J5_2

1
from matplotlib import pyplot as plt
2

3
data = [
4
    35.1572,
5
    35.127,
6
    35.2324,
7
    35.2354,
8
    35.0273,
9
    35.1826,
10
    34.9893,
11
    38.5166,
12
    26.0977,
13
    19.8721,
14
    16.7422,
15
    15.1475,
16
    15.3086,
17
    15.2256,
18
    15.3184,
19
    15.3652,
20
    17.7168,
21
    21.9326,
22
    31.0332,
23
    33.0059,
24
    32.7275,
25
    28.2588,
26
    28.2734,
27
    40.2373,
28
    40.2959,
29
    40.2861,
30
    40.2666,
31
    40.3613,
32
    40.3311,
33
    40.4531,
34
    40.3613,
35
    35.7764,
36
    27.9834,
37
    27.8662,
38
    27.9258,
39
    28.0811,
40
    28.1807,
41
    27.9951,
42
    37.1006,
43
    23.1748,
44
    18.5166,
45
    16.0391,
46
    15.2021,
47
    15.1699,
48
    15.1309,
49
    15.1172,
50
    15.1289,
51
    15.7246,
52
    18.8232,
53
    23.3682,
54
    37.2588,
55
    30.6934,
56
    28.5869,
57
    29.7734,
58
    39.3359,
59
]
60

61
data_mean = [
62
    35.1572,
63
    35.1526,
64
    35.1649,
65
    35.1757,
66
    35.1529,
67
    35.1575,
68
    35.1316,
69
    35.6524,
70
    34.1824,
71
    31.9808,
72
    29.6364,
73
    27.4073,
74
    25.546,
75
    23.9582,
76
    22.629,
77
    21.5115,
78
    20.9277,
79
    21.0823,
80
    22.6132,
81
    24.2121,
82
    25.5222,
83
    25.9432,
84
    26.3017,
85
    28.4456,
86
    30.2687,
87
    31.8099,
88
    33.1109,
89
    34.2264,
90
    35.1655,
91
    35.979,
92
    36.6532,
93
    36.5183,
94
    35.2053,
95
    34.0762,
96
    33.13,
97
    32.3532,
98
    31.7113,
99
    31.1396,
100
    32.0566,
101
    30.6902,
102
    28.8173,
103
    26.8514,
104
    25.0592,
105
    23.5378,
106
    22.2444,
107
    21.1479,
108
    20.2219,
109
    19.53,
110
    19.4213,
111
    20.0285,
112
    22.6793,
113
    23.9123,
114
    24.6314,
115
    25.4225,
116
    27.563,
117
]
118

119
outliers = [
120
    7,
121
    8,
122
    23,
123
    31,
124
    32,
125
    38,
126
    50
127
]
128

129
plt.plot(data)
130
plt.plot(data_mean)
131
[plt.axvline(x = outlier, color = 'red') for outlier in outliers]
132
plt.show()