数组操作

数组操作.png

更改形状

通过修改shape属性改变数组形状

x = np.array([1, 2, 9, 4, 5, 6, 7, 8])
print(x.shape)

(8,)

x.shape = [2, 4]
print(x)

[[1 2 9 4]
 [5 6 7 8]]

flat方法将数组转换为一维的迭代器

x = np.arange(12).reshape(3, 4)
x

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

y = x.flat
print(y)
for i in y:
    print(i, end=' ')

<numpy.flatiter object at 0x00000257A2BE29E0>
0 1 2 3 4 5 6 7 8 9 10 11 

print(x)

[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

flatten方法将数组的副本转换为一维数组

x

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

y = x.flatten()
print(y)
print(x)

[ 0  1  2  3  4  5  6  7  8  9 10 11]
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

ravel方法返回的是多维数组展平后的视图

x

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

y = np.ravel(x)
print(y)

[ 0  1  2  3  4  5  6  7  8  9 10 11]

y[3] = 5
print(x)

[[ 0  1  2  5]
 [ 4  5  6  7]
 [ 8  9 10 11]]

reshape方法也可以跟更改shape属性一样改变数组形状

x = np.arange(12).reshape(3, -1)
x

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

数组转置

x

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

x.T

array([[ 0,  4,  8],
       [ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11]])

x.transpose()

array([[ 0,  4,  8],
       [ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11]])

更改维度

np.newaxis增加一个维度

x = np.array([1, 2, 9, 4, 5, 6, 7, 8])
print(x.shape) 
print(x)  

(8,)
[1 2 9 4 5 6 7 8]

y = x[np.newaxis, :]
print(y.shape) 
print(y) 

(1, 8)
[[1 2 9 4 5 6 7 8]]

np.squeeze函数可以通过删除单维度的条目来降低数组一个维度

print(y, y.ndim)

[[1 2 9 4 5 6 7 8]] 2

z = np.squeeze(y)
print(z.shape)

(8,)

数组合并

np.concatenate

x = np.array([1, 2, 3])
y = np.array([7, 8, 9])
z = np.concatenate([x, y])
print(z)

[1 2 3 7 8 9]

z = np.concatenate([x, y], axis=0)
z

array([1, 2, 3, 7, 8, 9])

np.vstack

a = np.vstack([x, y])
a

array([[1, 2, 3],
       [7, 8, 9]])

a = np.vstack(a*3)
a

array([[ 3,  6,  9],
       [21, 24, 27]])

np.hstack

a = np.hstack([x, y])
a

array([1, 2, 3, 7, 8, 9])

数组分割

np.split

x = np.array([[11, 12, 13, 14],
              [16, 17, 18, 19],
              [21, 22, 23, 24]])
y = np.vsplit(x, 3)
print(y)

[array([[11, 12, 13, 14]]), array([[16, 17, 18, 19]]), array([[21, 22, 23, 24]])]

np.vsplit

y = np.vsplit(x, [1])
print(y)

[array([[11, 12, 13, 14]]), array([[16, 17, 18, 19],
       [21, 22, 23, 24]])]

np.hsplit

x = np.array([[11, 12, 13, 14],
              [16, 17, 18, 19],
              [21, 22, 23, 24]])
y = np.hsplit(x, 2)
print(y)

[array([[11, 12],
       [16, 17],
       [21, 22]]), array([[13, 14],
       [18, 19],
       [23, 24]])]

数组平铺

np.tile

a = np.arange(6).reshape(2, -1)
np.tile(a, (2, 1))  # 在行方向上堆叠2次，列方向上堆叠1次

array([[0, 1, 2],
       [3, 4, 5],
       [0, 1, 2],
       [3, 4, 5]])

np.repeat

a = np.array([1, 2, 3])
np.repeat(a, 3)  # 将数组 a 的每个元素重复3次

array([1, 1, 1, 2, 2, 2, 3, 3, 3])

数组去重

a = np.array([2, 1, 3, 3, 4, 1, 2])
np.unique(a) # 计算数组a中的唯一值，并排序

array([1, 2, 3, 4])