数组创建函数

ndarray的数据类型

二维数组的索引方式

二维数组切片

通用函数



基本数组的统计方法

数组的集合运算

常用的线性代数函数

随机数

范例:随机漫步
import random
position = 0
walk = [position]
steps = 1000
for i in range(steps):
step = 1 if random.randint(0,1) else -1
position += step
walk.append(position)
结果如下图所示:

nsteps = 1000
draws = np.random.randint(0,2,size=nsteps)
steps = np.where(draws>0, 1, -1)
walk = steps.cumsum()
array([ 1, 2, 3, 2, 3, 4, 5, 4, 3, 4, 3, 2, 1,
0, -1, -2, -3, -2, -3, -2, -1, -2, -1, 0, 1, 0,
-1, 0, -1, 0, -1, 0, 1, 2, 3, 4, 3, 2, 1,
0, 1, 0, 1, 2, 3, 4, 5, 6, 7, 6, 5, 6,
7, 6, 5, 4, 5, 4, 3, 2, 1, 2, 1, 0, -1,
-2, -1, 0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 4,
5, 6, 7, 6, 5, 6, 7, 6, 7, 8, 9, 8, 7,
8, 7, 8, 9, 8, 9, 8, 9, 8, 7, 8, 9, 10,
11, 10, 11, 10, 9, 8, 9, 8, 9, 10, 11, 12, 13,
14, 13, 14, 13, 12, 11, 12, 13, 12, 11, 12, 13, 14,
13, 14, 13, 12, 13, 14, 13, 12, 11, 10, 11, 10, 9,
8, 9, 8, 7, 6, 7, 6, 5, 6, 5, 6, 5, 4,
5, 6, 7, 6, 5, 4, 5, 4, 3, 2, 3, 2, 1,
2, 1, 0, 1, 2, 3, 4, 5, 6, 5, 4, 3, 2,
1, 0, 1, 0, 1, 2, 3, 4, 3, 2, 1, 2, 1,
2, 3, 4, 3, 2, 1, 2, 1, 0, 1, 2, 1, 0,
1, 2, 3, 4, 5, 6, 7, 6, 7, 6, 5, 4, 3,
4, 3, 4, 3, 4, 5, 6, 7, 6, 5, 6, 5, 4,
3, 4, 3, 2, 3, 4, 5, 6, 5, 6, 5, 6, 7,
8, 7, 8, 7, 8, 7, 6, 5, 6, 7, 8, 7, 8,
7, 6, 7, 6, 7, 6, 5, 4, 3, 2, 1, 0, 1,
0, -1, 0, 1, 2, 1, 0, 1, 0, 1, 0, -1, -2,
-1, -2, -1, -2, -3, -2, -3, -4, -5, -6, -5, -4, -3,
-4, -3, -2, -1, -2, -3, -2, -1, -2, -3, -4, -3, -2,
-3, -4, -3, -2, -1, -2, -3, -4, -5, -6, -7, -6, -5,
-6, -7, -8, -7, -8, -9, -8, -9, -8, -9, -8, -9, -10,
-9, -10, -11, -10, -9, -8, -9, -8, -7, -8, -9, -10, -11,
-10, -11, -10, -9, -10, -9, -8, -9, -8, -9, -10, -9, -8,
-9, -8, -7, -8, -7, -8, -9, -10, -9, -8, -9, -8, -7,
-6, -5, -4, -3, -4, -3, -4, -5, -4, -5, -4, -3, -4,
-3, -2, -3, -2, -3, -4, -5, -4, -3, -4, -5, -4, -3,
-2, -3, -4, -3, -2, -3, -2, -3, -2, -3, -2, -1, 0,
-1, -2, -3, -2, -1, -2, -3, -4, -3, -2, -1, -2, -1,
0, 1, 2, 1, 0, 1, 0, 1, 2, 3, 4, 5, 6,
5, 6, 7, 8, 9, 10, 9, 8, 7, 8, 7, 6, 5,
6, 7, 6, 5, 6, 5, 6, 7, 6, 5, 6, 5, 4,
5, 4, 3, 2, 1, 2, 1, 2, 3, 2, 3, 2, 3,
4, 5, 6, 7, 6, 7, 8, 9, 10, 11, 10, 9, 10,
9, 10, 11, 12, 13, 14, 13, 14, 15, 14, 13, 14, 13,
12, 13, 14, 15, 16, 17, 18, 17, 16, 15, 16, 15, 16,
15, 14, 13, 14, 15, 16, 15, 14, 13, 14, 13, 14, 15,
16, 17, 18, 19, 18, 17, 16, 17, 16, 15, 14, 15, 14,
13, 14, 15, 14, 13, 12, 13, 14, 13, 12, 13, 12, 13,
14, 15, 16, 17, 18, 19, 20, 19, 20, 19, 20, 19, 20,
19, 20, 19, 20, 21, 20, 21, 22, 23, 24, 25, 26, 25,
26, 25, 26, 27, 26, 27, 28, 29, 30, 31, 30, 31, 32,
31, 30, 29, 30, 31, 32, 31, 32, 33, 32, 31, 32, 31,
32, 33, 32, 33, 34, 33, 34, 35, 34, 33, 34, 35, 34,
33, 32, 31, 32, 33, 34, 35, 34, 33, 34, 35, 34, 35,
34, 35, 34, 35, 36, 35, 34, 33, 34, 35, 34, 33, 32,
33, 32, 31, 32, 33, 34, 33, 34, 33, 32, 31, 30, 31,
30, 29, 30, 31, 30, 29, 28, 27, 28, 27, 26, 25, 24,
25, 24, 25, 26, 25, 26, 25, 26, 27, 28, 27, 26, 27,
26, 25, 26, 25, 26, 25, 24, 25, 26, 25, 26, 27, 26,
27, 26, 25, 26, 25, 24, 25, 26, 25, 26, 27, 28, 27,
28, 27, 28, 29, 30, 29, 28, 29, 30, 29, 28, 29, 30,
31, 32, 31, 32, 31, 32, 31, 30, 29, 30, 29, 28, 29,
30, 31, 30, 29, 30, 29, 30, 29, 28, 29, 28, 29, 30,
31, 32, 33, 32, 31, 32, 33, 34, 33, 32, 31, 32, 33,
34, 35, 34, 33, 34, 35, 34, 33, 34, 35, 36, 35, 34,
35, 34, 33, 32, 33, 34, 33, 34, 35, 34, 33, 32, 33,
34, 35, 34, 35, 34, 35, 34, 35, 36, 35, 34, 33, 34,
35, 34, 35, 34, 33, 32, 33, 34, 33, 34, 33, 34, 33,
32, 33, 34, 33, 32, 31, 32, 31, 30, 29, 30, 29, 28,
29, 30, 29, 30, 29, 30, 31, 30, 29, 28, 29, 28, 29,
28, 27, 28, 27, 28, 29, 30, 29, 30, 31, 32, 33, 34,
35, 36, 37, 36, 37, 38, 39, 38, 39, 38, 37, 38, 39,
40, 41, 40, 39, 38, 39, 38, 39, 38, 37, 38, 37, 36,
35, 36, 35, 36, 37, 38, 37, 38, 37, 38, 39, 38, 39,
38, 39, 40, 39, 40, 41, 42, 43, 42, 41, 40, 39, 38,
39, 40, 39, 40, 41, 40, 39, 38, 39, 40, 41, 40, 41,
40, 39, 40, 39, 40, 41, 42, 41, 42, 41, 40, 39, 38,
39, 38, 37, 38, 39, 40, 39, 40, 39, 40, 39, 38, 37,
38, 37, 38, 39, 40, 39, 38, 39, 40, 41, 40, 39, 40,
39, 38, 37, 38, 39, 40, 41, 42, 41, 40, 39, 38, 37,
38, 39, 38, 37, 38, 37, 38, 39, 38, 37, 36, 37, 36,
35, 36, 35, 36, 35, 36, 37, 38, 39, 38, 37, 38],
dtype=int32)
结果如下图所示:

本文介绍使用Python和NumPy库实现随机漫步的过程,通过创建一个初始位置为0的漫步者,进行1000步随机移动,每一步随机选择前进或后退,并展示漫步路径的数组表示。
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