numpy

 1 #python
 2 
 3 import numpy as np
 4 
 5 ＃　创建ndarray 对象
 6 a = np.arange(6)
 7 print(a.shape)
 8 print(a.dtype)
 9 type(a)
10 
11 b = np.array([1,1,2,4,5,6,0,7])
12 
13 c = np.array([np.arange(3),np.arange(3)])
14 print(c.shape)
15 print(c.dtype)
16 
17 d = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[0,4,6]]] , dtype=np.float32)
18 print(d.shape)
19 print(d.dtype)
20 
21 e = np.zeros(5)
22 print(e.shape)
23 print(e.dtype)    #dtype(‘float64‘)
24 
25 f = np.ones([2,6],dtype=np.int64)
26 print(f.shape)   #(2, 6)
27 print(f.dtype)
28 
29 
30 ＃－－－－　索引和切片
31 a = np.arange(9)
32 a[3:7]    # array([3, 4, 5, 6])    包含左边的索引，不包含右边的索引
33 a[0:7:2]  #array([0, 2, 4, 6])    以２为步长
34 a[7::-1]   #array([7, 6, 5, 4, 3, 2, 1, 0])  以-1为步长
35 a[7:0:-1]  #array([7, 6, 5, 4, 3, 2, 1]) 
36 
37 b = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[0,4,6]]])
38 b[0,1:,:]

 1 # python
 2 
 3 import numpy as np
 4 b = np.arange(24).reshape(2,3,4)
 5 print(b)
 6 
 7 print(b.flatten())
 8 
 9 b.shape = (6,4)  # 可以直接改变维度
10 
11 b.transpose()
12 b.resize((2,12))
13 
14  

 1 import numpy as np
 2 
 3 # ----- 组合　np.concatenate　更加灵活
 4 a = np.arange(9).reshape(3,3)
 5 
 6 b = a**2
 7 
 8 # 水平组合
 9 c = np.hstack((a,b))
10 c2 = np.concatenate((a,b),axis=1)
11 
12 # 垂直组合
13 d = np.vstack((a,b))
14 d2 = np.concatenate((a,b),axis=0)
15 print(d2)
16 
17 # 深度组合
18 e = np.dstack((a,b))
19 
20 #----- 分割　np.split() 更加灵活
21 a = np.array([[1,4,6],[2,4,12],[3,9,22]])
22 print(a)
23 
24 # 分割
25 print( np.split(a,3,axis=0) )
26 print( np.split(a,3,axis=1) )