numpy中的拼接、堆叠、差分

#拼接
import numpy as np

a = np.arange(1,25).reshape(2,3,4)
b = np.arange(101,125).reshape(2,3,4)
print('axis = 0')
c = np.concatenate((a,b), axis = 0)
print(c)
print(c.shape)
print('axis = 1')
c = np.concatenate((a,b), axis = 1)
print(c)
print(c.shape)
c = np.concatenate((a,b), axis = 2)
print(c)
print(c.shape)
输出

axis = 0
[[[  1   2   3   4]
  [  5   6   7   8]
  [  9  10  11  12]]

 [[ 13  14  15  16]
  [ 17  18  19  20]
  [ 21  22  23  24]]

 [[101 102 103 104]
  [105 106 107 108]
  [109 110 111 112]]

 [[113 114 115 116]
  [117 118 119 120]
  [121 122 123 124]]]
(4, 3, 4)
axis = 1
[[[  1   2   3   4]
  [  5   6   7   8]
  [  9  10  11  12]
  [101 102 103 104]
  [105 106 107 108]
  [109 110 111 112]]

 [[ 13  14  15  16]
  [ 17  18  19  20]
  [ 21  22  23  24]
  [113 114 115 116]
  [117 118 119 120]
  [121 122 123 124]]]
(2, 6, 4)
[[[  1   2   3   4 101 102 103 104]
  [  5   6   7   8 105 106 107 108]
  [  9  10  11  12 109 110 111 112]]

 [[ 13  14  15  16 113 114 115 116]
  [ 17  18  19  20 117 118 119 120]
  [ 21  22  23  24 121 122 123 124]]]
(2, 3, 8)

 

#堆叠

# 数组堆叠
#Vstack最高维增加
#hstack最低维添加
a = np.arange(5)    # a为一维数组，5个元素
b = np.arange(5,9) # b为一维数组,4个元素
ar1 = np.hstack((a,b))  # 注意:((a,b))，这里形状可以不一样
print(a,a.shape)
print(b,b.shape)
print(ar1,ar1.shape)
a = np.array([[1],[2],[3]])   # a为二维数组，3行1列
b = np.array([['a'],['b'],['c']])  # b为二维数组，3行1列
ar2 = np.hstack((a,b))  # 注意:((a,b))，这里形状必须一样
print(a,a.shape)
print(b,b.shape)
print(ar2,ar2.shape)
print('-----')
# numpy.hstack(tup)：水平（按列顺序）堆叠数组

a = np.arange(5)    
b = np.arange(5,10)
ar1 = np.vstack((a,b))
print(a,a.shape)
print(b,b.shape)
print(ar1,ar1.shape)
a = np.array([[1],[2],[3]])   
b = np.array([['a'],['b'],['c'],['d']])   
ar2 = np.vstack((a,b))  # 这里形状可以不一样
print(a,a.shape)
print(b,b.shape)
print(ar2,ar2.shape)
print('-----')
# numpy.vstack(tup)：垂直（按列顺序）堆叠数组

a = np.arange(5)    
b = np.arange(5,10)
ar1 = np.stack((a,b))
ar2 = np.stack((a,b),axis = 1)
print(a,a.shape)
print(b,b.shape)
print(ar1,ar1.shape)
print(ar2,ar2.shape)
# numpy.stack(arrays, axis=0)：沿着新轴连接数组的序列，形状必须一样！
# 重点解释axis参数的意思，假设两个数组[1 2 3]和[4 5 6]，shape均为(3,0)
# axis=0：[[1 2 3] [4 5 6]]，shape为(2,3)
# axis=1：[[1 4] [2 5] [3 6]]，shape为(3,2)

 

#拆分
import numpy as np
a = np.arange(1,37).reshape(3,3,4)
print(a)
print('-'*50)

print(np.split(a,(1,2),axis = 0))
print('axis=0')
print('-'*50)
axis=0等价于vsplit
print(np.split(a,(1,2),axis = 1))
print('axis=1')
print('-'*50)
axis=1等价于hsplit
print(np.split(a,(1,2),axis =2))
print('axis=2')
axis = 2等价于dsplit

F:\anaconda3\az2\python.exe G:/0work_study/3deep/学习资料/sxt/numpy代码/ex.py
[[[ 1  2  3  4]
  [ 5  6  7  8]
  [ 9 10 11 12]]

 [[13 14 15 16]
  [17 18 19 20]
  [21 22 23 24]]

 [[25 26 27 28]
  [29 30 31 32]
  [33 34 35 36]]]
--------------------------------------------------
[array([[[ 1,  2,  3,  4],
        [ 5,  6,  7,  8],
        [ 9, 10, 11, 12]]]),
 array([[[13, 14, 15, 16],
        [17, 18, 19, 20],
        [21, 22, 23, 24]]]), 
array([[[25, 26, 27, 28],
        [29, 30, 31, 32],
        [33, 34, 35, 36]]])]
axis=0
--------------------------------------------------
[array([[[ 1,  2,  3,  4]],

       [[13, 14, 15, 16]],

       [[25, 26, 27, 28]]]), 
array([[[ 5,  6,  7,  8]],

       [[17, 18, 19, 20]],

       [[29, 30, 31, 32]]]), 
array([[[ 9, 10, 11, 12]],

       [[21, 22, 23, 24]],

       [[33, 34, 35, 36]]])]
axis=1
--------------------------------------------------
[array([[[ 1],
        [ 5],
        [ 9]],

       [[13],
        [17],
        [21]],

       [[25],
        [29],
        [33]]]), 
array([[[ 2],
        [ 6],
        [10]],

       [[14],
        [18],
        [22]],

       [[26],
        [30],
        [34]]]), 
array([[[ 3,  4],
        [ 7,  8],
        [11, 12]],

       [[15, 16],
        [19, 20],
        [23, 24]],

       [[27, 28],
        [31, 32],
        [35, 36]]])]
axis=2

Process finished with exit code 0