python:numpy-1:fundamental knowledge

Posted 2020-10-20 xy小崽子

import numpy as np
a=np.array([1,2,3])
print a
b=np.array([[1,2],[3,4]])
print b
print np.arange(1,5,0.5)#取不到 5
print np.random.random((2,2))#2*2随机浮点数数组
print np.linspace(1,2,10,endpoint=False)
print np.linspace(1,2,10) #默认2能够取到
print np.fromfunction(lambda i,j:(i+1)*(j+1),[2,3])

c=np.array([4,5,6])
print np.hstack((a,c))#横向连接
print np.vstack((a,c))#纵向连接
print b.T #转置
print a+c#对应元素相加

ans:

[1 2 3]
[[1 2]
 [3 4]]
[ 1.   1.5  2.   2.5  3.   3.5  4.   4.5]
[[ 0.54937261  0.22419401]
 [ 0.41047764  0.53122061]]
[ 1.   1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9]
[ 1.          1.11111111  1.22222222  1.33333333  1.44444444  1.55555556
  1.66666667  1.77777778  1.88888889  2.        ]
[[ 1.  2.  3.]
 [ 2.  4.  6.]]
[1 2 3 4 5 6]
[[1 2 3]
 [4 5 6]]
[[1 3]
 [2 4]]

 

通过list生成数组:

import numpy as np
a=np.array([1,2,3])
x=[1,2,3]
xx=np.array(x)
print xx

y=[[1,2],[3,4]]
yy=np.array(y)
print yy
print yy.flatten()#展平,不能作用于列表

ans:
[1 2 3]
[[1 2]
 [3 4]]
[1 2 3 4]

 

b=np.array([[1,2],[3,4]])
print b.sum()
print b.sum(axis=0)#按列求和
print b.sum(axis=1)#按行求和
print b.min()
print b.argmax()#max的下标
print b.mean()
print b.std()
b[1]
Out[117]: array([3, 4])

b[1][1]
Out[118]: 4

ans:

10
[4 6]
[3 7]
1
3

2.5
1.11803398875

remark:数组的属性函数不需要末尾加()

b=np.array([[1,2],[3,4]])
print b.size#元素个数
print b.ndim#秩
print b.shape
print b.dtype#元素类型
ans:
4
2
(2L, 2L)
int32

 numpy比math的计算速度更快:

import numpy as np
import time
import math
x=np.arange(0,100,0.01)
t1=time.clock()
for i,t in enumerate(x):
    x[i]=math.pow((math.sin(t)),2)
t2=time.clock()
y=np.arange(0,100,0.01)
t3=time.clock()
y=np.power(np.sin(y),2)
t4=time.clock()
print(‘running time of math is:‘,t2-t1)
print(‘running time of numpy is:‘,t4-t3)

ans:
(‘running time of math is:‘, 0.011655904997766612)
(‘running time of numpy is:‘, 0.0023625201675097404)

numpy与scipy的结合使用:

import numpy as np
from scipy import linalg
a=np.array([[1,0],[1,2]])
print np.dot(a,a)
print linalg.det(a)
print linalg.inv(a)
x,y=linalg.eig(a)
print x #特征值
print y#特征向量??奇怪的值,随便给出的一个特征向量

ans:
[[1 0]
 [3 4]]
2.0
[[ 1.  -0. ]
 [-0.5  0.5]]
[ 2.+0.j  1.+0.j]
[[ 0.          0.70710678]
 [ 1.         -0.70710678]]

import numpy as np
from scipy.cluster.vq import vq,kmeans,whiten
list1=[89,90,76,90]
list2=[96,78,89,79]
list3=[90,98,89,80]
list4=[80,72,79,84]
list5=[92,81,89,87]
data=np.array([list1,list2,list3,list4,list5])
print data
whiten=whiten(data)
centroids,_=kmeans(whiten,2)
result,_=vq(whiten,centroids)
print(result)

ans:
[[89 90 76 90]
 [96 78 89 79]
 [90 98 89 80]
 [80 72 79 84]
 [92 81 89 87]]
[1 0 0 1 0]

 

 

三维数组:

import numpy as np
b=np.arange(24).reshape(2,3,4)
print b
print b[1]#访问第二层
print b[0,1,::2]#从头开始,间隔是2
print b[0,1,[1,2]]
print b[::-1]#两层交换
print b.ravel()#展平变一维的数组
print b.reshape(6,4)
x=np.array([[1,2,3],[4,5,6],[7,8,9]])
#分割数组
x1,x2,x3=np.hsplit(x,3) 
print x1 #得到[1,4,7]
y1,y2,y3=np.vsplit(x,3)
print y1#得到[1,2,3]
z=np.array([1+2.j,3+4.j])
print z.real
print z.tolist()
print x.tolist()

ans:
[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]
[[12 13 14 15]
 [16 17 18 19]
 [20 21 22 23]]
[4 6]
[5 6]
[[[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]

 [[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]]
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]
 [16 17 18 19]
 [20 21 22 23]]
[[1]
 [4]
 [7]]
[[1 2 3]]
[ 1.  3.]
[(1+2j), (3+4j)]
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]