numpy常用操作

一 代码

1. import numpy as np
2. print(np.arange(1,11))
3. print(np.arange(1,11).reshape([2,5]))
4. print(np.arange(1,11).reshape([2,-1]))
5. lst=np.arange(1,11).reshape([2,-1])
6. print("exp:")
7. print(np.exp(lst))
8. print("exp2:")
9. print(np.exp2(lst))
10. print("sqrt:")
11. print(np.sqrt(lst))
12. print("sin:")
13. print(np.sin(lst))
14. print("log:")
15. print(np.log(lst))
16. lst = np.array([[[1,2,3,4],
17. [4,5,6,7,]],
18. [[7,8,9,10],
19. [10,11,12,13]],
20. [[14,15,16,17],
21. [18,19,20,21]]
22. ])
23. print(lst.sum())
24. print(lst.sum(axis=0))
25. print(lst.sum(axis=1))
26. print(lst.sum(axis=2))
27. print(lst.max())
28. print("Max")
29. print(lst.max(axis=1))
30. print("Min")
31. print(lst.min(axis=0))
33. lst1 = np.array([10,20,30,40])
34. lst2 = np.array([4,3,2,1])
35. print(lst1+lst2)
36. print(lst1-lst2)
37. print(lst1*lst2)
38. print(lst1/lst2)
39. print(lst1**2)
40. print(np.dot(lst1.reshape([2,2]),lst2.reshape([2,2])))
41. print(np.concatenate((lst1,lst2),axis=0))
42. print(np.vstack((lst1,lst2)))
43. print(np.hstack((lst1,lst2)))
44. print(np.split(lst1,2))
45. print(np.split(lst1,4))
46. print(np.copy(lst1))

二 运行结果

[ 1  2  3  4  5  6  7  8  9 10]

[[ 1  2  3  4  5]

 [ 6  7  8  9 10]]

[[ 1  2  3  4  5]

 [ 6  7  8  9 10]]

exp:

[[  2.71828183e+00   7.38905610e+00   2.00855369e+01   5.45981500e+01

    1.48413159e+02]

 [  4.03428793e+02   1.09663316e+03   2.98095799e+03   8.10308393e+03

    2.20264658e+04]]

exp2:

[[    2.     4.     8.    16.    32.]

 [   64.   128.   256.   512.  1024.]]

sqrt:

[[ 1.          1.41421356  1.73205081  2.          2.23606798]

 [ 2.44948974  2.64575131  2.82842712  3.          3.16227766]]

sin:

[[ 0.84147098  0.90929743  0.14112001 -0.7568025  -0.95892427]

 [-0.2794155   0.6569866   0.98935825  0.41211849 -0.54402111]]

log:

[[ 0.          0.69314718  1.09861229  1.38629436  1.60943791]

 [ 1.79175947  1.94591015  2.07944154  2.19722458  2.30258509]]

252

[[22 25 28 31]

 [32 35 38 41]]

[[ 5  7  9 11]

 [17 19 21 23]

 [32 34 36 38]]

[[10 22]

 [34 46]

 [62 78]]

21

Max

[[ 4  5  6  7]

 [10 11 12 13]

 [18 19 20 21]]

Min

[[1 2 3 4]

 [4 5 6 7]]

[14 23 32 41]

[ 6 17 28 39]

[40 60 60 40]

[  2.5          6.66666667  15.          40.        ]

[ 100  400  900 1600]

[[ 80  50]

 [200 130]]

[10 20 30 40  4  3  2  1]

[[10 20 30 40]

 [ 4  3  2  1]]

[10 20 30 40  4  3  2  1]

[array([10, 20]), array([30, 40])]

[array([10]), array([20]), array([30]), array([40])]

[10 20 30 40]