numpy中数组的堆叠方法

>>> import numpy as np
# 数据准备
>>> data = [i for i in range(18)]
>>> data_array = np.asarray(data)
>>> data_array = np.asarray(data).reshape([6,3])
>>> data_array.shape
(6, 3)
>>> data_array
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11],
       [12, 13, 14],
       [15, 16, 17]])
# 标签准备
>>> label = [0, 1] *3 
>>> label_array = np.asarray(label)
>>> label_array.shape
(6,)
>>> label_array
array([0, 1, 0, 1, 0, 1])
# 在数据的右侧水平方向上合并标签
>>> data_label = np.hstack((data_array,label_array))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/numpy/core/shape_base.py", line 288, in hstack
    return _nx.concatenate(arrs, 1)
ValueError: all the input arrays must have same number of dimensions

# 在准备标签时，先将一维的标签 reshape 为二维 numpy 数组，即 6 行 1 列
>>> label_array = label_array.reshape(-1,1)
>>> data_label = np.hstack((data_array,label_array))
>>> data_label.shape
(6, 4)
>>> data_label
array([[ 0,  1,  2,  0],
       [ 3,  4,  5,  1],
       [ 6,  7,  8,  0],
       [ 9, 10, 11,  1],
       [12, 13, 14,  0],
       [15, 16, 17,  1]])

# 准备第一数据集
>>> import numpy as np
>>> data1 = np.random.nORMal(0,1,(2,5))
>>> data1.shape
(2, 5)
>>> data1
array([[-1.49100993,  0.03782522,  0.33961941, -0.64073217,  0.84000297],
       [-1.02662855, -0.91858614, -0.27410549, -0.86956142, -0.44147313]])
# 准备第二个数据集
>>> data2 = np.arange(0,30,2)
>>> data2 = np.arange(0,30,2).reshape([3,5])
>>> data2.shape
(3, 5)
>>> data2
array([[ 0,  2,  4,  6,  8],
       [10, 12, 14, 16, 18],
       [20, 22, 24, 26, 28]])
# 垂直方向堆叠连个数据集
>>> data = np.vstack((data1,data2))
>>> data.shape
(5, 5)
>>> data
array([[-1.49100993,  0.03782522,  0.33961941, -0.64073217,  0.84000297],
       [-1.02662855, -0.91858614, -0.27410549, -0.86956142, -0.44147313],
       [ 0.        ,  2.        ,  4.        ,  6.        ,  8.        ],
       [10.        , 12.        , 14.        , 16.        , 18.        ],
       [20.        , 22.        , 24.        , 26.        , 28.        ]])

# 准备第一个数据集
>>> data_v1 = np.random.randint(0,10,(2,5))
>>> data_v1.shape
(2, 5)
>>> data_v1
array([[4, 4, 0, 7, 3],
       [3, 9, 0, 3, 0]])
# 准备第二个数据集
>>> data_v2 = np.ones((3,5))
>>> data_v2.shape
(3, 5)
>>> data_v2
array([[1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.]])
# 准备第三个数据集
>>> data_v3 = np.full((2,5),0)
>>> data_v3.shape
(2, 5)
>>> data_v3
array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]])
# 定义一个临时存放多个数据集的列表（list），并将所有数据集添加到列表中
>>> data_vlist = []
>>> data_vlist.append(data_v1)
>>> data_vlist.append(data_v2)
>>> data_vlist.append(data_v3)
>>> len(data_vlist)
3
>>> data_vlist
[array([[4, 4, 0, 7, 3],
       [3, 9, 0, 3, 0]]), array([[1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.]]), array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]])]
# 将存放所有数据集的列表作为 np.vstack() 的输入参数，即可一次性合并多个数据集
>>> data_vstack = np.vstack(data_vlist)
>>> data_vstack
array([[4., 4., 0., 7., 3.],
       [3., 9., 0., 3., 0.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])
>>> data_vstack.shape
(7, 5)
>>> data_vstack
array([[4., 4., 0., 7., 3.],
       [3., 9., 0., 3., 0.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])
#########################################################

# 准备首个数列
>>> import numpy as np
>>> data_h1 = np.random.randint(0, 10, (3,3))
>>> data_h1.shape
(3, 3)
>>> data_h1 
array([[6, 4, 5],
       [4, 5, 0],
       [7, 1, 9]])
# 准备第二个数列
>>> data_h2 = np.zeros((3,2))
>>> data_h2.shape
(3, 2)
>>> data_h2
array([[0., 0.],
       [0., 0.],
       [0., 0.]])
# 准备第三个数列
>>> data_h3 = np.ones((3,1), dtype=int)
>>> data_h3.shape
(3, 1)
>>> data_h3
array([[1],
       [1],
       [1]])
# 定义一个临时存放多个数据列的列表（list），并将所有数据列添加到列表中
>>> data_hlist = []
>>> data_hlist.append(data_h1)
>>> data_hlist.append(data_h2)
>>> data_hlist.append(data_h3)
>>> len(data_hlist)
3
>>> data_hlist
[array([[6, 4, 5],
       [4, 5, 0],
       [7, 1, 9]]), array([[0., 0.],
       [0., 0.],
       [0., 0.]]), array([[1],
       [1],
       [1]])]
# 将存放所有数据列的列表作为 np.hstack() 的输入参数，即可一次性合并多个数据列
>>> data_hstack = np.hstack(data_hlist)
>>> data_hstack.shape
(3, 6)
>>> data_hstack
array([[6., 4., 5., 0., 0., 1.],
       [4., 5., 0., 0., 0., 1.],
       [7., 1., 9., 0., 0., 1.]])