需要对numy的数组进行合并，做以下整理：
1、"+"合并
c1 = ["Red","Green","Blue"]
c2 =["Orange","Yellow","Indigo"]
c3 = c1 + c2
=>
c3 ==["Red","Green","Blue","Orange","Yellow","Indigo"]
2、append
特点：可读性好，比较灵活，但是占内存大（主页复制）。参数形式如下：
1）不同维度数组合并，未指定axis
import numpy as np
a = np.array([1, 2, 3])
b = np.array([[4, 5, 6], [7, 8, 9]])
c = np.append(a, b)
print("c =", c)
=>结果合并为一行
2）不同行、相同列数，同纬度，按列合并axis=0
import numpy as np
a = np.array([[1, 2, 3]])
b = np.array([[4, 5, 6], [7, 8, 9]])
c = np.append(a, b, axis=0)
print("c =", c)
=>结果合并三行
3）相同行、不同列，同纬度，按行合并axis=1
a = np.array([[1, 2], [3, 4]])
b = np.array([[4, 5, 6], [7, 8, 9]])
c = np.append(a, b, axis=1)
print("c =", c)
=>结果合并为5列
3、concatenate
特点：没有内存问题
1）列上合并，axis=0
import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
c = np.concatenate((a, b), axis=0)
print("c =", c)
2）行上合并，axis=1
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
c = np.concatenate((a, b.T), axis=1)
print("b.T =", b.T)
print("c =", c)
3）实例应用，多维矩阵list扩展
a = np.array([[[1, 2], [1, 2]], [[3, 4], [1, 2]]])
b = np.array([[[5, 6], [5, 6]], [[7, 8], [5, 6]]])
c = np.concatenate((a, b))
print("c =", c)
