这是另一个替代解决方案

In [1]: a=[[1, 2],[3, 4],[5, 6]]
In [2]: sum([sum(i) for i in a])
Out[2]: 21

营养问题的解决办法是:

import numpy as np
x = np.array([[1, 2],[3, 4],[5, 6]])

结果:

>>> b=np.sum(x)
   print(b)
21

最好还是忘记索引计数器,

def sum1(input):
    my_sum = 0
    for row in input:
        my_sum += sum(row)
    return my_sum

print sum1([[1, 2],[3, 4],[5, 6]])

Python 的优美( 和奇特) 特性之一是让它为您进行计算。 sum () 是内置的, 您不应该使用内置的内设名称来显示自己的标识符 。

Speed comparison

import random
import timeit
import numpy
x = [[random.random() for i in range(100)] for j in range(100)]
xnp = np.array(x)

Methods

print("Sum python array:")
%timeit sum(map(sum,x))
%timeit sum([sum(i) for i in x])
%timeit sum(sum(x,[]))
%timeit sum([x[i][j] for i in range(100) for j in range(100)])

print("Convert to numpy, then sum:")
%timeit np.sum(np.array(x))
%timeit sum(sum(np.array(x)))

print("Sum numpy array:")
%timeit np.sum(xnp)
%timeit sum(sum(xnp))

Results

Sum python array:
130 µs ± 3.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
149 µs ± 4.16 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
3.05 ms ± 44.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.58 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Convert to numpy, then sum:
1.36 ms ± 90.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
1.63 ms ± 26.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Sum numpy array:
24.6 µs ± 1.95 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
301 µs ± 4.78 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

python 中的 range () 不包括最后一个元素。 换句话说, range (1, 5), is[ 1, 5) 或[ 1, 4] 。 所以, 您应该使用 len( input) 来循环行/ 列。

def sum1(input):
    sum = 0
    for row in range (len(input)):
        for col in range(len(input[0])):
            sum = sum + input[row][col]

    return sum

不要将 - 1 放在范围( 列( 输入)-1) 中, 而不是使用 :

range(len(input))

区域 区域自动返回小于参数值的列表,因此不需要明确给予 -1。

def sum1(input):
    return sum([sum(x) for x in input])

快速回答,使用...

total = sum(map(sum,[array]))

您的阵列标题是 [array] 。

在Python 3. 7中

import numpy as np
x = np.array([ [1,2], [3,4] ])
sum(sum(x))

产出产出产出产出

10

与简单的算法相比,似乎有一个普遍共识,即富油是一个复杂的解决办法。但为了找到答案:

import numpy as np


def addarrays(arr):

    b = np.sum(arr)
    return sum(b)


array_1 = [
  [1, 2],
  [3, 4],
  [5, 6]
]
print(addarrays(array_1))

这似乎是首选解决办法:

x=[[1, 2],[3, 4],[5, 6]]                                                   
sum(sum(x,[]))                                                             

def sum1(input):
    sum = 0
    for row in input:
        for col in row:
            sum += col
    return sum
print(sum1([[1, 2],[3, 4],[5, 6]]))

def sum1(input):
    sum = 0
    for row in range (len(input)-1):
        for col in range(len(input[0])-1):
            sum = sum + input[row][col]

    return sum


print (sum1([[1, 2],[3, 4],[5, 6]]))

You had a problem with parenthesis at the print command.... This solution will be good now The correct solution in Visual Studio Code

我想这就是你想做的

def sum1(arr):
sum = 0
for i in range(len(arr)):
    for j in range(len(arr[0])):
        sum += arr[i][j]
return sum

print(sum1([[1, 2], [3, 4], [5, 6]]))

curVal = sum([sum(grid[curRow + i][curCol-1:curCol+2]) for i in range(-1, 2)])