#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:anaconda3az2python.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