np.r_：是按列连接两个矩阵，就是把两矩阵上下相加，要求列数相等，类似于pandas中的concat()。
np.c_：是按行连接两个矩阵，就是把两矩阵左右相加，要求行数相等，类似于pandas中的merge()。
import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) c = np.c_[a,b] print(np.r_[a,b]) print(' ') print(c) print(' ') print(np.c_[c,a]) ################################ 结果： [1 2 3 4 5 6] [[1 4] [2 5] [3 6]] [[1 4 1] [2 5 2] [3 6 3]]
其它函数
a = np.arange( 10, 30, 5 ) print(a) # [10 15 20 25] a = np.arange(15).reshape(3, 5) print(a) # [[ 0 1 2 3 4] # [ 5 6 7 8 9] # [10 11 12 13 14]] a = np.array([1,2,3,4,5,6,7,8,9]).reshape(3,3) print(a) # [[1 2 3] # [4 5 6] # [7 8 9]] a = np.zeros((3,4)) print(a) # [[0. 0. 0. 0.] # [0. 0. 0. 0.] # [0. 0. 0. 0.]] a = np.linspace( 0, 2, 9 ) print(a) # [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ] a = np.ones(3, dtype=np.int32) print(a) # [1 1 1] a = np.ones((2,3), dtype=int) print(a) # [[1 1 1] # [1 1 1]]