简 介： 在numpy中对于矩阵存在r_,c_,s_三个操作，r_是基本的操作，c_是相当于r_[’-1,2,0’,a,a]的操作，s_则只是生成索引的操作。

关键词： 矩阵链接操作

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三个索引操作
目 录
Contents
简单来看
r_ 操作
s_ 操作
总 结

§01 三个索引操作

1.1 简单来看

1.1.1 对于1D数组

  这三个用于对numpy中array的操作，并不是函数，而是对阵列进行链接操作。

  • numpy.r_: 是把原数组按照行（row）进行串联； numpy.r_ : https://numpy.org/doc/stable/reference/generated/numpy.r_.html
  • numpy.c_: 是吧原书按照列（column）串联； numpy.c_ : https://numpy.org/doc/stable/reference/generated/numpy.c_.html
from numpy import *a = array([1,2,3,4])
b = array([5,6,7,8])
c = r_[a,b]
d = c_[a,b]
print("c: {}".format(c),"d: {}".format(d))

c: [1 2 3 4 5 6 7 8]
d: [[1 5][2 6][3 7][4 8]]

1.1.2 对于2D数组

（1）1×N数组

import sys,os,math,time
import matplotlib.pyplot as plt
from numpy import *a = array([[1,2,3,4]])
b = array([[5,6,7,8]])
c = r_[a,b]
d = c_[a,b]
print("c: {}".format(c),"d: {}".format(d))

c: [[1 2 3 4][5 6 7 8]]
d: [[1 2 3 4 5 6 7 8]]

（2）N×1数组

import sys,os,math,time
import matplotlib.pyplot as plt
from numpy import *a = array([1,2,3,4]).reshape(4,1)
b = array([5,6,7,8]).reshape(4,1)
c = r_[a,b]
d = c_[a,b]
print("c: {}".format(c),"d: {}".format(d))

c: [[1][2][3][4][5][6][7][8]]
d: [[1 5][2 6][3 7][4 8]]

（3）N×M数组

import sys,os,math,time
import matplotlib.pyplot as plt
from numpy import *a = array([1,2,3,4]).reshape(2,2)
b = array([5,6,7,8]).reshape(2,2)
c = r_[a,b]
d = c_[a,b]
print("c: {}".format(c),"d: {}".format(d))

c: [[1 2][3 4][5 6][7 8]]
d: [[1 2 5 6][3 4 7 8]]

1.2 r_ 操作

1.2.1 组合成一组

aa = r_[array([1,2,3]),0,0,array([4,5,6])]
print("aa: {}".format(aa))

aa: [1 2 3 0 0 4 5 6]

aa = r_[-1:1:6j, [0]*3, 5, 6]
print("aa: {}".format(aa))

aa: [-1.  -0.6 -0.2  0.2  0.6  1.   0.   0.   0.   5.   6. ]

1.2.2 带有字符串

  按照最后索引进行连接：

a = array([[0,1,2],[3,4,5]])
print("a: {}".format(a))
aa = r_['-1',a,a]
print("aa: {}".format(aa))

a: [[0 1 2][3 4 5]]
aa: [[0 1 2 0 1 2][3 4 5 3 4 5]]

a = array([[0,1,2],[3,4,5]])
print("a: {}".format(a))
aa = r_['0',a,a]
print("aa: {}".format(aa))

a: [[0 1 2][3 4 5]]
aa: [[0 1 2][3 4 5][0 1 2][3 4 5]]

a = array([[0,1,2],[3,4,5]]).reshape(6)
print("a: {}".format(a))
aa = r_['0,2',a,a]
print("aa: {}".format(aa))

a: [0 1 2 3 4 5]
aa: [[0 1 2 3 4 5][0 1 2 3 4 5]]

a = array([[0,1,2],[3,4,5]]).reshape(6)
print("a: {}".format(a))
aa = r_['r',a,a]
print("aa: {}".format(aa))

a: [0 1 2 3 4 5]
aa: [[0 1 2 3 4 5 0 1 2 3 4 5]]

a = array([[0,1,2],[3,4,5]]).reshape(6)
print("a: {}".format(a))
aa = r_['c',a,a]
print("aa: {}".format(aa))

a: [0 1 2 3 4 5]
aa: [[0][1][2][3][4][5][0][1][2][3][4][5]]

a = array([[0,1,2],[3,4,5]]).reshape(6)
print("a: {}".format(a))
aa = r_['1,2,0',a,a]
print("aa: {}".format(aa))

a: [0 1 2 3 4 5]
aa: [[0 0][1 1][2 2][3 3][4 4][5 5]]

a = array([[0,1,2],[3,4,5]]).reshape(6)
print("a: {}".format(a))
aa = r_['0,2,0',a,a]
print("aa: {}".format(aa))

a: [0 1 2 3 4 5]
aa: [[0][1][2][3][4][5][0][1][2][3][4][5]]

  通过上面例子可以看到， c_[a,a] 操作相当于 r_[’-1,2,0’,a,a]的操作。

1.3 s_ 操作

  • index_exp
      Predefined instance that always returns a tuple: index_exp = IndexExpression(maketuple=True).

  • s_
      Predefined instance without tuple conversion: s_ = IndexExpression(maketuple=False).

a = arange(0,10,1)
print("a: {}".format(a))
ac = s_[2::2]
b = a[ac]
print("b: {}".format(b))

a: [0 1 2 3 4 5 6 7 8 9]
b: [2 4 6 8]

※ 总  结 ※

  在numpy中对于矩阵存在r_,c_,s_三个操作，r_是基本的操作，c_是相当于r_[’-1,2,0’,a,a]的操作，s_则只是生成索引的操作。

■ 相关文献链接:

  • numpy.r_
  • numpy.c_

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