菜鸟自学机器学习3.Python-Numpy

所使用的csv文件数据：

duration,protocol_type,service,flag,src_bytes,dst_bytes,land,wrong_fragment,urgent,hot,num_failed_logins,logged_in,num_compromised,root_shell,su_attempted,num_root,num_file_creations,num_shells,num_access_files,num_outbound_cmds,is_hot_login,is_guest_login,count,srv_count,serror_rate,srv_serror_rate,rerror_rate,srv_rerror_rate,same_srv_rate,diff_srv_rate,srv_diff_host_rate,dst_host_count,dst_host_srv_count,dst_host_same_srv_rate,dst_host_diff_srv_rate,dst_host_same_src_port_rate,dst_host_srv_diff_host_rate,dst_host_serror_rate,dst_host_srv_serror_rate,dst_host_rerror_rate,dst_host_srv_rerror_rate,

0,tcp,http,SF,215,45076,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,normal.
0,tcp,http,SF,162,4528,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,1,1,1,0,1,0,0,0,0,0,normal.
0,tcp,http,SF,236,1228,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,2,2,1,0,0.5,0,0,0,0,0,normal.
0,tcp,http,SF,233,2032,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,3,3,1,0,0.33,0,0,0,0,0,normal.
0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,1,0,0,4,4,1,0,0.25,0,0,0,0,0,normal.
0,tcp,http,SF,238,1282,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,1,0,0,5,5,1,0,0.2,0,0,0,0,0,normal.
0,tcp,http,SF,235,1337,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,5,0,0,0,0,1,0,0,6,6,1,0,0.17,0,0,0,0,0,normal.
0,tcp,http,SF,234,1364,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0,0,0,0,1,0,0,7,7,1,0,0.14,0,0,0,0,0,normal.
0,tcp,http,SF,239,1295,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,7,0,0,0,0,1,0,0,8,8,1,0,0.12,0,0,0,0,0,normal.
0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0,0,0,0,1,0,0,9,9,1,0,0.11,0,0,0,0,0,normal.
0,tcp,http,SF,184,124,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,10,10,1,0,0.1,0,0,0,0,0,normal.
0,tcp,http,SF,185,9020,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,11,11,1,0,0.09,0,0,0,0,0,normal.
0,tcp,http,SF,239,1295,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,12,12,1,0,0.08,0,0,0,0,0,normal.
0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,13,13,1,0,0.08,0,0,0,0,0,normal.
0,tcp,http,SF,236,1228,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,1,0,0,14,14,1,0,0.07,0,0,0,0,0,normal.
0,tcp,http,SF,233,2032,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,1,0,0,15,15,1,0,0.07,0,0,0,0,0,normal.
0,tcp,http,SF,238,1282,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,5,0,0,0,0,1,0,0,16,16,1,0,0.06,0,0,0,0,0,normal.
0,tcp,http,SF,235,1337,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0,0,0,0,1,0,0,17,17,1,0,0.06,0,0,0,0,0,normal.
0,tcp,http,SF,234,1364,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,7,0,0,0,0,1,0,0,18,18,1,0,0.06,0,0,0,0,0,normal.

0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0,0,0,0,1,0,0,19,19,1,0,0.05,0,0,0,0,0,normal.

1.创建数组与向量与处理

import numpy 
vector=numpy.array([5,7,8,9]) #创建一个向量
matrix=numpy.array([[12,21,55],[99,65,75],[25,74,36]]) #创建一个数组
print(vector)
print("===============================================================================================")

print(matrix)

运行结果：

[5 7 8 9]
===============================================================================================
[[12 21 55]
 [99 65 75]
 [25 74 36]]

import numpy as np
numbers=np.array([1,5,8,6,9])
matrix=np.array([[12,21,55],[99,65,75],[25,74,36],[27,74,66]])
print(numbers)
print("===============================================================================================")
print (numbers.shape)  #显示向量元素的个数
print (matrix.shape)   #显示数组行和列的个数

运行结果：

[1 5 8 6 9]
===============================================================================================
(5,)
(4, 3)

2.csv文件读取与数据处理

cup99=numpy.genfromtxt("F:\study.csv",delimiter=",",dtype=str,skip_header=1) #第二个参数表示以","为分隔符，第三个参数表示以字符串格式读取，
#第四个参数表示去除第一行数据

print(cup99)

输出结果：

[['0' 'tcp' 'http' 'SF' '215' '45076' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '1' '1' '0' '0' '0' '0' '1' '0' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '162' '4528' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '2' '2' '0' '0' '0' '0' '1' '0' '0' '1' '1'
  '1' '0' '1' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '236' '1228' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '1' '1' '0' '0' '0' '0' '1' '0' '0' '2' '2'
  '1' '0' '0.5' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '233' '2032' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '2' '2' '0' '0' '0' '0' '1' '0' '0' '3' '3'
  '1' '0' '0.33' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '239' '486' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '3' '3' '0' '0' '0' '0' '1' '0' '0' '4' '4'
  '1' '0' '0.25' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '238' '1282' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '4' '4' '0' '0' '0' '0' '1' '0' '0' '5' '5'
  '1' '0' '0.2' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '235' '1337' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '5' '5' '0' '0' '0' '0' '1' '0' '0' '6' '6'
  '1' '0' '0.17' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '234' '1364' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '6' '6' '0' '0' '0' '0' '1' '0' '0' '7' '7'
  '1' '0' '0.14' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '239' '1295' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '7' '7' '0' '0' '0' '0' '1' '0' '0' '8' '8'
  '1' '0' '0.12' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '181' '5450' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '8' '8' '0' '0' '0' '0' '1' '0' '0' '9' '9'
  '1' '0' '0.11' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '184' '124' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '1' '1' '0' '0' '0' '0' '1' '0' '0' '10'
  '10' '1' '0' '0.1' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '185' '9020' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '2' '2' '0' '0' '0' '0' '1' '0' '0' '11'
  '11' '1' '0' '0.09' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '239' '1295' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '1' '1' '0' '0' '0' '0' '1' '0' '0' '12'
  '12' '1' '0' '0.08' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '181' '5450' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '2' '2' '0' '0' '0' '0' '1' '0' '0' '13'
  '13' '1' '0' '0.08' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '236' '1228' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '3' '3' '0' '0' '0' '0' '1' '0' '0' '14'
  '14' '1' '0' '0.07' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '233' '2032' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '4' '4' '0' '0' '0' '0' '1' '0' '0' '15'
  '15' '1' '0' '0.07' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '238' '1282' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '5' '5' '0' '0' '0' '0' '1' '0' '0' '16'
  '16' '1' '0' '0.06' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '235' '1337' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '6' '6' '0' '0' '0' '0' '1' '0' '0' '17'
  '17' '1' '0' '0.06' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '234' '1364' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '7' '7' '0' '0' '0' '0' '1' '0' '0' '18'
  '18' '1' '0' '0.06' '0' '0' '0' '0' '0' 'normal.']
 ['0' 'tcp' 'http' 'SF' '239' '486' '0' '0' '0' '0' '0' '1' '0' '0' '0'
  '0' '0' '0' '0' '0' '0' '0' '8' '8' '0' '0' '0' '0' '1' '0' '0' '19'
  '19' '1' '0' '0.05' '0' '0' '0' '0' '0' 'normal.']]

 筛选列表中的元素

#cup99=numpy.genfromtxt("F:\study.csv",delimiter=",",dtype=str,skip_header=1) #第二个参数表示以","为分隔符，第三个参数表示以字符串格式读取，
cup99_sel1=cup99[0,5] #取出第0行第4列
cup99_sel2=cup99[2,5] #取出第2行第4列
print (cup99_sel1)

print (cup99_sel2)

输出结果：

45076
1228

3.筛选与处理向量与数组中的数据

向量中筛选

vector = numpy.array([5,10,15,87,66])
print(vector[2:-1]) #选择numpy中的数 第一个参数指起始位置，第二个参数指终止位置

输出结果：

[15 87]

数组中筛选

matrix = numpy.array([
    [51,8,12],
    [87,12,89],
    [78,65,19]
])
print(matrix[:,-1]) #在矩阵中筛选元素 “:”表示取所有 第一个参数表示行 第二个参数表示列
print("-----------------------------------------------------------------------------------")
print(matrix[0:2,1:2]) #表示取0-2行 中的 1-2 列

输出结果：

[12 89 19]
-----------------------------------------------------------------------------------
[[ 8]
 [12]]

数组与向量进行判断：

matrix = numpy.array([
    [51,8,12],
    [87,12,89],
    [78,65,19]
])
matrix_eight=numpy.array([matrix[:,1:2]])
print(matrix_eight)
print("-----------------------------------------------------------------------------------")
equal_to_eight=((matrix_eight ==12) )   # = = 相当于判断是否相等 相等为true 不相等为false
equal_to_one=((matrix_eight ==8) | (matrix_eight ==16))
print(equal_to_eight)
print("-----------------------------------------------------------------------------------")
print(matrix_eight[equal_to_eight])
print("-----------------------------------------------------------------------------------")
print(matrix_eight[equal_to_one]) # 将判断好的Boolean进行查找会对于输出匹配的值
print("-----------------------------------------------------------------------------------")
matrix_eight[equal_to_one]=9999 #更改已经选择的值

print(matrix_eight)

输出结果：

[[[ 8]
  [12]
  [65]]]
-----------------------------------------------------------------------------------
[[[False]
  [ True]
  [False]]]
-----------------------------------------------------------------------------------
[12]
-----------------------------------------------------------------------------------
[8]
-----------------------------------------------------------------------------------
[[[9999]
  [  12]
  [  65]]]

更改数据中的type

vector =numpy.array(["1","2","3"])
print (vector.dtype)
print (vector)
vector=vector.astype(float)  #更改numpy类型
print (vector.dtype)
print (vector)

输出结果：

<U1
['1' '2' '3']
float64

[1. 2. 3.]

查找最大值最小值与求和：

vector =numpy.array([18,98,58,11,12,33])
print (vector.min()) #查找最小值
print(vector.max()) #查找最大值
print("-----------------------------------------------------------------------------------")


admire = numpy.array([
    [51,8,12],
    [87,12,89],
    [78,65,19]
])
matrix.sum(axis=1) 
matrix.sum(axis=0)#axis=0是以列为维度做求和 ，axis=1是以行为维度做求和

输出结果：

11
98
-----------------------------------------------------------------------------------
[216  85 120]

创建数组或向量：

import numpy as np
print(np.arange(15)) #制造一个从0-15的数
print("-----------------------------------------------------------------------------------")
a=np.arange(15).reshape(3,5) #将arange(15)转化成一个3行5列的数组
print(a)

a.shape #打印行列数

np.arange(55,105,6) #创建一个从55开始到105结束，间隔为6 的矩阵

a.ndim #打印矩阵的维度

a.size #打印矩阵内元素个数

输出结果：

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]
-----------------------------------------------------------------------------------
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]

Out[7]:2

(3, 5)

Out[7]:array([ 55,  61,  67,  73,  79,  85,  91,  97, 103])

Out[7]:2

Out[7]:15

4.初始化矩阵

np.zeros((3,4)) #初始化一个为三行四列的0矩阵

输出结果：

array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])

np.ones((2,3,4),dtype=np.int32) #创建一个维度为三，全为1，数据类型为int32的矩阵

输出结果：

array([[[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]],

       [[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]]])

np.random.random((3,3))  #创建一个三行三列，0至1 为元素的随机矩阵

输出结果：

array([[0.82787562, 0.16647778, 0.175306  ],
       [0.23084511, 0.73509222, 0.42879961],
       [0.74894781, 0.25325923, 0.59442485]])

from numpy import pi
np.linspace(0,2*pi,100) #0-2pi 为区间平均的去找数

输出结果：

array([0.        , 0.06346652, 0.12693304, 0.19039955, 0.25386607,
       0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,
       0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,
       0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,
       1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,
       1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,
       1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,
       2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,
       2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,
       2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,
       3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,
       3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,
       3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,
       4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,
       4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,
       4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,
       5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,
       5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,
       5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,
       6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531])

 5.矩阵的加减乘与判断

a=np.array([20,30,40,50])
b=np.arange(4)
c=a-b #矩阵加减法
print(c)
print("-----------------------------------------------------------------------------------")
c=c-1 #减去一个单位矩阵
print(c)
print("-----------------------------------------------------------------------------------")
b**2 #对b平方
print(b)
print("-----------------------------------------------------------------------------------")
print(a<35)#对矩阵进行判断

输出结果：

[20 29 38 47]
-----------------------------------------------------------------------------------
[19 28 37 46]
-----------------------------------------------------------------------------------
[0 1 2 3]
-----------------------------------------------------------------------------------
[ True  True False False]

a=np.arange(9).reshape(3,3)
b=np.arange(9).reshape(3,3)
#print(a.dot(b))
c=np.dot(a,b) #进行矩阵相乘
c

输出结果：

array([[ 15,  18,  21],
       [ 42,  54,  66],
       [ 69,  90, 111]])

6.numpy中常用的函数

a = np.random.random((3,4))
b = np.floor(a*10)  #向下取整
print(a)
print("-----------------------------------------------------------------------------------")
print(b)
print("-----------------------------------------------------------------------------------")
print(b.ravel()) #将矩阵转为向量
b.shape = (6,2)#将向量转为矩阵（行，列）
c=b.T #转置矩阵
print("-----------------------------------------------------------------------------------")
print(c)

输出结果：

[[0.9312249  0.85143506 0.45119295 0.06834031]
 [0.14747723 0.09853935 0.79532103 0.21991663]
 [0.30461187 0.42756043 0.94496506 0.42769361]]
-----------------------------------------------------------------------------------
[[9. 8. 4. 0.]
 [1. 0. 7. 2.]
 [3. 4. 9. 4.]]
-----------------------------------------------------------------------------------
[9. 8. 4. 0. 1. 0. 7. 2. 3. 4. 9. 4.]
-----------------------------------------------------------------------------------
[[9. 4. 1. 7. 3. 9.]
 [8. 0. 0. 2. 4. 4.]]

矩阵拼接

A=np.floor(10*np.random.random((2,2)))
B=np.floor(10*np.random.random((2,2)))
print(A)
print("-----------------------------------------------------------------------------------")
print(B)
print("-----------------------------------------------------------------------------------")
print(np.vstack((A,B))) #对矩阵进行拼接纵向
print("-----------------------------------------------------------------------------------")
print(np.hstack((A,B))) #对矩阵进行拼接横向

输出结果：

[[7. 4.]
 [7. 3.]]
-----------------------------------------------------------------------------------
[[9. 7.]
 [1. 2.]]
-----------------------------------------------------------------------------------
[[7. 4.]
 [7. 3.]
 [9. 7.]
 [1. 2.]]
-----------------------------------------------------------------------------------
[[7. 4. 9. 7.]
 [7. 3. 1. 2.]]

矩阵切分

a=np.floor(10*np.random.random((2,12)))
print(a)
print("-----------------------------------------------------------------------------------")
print(np.hsplit(a,3)) #对a进行横向切分（要操作的矩阵，几份）
print("-----------------------------------------------------------------------------------")
print(np.hsplit(a,(3,4))) #对3，和4 位置切分
print("-----------------------------------------------------------------------------------")
b=np.floor(10*np.random.random((12,2)))

print(np.vsplit(b,3))#对列切分

输出结果：

[[2. 9. 3. 1. 6. 4. 0. 8. 2. 7. 1. 8.]
 [8. 8. 7. 3. 7. 1. 0. 1. 7. 5. 1. 1.]]
-----------------------------------------------------------------------------------
[array([[2., 9., 3., 1.],
       [8., 8., 7., 3.]]), array([[6., 4., 0., 8.],
       [7., 1., 0., 1.]]), array([[2., 7., 1., 8.],
       [7., 5., 1., 1.]])]
-----------------------------------------------------------------------------------
[array([[2., 9., 3.],
       [8., 8., 7.]]), array([[1.],
       [3.]]), array([[6., 4., 0., 8., 2., 7., 1., 8.],
       [7., 1., 0., 1., 7., 5., 1., 1.]])]
-----------------------------------------------------------------------------------
[array([[1., 3.],
       [6., 2.],
       [0., 8.],
       [0., 8.]]), array([[2., 9.],
       [7., 5.],
       [0., 6.],
       [9., 0.]]), array([[3., 8.],
       [6., 2.],
       [6., 0.],
       [8., 7.]])]

查找最大值

import numpy as np
data = np.sin(np.arange(20)).reshape(5,4)
print(data)
ind = data.argmax(axis=0) #寻找最大数的序号索引值，axis=0按列查找，axis=1按行查找
ind

输出结果：

[[ 0.          0.84147098  0.90929743  0.14112001]
 [-0.7568025  -0.95892427 -0.2794155   0.6569866 ]
 [ 0.98935825  0.41211849 -0.54402111 -0.99999021]
 [-0.53657292  0.42016704  0.99060736  0.65028784]
 [-0.28790332 -0.96139749 -0.75098725  0.14987721]]

Out[6]:

array([2, 0, 3, 1], dtype=int64)

矩阵的复制：

a=np.arange(0,40,10)
print(a)
b=np.tile(a,(4,2)) #对矩阵进行复制（行，列）
print(b)

输出结果：

[ 0 10 20 30]
[[ 0 10 20 30  0 10 20 30]
 [ 0 10 20 30  0 10 20 30]
 [ 0 10 20 30  0 10 20 30]
 [ 0 10 20 30  0 10 20 30]]