Python plt.plot绘制单曲线的多条曲线

以下是我使用的数据集的链接：

最后一个plt.show显示了一个图，其中有许多行，而不是我想要的一行多项式回归。出了什么问题？如何解决

资料

，hacim，延迟
0,815,1.44
1,750,1.11
2,321,2.37
3,1021,1.44
4,255,1.09
5,564,1.61
6,1455,15.27
7,525,2.7
8,1118,106.98
9,1036,3.47
10,396,1.34
11,1485,21.49
12,1017,12.22
13,1345,2.72
14,312,1.71
15,742,33.79
16,1100,39.62
17,1445,4.88
18,847,1.55
19,991,1.82
20,1296,10.77
21,854,1.81
22,1198,61.9
23,1162,8.22
24,1463,42.25
25,1272,4.31
26,745,2.36
27,521,2.14
28,1247,94.33
29,732,12.55
30,489,1.05
31,1494,12.78
32,591,3.18
33,257,1.18
34,602,4.24
35,335,2.06
36,523,3.63
37,752,7.61
38,349,1.76
39,771,0.79
40,855,39.08
41,948,3.95
42,1378,97.28
43,598,2.69
44,558,1.67
45,634,34.69
46,1146,12.22
47,1087,1.74
48,628,1.03
49,711,3.34
50,1116,7.27
51,748,1.09
52,1212,14.16
53,434,1.42
54,1046,8.25
55,568,1.33
56,894,2.61
57,1041,4.79
58,801,1.84
59,1387,11.5
60,1171,161.21
61,734,2.43
62,1471,17.42
63,461,1.42
64,751,2.36
65,898,2.4
66,593,1.74
67,942,3.39
68,825,1.09
69,715,20.23
70,725,5.43
71,1128,7.57
72,1348,4.49
73,1393,9.77
74,1379,97.76
75,859,2.59
76,612,15.98
77,1495,8.22
78,887,1.85
79,867,38.65
80,1353,1.6
81,851,60.25
82,1079,24.05
83,1100,25.58
84,638,1.23
85,1115,1.94
86,1443,4.79
87,1421,10.33
88,1279,7.29
89,1176,173.44
90,315,1.53
91,1019,34.03
92,1337,48.67
93,576,28.83
94,919,2.88
95,361,1.5
96,989,1.47
97,1286,32.11

让我们使用熊猫图，它更容易：

X=pd.DataFrame(df['hacim'])
Y=pd.DataFrame(df['delay'])

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, Y)

df['y_pred'] = lin_reg_2.predict(poly_reg.fit_transform(X))
df = df.sort_values('hacim')
ax = df.plot.scatter('hacim','delay')
df.plot('hacim', 'y_pred', ax=ax, color='r')
plt.title('X Vs Y')
plt.xlabel('hacim')
plt.ylabel('delay')
plt.show()

输出：

绘制线图时，散点线的根是未排序的数据

您可以这样做：

plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue', marker='o', linestyle='none')

输出：

plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue', marker='o', linestyle='none')