numpy运算简介（二）

numpy.sum

d = np.array([
    [1, 2, 1],
    [3, 0, 2]
])
print(d.shape)  # (2, 3)

axis的参数不能超过数组的维度，用来压缩其表示的维度，从下面的代码可以和明显看出其运算原理

print(np.sum(d))  # 1+3+2+0+1+2 = 9
print(np.sum(d, axis=0))  # [1+3, 2+0, 1+2] = [4, 2, 3]
print(np.sum(d, axis=1))  # [1+2+1, 3+0+2] = [4, 5]

再来个三维数组

c = np.array([
    [
        [2, 3, 1],
        [4, 1, 0]
    ],
    [
        [0, 3, 1],
        [0, 1, 0]
    ]
])
print(c.shape)  # (2, 2, 3)

print(np.sum(c))  # 2+3+1+4+1+0 + 0+3+1+0+1+0=16
print(np.sum(c, axis=0))  # [[2, 3, 1], [4, 1, 0]] + [[0, 3, 1], [0, 1, 0]] = [[2, 6, 2], [4, 2, 0]]
print(np.sum(c, axis=1))  # [[2, 3, 1] + [4, 1, 0]] + [[0, 3, 1] + [0, 1, 0]] = [[6, 4, 1], [0, 4, 1]]
print(np.sum(c, axis=2))  # [[2+3+1, 4+1+0],[0+3+1, 0+1+0]] = [[6, 5], [4, 1]]

np.max、np.min、np.mean等同理 （以2维数组为例）

print(np.max(d))  # 3
print(np.max(d, axis=0))  # [3, 2,  2]
print(np.max(d, axis=1))  # [2, 3]

print(np.min(d))  # 0
print(np.min(d, axis=0))  # [1, 0, 1]
print(np.min(d, axis=1))  # [1, 0]

print(np.mean(d))  # 1.5
print(np.mean(d, axis=0))  # [2.  1.  1.5]
print(np.mean(d, axis=1))  # [1.33333333 1.66666667]