numpy介绍

一 numpy

1、关键词：开源、数据计算扩展

2、功能：ndarray、多维操作、线性代数

3、官网：http://www.numpy.org

4、安装方法：pip install numpy 

二 代码

1. import numpy as np
2. lst=[[1,3,5],[2,4,6]]
3. print(type(lst))
4. np_lst=np.array(lst)
5. print(type(np_lst))
6. np_lst=np.array(lst,dtype=np.float)
7. print(np_lst.shape)
8. print(np_lst.ndim)
9. print(np_lst.dtype)
10. print(np_lst.itemsize)
11. print(np_lst.size)

三 运行结果

<class 'list'>

<class 'numpy.ndarray'>

(2, 3)

2

float64

8

6

四 numpy常用的Array

1、代码

1. import numpy as np
2. print(np.zeros([2,4]))
3. print(np.ones([3,5]))
4. print("Rand:")
5. print(np.random.rand(2,4))
6. print(np.random.rand())
7. print("RandInt:")
8. print(np.random.randint(1,10))
9. print(np.random.randint(1,10,3))
10. #标准正态随机数
11. print("Randn:")
12. print(np.random.randn(2,4))
13. print("Choice:")
14. print(np.random.choice([10,20,30,2,8]))
15. print("Distribute:")
16. print(np.random.beta(1,10,100))

2、运行结果

1. [[0.0.0.0.]
2. [0.0.0.0.]]
3. [[1.1.1.1.1.]
4. [1.1.1.1.1.]
5. [1.1.1.1.1.]]
6. Rand:
7. [[0.902201410.53545960.90832360.96268421]
8. [0.835276060.034453280.569252350.75789267]]
9. 0.8128122424230132
10. RandInt:
11. 4
12. [725]
13. Randn:
14. [[0.68700498-0.27635927-0.50090856-0.24558428]
15. [1.060551580.074170260.928740251.73472707]]
16. Choice:
17. 20
18. Distribute:
19. [0.105204770.058352280.150815480.068825090.059831220.05248901
20. 0.017116910.065622140.068027130.088252510.178780870.02111876
21. 0.09691770.043373150.031732620.075367340.016598390.05821397
22. 0.161156750.002636980.50100860.004224150.030147020.17149838
23. 0.017918740.035264370.082306410.017776270.088855380.05613144
24. 0.074784890.02873450.024234820.047815830.080665580.24980657
25. 0.016834870.127688470.125125260.032443590.125556230.05167776
26. 0.055691650.024578280.102738310.062891360.025742610.23747873
27. 0.050321580.04884150.180323960.091808520.006061930.00378815
28. 0.151545950.064324170.461332260.222463780.029000070.03095598
29. 0.247842440.179159350.023313780.018997010.203750490.08266777
30. 0.023091420.083259980.119411450.053434460.13488790.14747583
31. 0.117551080.141027070.070442890.003816290.057899830.0654181
32. 0.100489660.006342720.46237450.004164650.100583170.00988711
33. 0.274182160.065760760.033061310.078905460.084925330.01243208
34. 0.056024980.114475260.049329550.018098950.129734140.23358211
35. 0.295033660.112927210.012101160.02871565]