1、np.vstack() ：垂直合并
>>> import numpy as np >>> A = np.array([1,1,1]) >>> B = np.array([2,2,2]) >>> print(np.vstack((A,B))) # vertical stack,属于一种上下合并,即对括号中的两个整体进行对应操作 [[1 1 1] [2 2 2]] >>> C = np.vstack((A,B)) >>> print(A.shape,C.shape) (3,) (2, 3)
2、np.hstack()：水平合并
>>> D = np.hstack((A,B)) # horizontal stack,即左右合并 >>> print(D) [1 1 1 2 2 2] >>> print(A.shape,D.shape) (3,) (6,)
3、np.newaxis():转置
>>> print(A[np.newaxis,:]) [[1 1 1]] >>> print(A[np.newaxis,:].shape) (1, 3) >>> print(A[:,np.newaxis]) [[1] [1] [1]] >>> print(A[:,np.newaxis].shape) (3, 1) >>> A = np.array([1,1,1])[:,np.newaxis] >>> B = np.array([2,2,2])[:,np.newaxis] >>> C = np.vstack((A,B)) # vertical stack >>> D = np.hstack((A,B)) # horizontal stack >>> print(D) [[1 2] [1 2] [1 2]] >>> print(A.shape,D.shape) (3, 1) (3, 2)
4、np.concatenate():针对多个矩阵或序列的合并操作
#axis参数很好的控制了矩阵的纵向或是横向打印，相比较vstack和hstack函数显得更加 >>> C = np.concatenate((A,B,B,A),axis=0) >>> print(C) [[1] [1] [1] [2] [2] [2] [2] [2] [2] [1] [1] [1]] >>> D = np.concatenate((A,B,B,A),axis=1) >>> print(D) [[1 2 2 1] [1 2 2 1] [1 2 2 1]]