J5_2
1from matplotlib import pyplot as plt
2
3data = [
435.1572,
535.127,
635.2324,
735.2354,
835.0273,
935.1826,
1034.9893,
1138.5166,
1226.0977,
1319.8721,
1416.7422,
1515.1475,
1615.3086,
1715.2256,
1815.3184,
1915.3652,
2017.7168,
2121.9326,
2231.0332,
2333.0059,
2432.7275,
2528.2588,
2628.2734,
2740.2373,
2840.2959,
2940.2861,
3040.2666,
3140.3613,
3240.3311,
3340.4531,
3440.3613,
3535.7764,
3627.9834,
3727.8662,
3827.9258,
3928.0811,
4028.1807,
4127.9951,
4237.1006,
4323.1748,
4418.5166,
4516.0391,
4615.2021,
4715.1699,
4815.1309,
4915.1172,
5015.1289,
5115.7246,
5218.8232,
5323.3682,
5437.2588,
5530.6934,
5628.5869,
5729.7734,
5839.3359,
59]
60
61data_mean = [
6235.1572,
6335.1526,
6435.1649,
6535.1757,
6635.1529,
6735.1575,
6835.1316,
6935.6524,
7034.1824,
7131.9808,
7229.6364,
7327.4073,
7425.546,
7523.9582,
7622.629,
7721.5115,
7820.9277,
7921.0823,
8022.6132,
8124.2121,
8225.5222,
8325.9432,
8426.3017,
8528.4456,
8630.2687,
8731.8099,
8833.1109,
8934.2264,
9035.1655,
9135.979,
9236.6532,
9336.5183,
9435.2053,
9534.0762,
9633.13,
9732.3532,
9831.7113,
9931.1396,
10032.0566,
10130.6902,
10228.8173,
10326.8514,
10425.0592,
10523.5378,
10622.2444,
10721.1479,
10820.2219,
10919.53,
11019.4213,
11120.0285,
11222.6793,
11323.9123,
11424.6314,
11525.4225,
11627.563,
117]
118
119outliers = [
1207,
1218,
12223,
12331,
12432,
12538,
12650
127]
128
129plt.plot(data)
130plt.plot(data_mean)
131[plt.axvline(x = outlier, color = 'red') for outlier in outliers]
132plt.show()