]]

How many times does the number 4 appear?数字 4 出现了多少次? It should return 5.它应该返回 5。

You should be able to get this pretty simply:你应该能够很简单地得到这个:

list(m.flatten()).count(x)

Another option which is probably faster, is to use the numpy builtin count_nonzero() :另一个可能更快的选项是使用 numpy 内置count_nonzero()

np.count_nonzero(m == x)

Hooray builtin functions.万岁内置函数。

You can use sum function:您可以使用sum函数:

In [52]: m = np.random.randint(0,9,(4,4))
In [53]: m
Out[53]: 
array([[8, 8, 2, 1],
       [2, 7, 1, 2],
       [8, 6, 8, 7],
       [5, 2, 5, 2]])

In [56]: np.sum(m == 8)
Out[56]: 4

m == 8 will return a boolean array contains True for each 8 then since python evaluates the True as 1 you can sum up the array items in order to get the number of intended items. m == 8将为每个 8 返回一个包含 True 的布尔数组,然后由于 python 将 True 评估为 1,您可以对数组项求和以获得预期项的数量。

If you want to get the frequency from all matrix elements, here's a simple solution using numpy.ndarray.flatten and collections.Counter :如果你想从所有矩阵元素中获取频率,这里有一个使用numpy.ndarray.flattencollections.Counter的简单解决方案:

import numpy as np
import collections

p = int(input("Length of matrix: "))
m = np.random.randint(0, 9, (p, p))
print(m)
print(collections.Counter(m.flatten()))

For example, when p=3 you'd get something like this:例如,当 p=3 时,您会得到如下结果:

[[8 4 8]
 [5 1 1]
 [1 1 1]]
Counter({1: 5, 8: 2, 4: 1, 5: 1})

You can flatten the matrix and then use the list count() method:您可以展平矩阵,然后使用 list count()方法:

from collections import Counter
import numpy as np
p = int(input("Length of matrix: "))
m = np.random.randint(0,9,(p,p))
print(m)
flat = [item for sublist in m for item in sublist]
flat.count(4)

I would try numpy unique function with argument return_counts=True (see: https://numpy.org/doc/stable/reference/generated/numpy.unique.html ).我会尝试使用参数 return_counts=True 的 numpy unique 函数(参见: https ://numpy.org/doc/stable/reference/generated/numpy.unique.html)。

import numpy as np
p = int(input("Length of matrix: "))
m = np.random.randint(0,9,(p,p))
# print(m)
un, nm = np.unique(m, return_counts = True)
# if number that you are looking for is 1 then:
print(nm[un==1])

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