# 导入numpy 模块 1 import numpy as np 10 a = np.random.random((2,4))
11 a
12 Out[5]:
13 array([[0.20974732, 0.73822026, 0.82760722, 0.050551 ],
14 [0.77337155, 0.06521922, 0.55524187, 0.59209907]])
# 求矩阵所有数据的和，最小值，最大值
22 np.sum(a)
23 Out[7]: 3.812057513268513
24 np.min(a)
25 Out[8]: 0.05055099733013646
26 np.max(a)
27 Out[9]: 0.8276072194278252
28 print("a=",a)
29 a= [[0.20974732 0.73822026 0.82760722 0.050551 ]
30 [0.77337155 0.06521922 0.55524187 0.59209907]]
# axis=0 代表列， axis=1代表行
31 print("min",np.min(a))
32 min 0.05055099733013646#求每列当中的最小值
33 print("lmin:",np.min(a,axis=0))
34 lmin: [0.20974732 0.06521922 0.55524187 0.050551 ]
35 print("lmin:",np.min(a,axis=1))
36 lmin: [0.050551 0.06521922]
37 print("sum:",np.sum(a,axis=1))
38 sum: [1.8261258 1.98593171]
# reshape 数据， 3行4列
39 A = np.arange(2,14).reshape(3,4)
40 A
41 Out[16]:
42 array([[ 2, 3, 4, 5],
43 [ 6, 7, 8, 9],
44 [10, 11, 12, 13]])
# ndarray中最小值，最大值的序号
45 print(np.argmin(A))
46 0
47 print(np.argmax(A))
48 11
49 print(np.mean(A))
50 7.5
51 print(np.average(A))
52 7.5
53 print(A.mean())
54 7.5
# cumsum 迭代相加
69 A
70 Out[24]:
71 array([[ 2, 3, 4, 5],
72 [ 6, 7, 8, 9],
73 [10, 11, 12, 13]])
81 print(A.cumsum())
82 [ 2 5 9 14 20 27 35 44 54 65 77 90]
83 A
84 Out[27]:
85 array([[ 2, 3, 4, 5],
86 [ 6, 7, 8, 9],
87 [10, 11, 12, 13]])# clip(a, a_min, a_max) 将ndarray中的数据进行判断，小于a_min的值都赋值为a_min, 大于a_max的都赋值a_max，在这之间的值不变。
88 print(np.clip(A,5,8))
89 [[5 5 5 5]
90 [6 7 8 8]
91 [8 8 8 8]]
# 判断ndarray阶数，几维向量 99 A.ndim
100 Out[30]: 2
101 A
102 Out[31]:
103 array([[ 2, 3, 4, 5],
104 [ 6, 7, 8, 9],
105 [10, 11, 12, 13]])
106 A.ndim
107 Out[32]: 2
108 a
109 Out[33]:
110 array([[0.20974732, 0.73822026, 0.82760722, 0.050551 ],
111 [0.77337155, 0.06521922, 0.55524187, 0.59209907]])
112 a.ndim
113 Out[34]: 2
114 A
115 Out[35]:
116 array([[ 2, 3, 4, 5],
117 [ 6, 7, 8, 9],
118 [10, 11, 12, 13]])
原文地址：https://www.cnblogs.com/brownz/p/9520767.html
时间： 2024-10-10 01:55:01