将分布拟合到直方图
Fitting a distribution to a histogram
我有一些使用 sns.distplot 绘制的数据
x轴是某个球员受伤的天数：
我想获取此发行版的 cdf。它不必非常准确，但我想 运行 一些模拟并且有效地我想要这个数据的分布，称之为 X。然后我想 运行
X.rvs(1000)
这会给我一个包含 1000 个随机数的数组，代表球员受伤的天数。例如如果它 returns
array(2,35,140,3,4,6,7,23,55,63,...,87)
那些应该代表球员受伤的天数。
真的不知道该怎么做，因为我要继续的是这个直方图背后的数据，它只是用 sns.distplot(data,kde=True)
绘制的
希望有人能帮忙
数据：
data = pd.DataFrame([78,
58,
124,
62,
30,
46,
31,
34,
94,
15,
41,
18,
63,
15,
63,
31,
35,
23,
19,
19,
47,
154,
113,
29,
35,
58,
62,
93,
93,
93,
37,
31,
16,
17,
16,
17,
62,
31,
145,
116,
183,
183,
183,
93,
148,
183,
13,
160,
183,
183,
68,
15,
183,
57,
91,
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86,
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183,
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161,
67,
183,
40,
121,
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9,
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9,
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52,
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183,
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65,
183,
30,
183,
19,
14,
183,
54,
37,
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152,
33,
22,
67,
183,
40,
17,
183,
50,
7,
183,
106,
72,
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183,
58,
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183,
183,
183,
183,
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29,
52,
15,
45,
22,
182,
24,
15,
15,
37,
33,
27,
60,
15,
27,
24,
8,
12,
15,
85,
21,
182,
37,
27,
49,
26,
7,
16,
14,
19,
33,
30,
4,
28,
60,
15,
162,
40,
39,
14,
22,
7,
47,
121,
40,
39,
15,
8,
30,
13,
150,
16,
7,
78,
30,
41,
26,
21,
65,
155,
54,
44,
85,
54,
72,
19,
21,
26,
127,
17,
4,
75,
23,
69,
44,
71,
102,
23,
21,
17,
61,
155,
27,
38,
27,
30,
107,
33,
24,
103,
61,
98,
6,
60,
22,
51,
]
鉴于原始数据，很容易提供合理的近似值。
from scipy import stats
p_data = [1, 1, 2, 4, 3, 8, 19, 11, 12, 7, 11, 12, 16, 7, 176, 62, 55, 44, 38, 48, 29, 28, 29, 35, 22, 32, 30, 36, 22, 33, 30, 30, 35, 20, 24, 33, 22, 28, 27, 32, 28, 13, 20, 9, 11, 12, 21, 13, 12, 18, 18, 16, 14, 12, 10, 15, 8, 14, 11, 19, 14, 11, 14, 12, 10, 12, 8, 7, 7, 7, 8, 5, 3, 10, 8, 10, 10, 8, 4, 6, 5, 3, 2, 8, 8, 8, 3, 8, 5, 7, 9, 2, 12, 5, 4, 3, 1, 9, 5, 3, 4, 4, 2, 5, 5, 5, 2, 1, 5, 6, 4, 2, 2, 1, 6, 3, 2, 6, 4, 5, 3, 2, 1, 2, 5, 3, 6, 3, 2, 1, 4, 1, 5, 4, 1, 2, 1, 2, 2, 2, 8, 2, 2, 4, 6, 1, 4, 3, 3, 6, 2, 4, 1, 4, 3, 3, 5, 3, 3, 4, 4, 5, 3, 2, 1, 2, 3, 3, 1, 1, 1, 4, 3, 3, 5, 4, 4, 4, 1, 148]
thesum=sum(p_data)
p_data = [_/thesum for _ in p_data]
x_k = range(1, 1+len(p_data))
custom = stats.rv_discrete(name='custm', values=(x_k, p_data))
R = custom.rvs(size=1000)
print (R[:20])
首先，我使用 itertools 中的 Counter 来计算每个持续时间发生的次数。正如所料，一些持续时间不会出现在数据中，并表示为零计数。牢记操作词 'approximate' 我觉得用接近相邻值的数字替换少数零值是合理的。结果就是您在 p_data 中看到的结果。然后我归一化（使值总和为 1 作为概率）。
从大约 180 天开始，右端出现了一些值。我把所有这些质量放在 180 天。零日时没有质量。这解释了为什么 x_k 从 1 到 180（含）。
最后我使用了 rv_discrete 对象，其 rvs 方法被证明相当快。
我有一些使用 sns.distplot 绘制的数据 x轴是某个球员受伤的天数：
我想获取此发行版的 cdf。它不必非常准确，但我想 运行 一些模拟并且有效地我想要这个数据的分布，称之为 X。然后我想 运行
X.rvs(1000)
这会给我一个包含 1000 个随机数的数组，代表球员受伤的天数。例如如果它 returns
array(2,35,140,3,4,6,7,23,55,63,...,87)
那些应该代表球员受伤的天数。
真的不知道该怎么做，因为我要继续的是这个直方图背后的数据，它只是用 sns.distplot(data,kde=True)
绘制的希望有人能帮忙
数据：
data = pd.DataFrame([78,
58,
124,
62,
30,
46,
31,
34,
94,
15,
41,
18,
63,
15,
63,
31,
35,
23,
19,
19,
47,
154,
113,
29,
35,
58,
62,
93,
93,
93,
37,
31,
16,
17,
16,
17,
62,
31,
145,
116,
183,
183,
183,
93,
148,
183,
13,
160,
183,
183,
68,
15,
183,
57,
91,
183,
86,
133,
20,
183,
89,
183,
43,
30,
183,
183,
136,
183,
183,
12,
183,
60,
161,
67,
183,
40,
121,
52,
58,
183,
183,
9,
151,
183,
183,
183,
116,
9,
95,
183,
27,
16,
183,
52,
167,
12,
183,
183,
94,
65,
183,
30,
183,
19,
14,
183,
54,
37,
183,
152,
33,
22,
67,
183,
40,
17,
183,
50,
7,
183,
106,
72,
183,
183,
22,
80,
183,
183,
58,
183,
183,
183,
183,
183,
15,
183,
183,
183,
183,
127,
156,
183,
26,
183,
183,
59,
9,
183,
183,
55,
183,
183,
183,
28,
18,
51,
18,
11,
18,
26,
77,
65,
61,
19,
61,
61,
61,
30,
30,
30,
182,
54,
182,
22,
121,
26,
64,
91,
91,
15,
18,
60,
17,
16,
60,
29,
15,
31,
181,
15,
16,
120,
24,
26,
30,
28,
90,
28,
90,
27,
25,
27,
32,
28,
28,
28,
28,
180,
150,
28,
47,
51,
60,
25,
43,
9,
16,
24,
89,
15,
13,
58,
106,
16,
59,
29,
19,
22,
16,
16,
51,
52,
33,
26,
178,
148,
148,
42,
72,
28,
86,
17,
17,
56,
18,
25,
17,
28,
41,
37,
15,
81,
25,
147,
147,
8,
36,
32,
18,
37,
42,
23,
86,
38,
36,
55,
24,
15,
60,
54,
41,
18,
15,
17,
31,
146,
115,
64,
115,
25,
32,
85,
85,
38,
15,
23,
175,
175,
175,
84,
145,
18,
22,
38,
35,
21,
22,
53,
43,
22,
21,
37,
15,
19,
49,
25,
52,
28,
21,
21,
15,
172,
11,
109,
20,
22,
32,
29,
168,
168,
15,
28,
166,
16,
31,
86,
165,
15,
25,
48,
163,
56,
15,
162,
38,
19,
17,
40,
95,
160,
56,
56,
27,
48,
158,
32,
157,
157,
157,
15,
27,
37,
16,
46,
141,
141,
15,
31,
19,
52,
43,
15,
51,
39,
74,
119,
23,
15,
134,
43,
17,
15,
54,
33,
79,
133,
133,
21,
72,
17,
118,
26,
24,
15,
106,
30,
15,
53,
16,
21,
127,
127,
21,
126,
126,
33,
112,
17,
93,
33,
21,
17,
43,
36,
15,
44,
53,
110,
17,
17,
109,
17,
47,
38,
12,
16,
33,
62,
16,
77,
78,
88,
52,
69,
24,
63,
104,
28,
104,
15,
101,
59,
42,
99,
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74,
41,
33,
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96,
20,
29,
30,
58,
41,
15,
95,
15,
33,
34,
25,
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51,
77,
15,
74,
76,
27,
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31,
20,
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35,
15,
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60,
73,
72,
39,
45,
35,
39,
70,
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51,
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17,
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68,
37,
18,
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15,
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49,
20,
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24,
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42,
23,
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7,
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鉴于原始数据，很容易提供合理的近似值。
from scipy import stats
p_data = [1, 1, 2, 4, 3, 8, 19, 11, 12, 7, 11, 12, 16, 7, 176, 62, 55, 44, 38, 48, 29, 28, 29, 35, 22, 32, 30, 36, 22, 33, 30, 30, 35, 20, 24, 33, 22, 28, 27, 32, 28, 13, 20, 9, 11, 12, 21, 13, 12, 18, 18, 16, 14, 12, 10, 15, 8, 14, 11, 19, 14, 11, 14, 12, 10, 12, 8, 7, 7, 7, 8, 5, 3, 10, 8, 10, 10, 8, 4, 6, 5, 3, 2, 8, 8, 8, 3, 8, 5, 7, 9, 2, 12, 5, 4, 3, 1, 9, 5, 3, 4, 4, 2, 5, 5, 5, 2, 1, 5, 6, 4, 2, 2, 1, 6, 3, 2, 6, 4, 5, 3, 2, 1, 2, 5, 3, 6, 3, 2, 1, 4, 1, 5, 4, 1, 2, 1, 2, 2, 2, 8, 2, 2, 4, 6, 1, 4, 3, 3, 6, 2, 4, 1, 4, 3, 3, 5, 3, 3, 4, 4, 5, 3, 2, 1, 2, 3, 3, 1, 1, 1, 4, 3, 3, 5, 4, 4, 4, 1, 148]
thesum=sum(p_data)
p_data = [_/thesum for _ in p_data]
x_k = range(1, 1+len(p_data))
custom = stats.rv_discrete(name='custm', values=(x_k, p_data))
R = custom.rvs(size=1000)
print (R[:20])
首先，我使用 itertools 中的 Counter 来计算每个持续时间发生的次数。正如所料，一些持续时间不会出现在数据中，并表示为零计数。牢记操作词 'approximate' 我觉得用接近相邻值的数字替换少数零值是合理的。结果就是您在 p_data 中看到的结果。然后我归一化（使值总和为 1 作为概率）。
从大约 180 天开始，右端出现了一些值。我把所有这些质量放在 180 天。零日时没有质量。这解释了为什么 x_k 从 1 到 180（含）。
最后我使用了 rv_discrete 对象，其 rvs 方法被证明相当快。