Question

I have some data which I've plotted using sns.distplot The x axis is the number of days a certain player is injured for:

I want to get the cdf for this distribution. It does not have to be extremely accurate but I want to run some simulations and effectively I want to have the distribution for this data, call it X. Then I want to run

X.rvs(1000)

which would give me an array of 1000 random numbers which represent the number of days players are injured. e.g. if it returns

array(2,35,140,3,4,6,7,23,55,63,...,87)

Those should represent the number of days players are injured.

Really not sure how to do this as all I have to go on is the data behind this histogram which is simply plotted with sns.distplot(data,kde=True)

Hope someone can help

data:

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,
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30,
182,
54,
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26,
64,
91,
91,
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120,
24,
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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,
]

Answer 1

鉴于原始数据，提供合理的近似值相当容易。

Counter

首先，我使用itertools中的p_data来计算每个持续时间发生的次数。正如所料，某些持续时间不会出现在数据中，并表示为零计数。考虑到操作词'近似'，我觉得用一些接近相邻值的数字替换几个零值是合理的。结果就是您在x_k中看到的结果。然后我规范化（使值总和为1）。

在大约180天之后，右端有一些价值运球。我把所有这些质量都放在了180天。零日没有质量。这解释了为什么rv_discrete从1到180（包括1和180）运行。

最后，我使用rvs方法证明速度相当快的{literal}<script language="javascript" type="text/javascript">  </script>{/literal}对象。

Fitting a distribution to a histogram

1 个答案: