plt.plot 绘制多条曲线而不是单条曲线

Question

这是我使用的数据集的链接：数据集

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

#Lets begin with polynomial regression

df = pd.read_excel('enes.xlsx', index='hacim')

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)

plt.scatter(X, Y, color = 'red')

plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')

plt.title('X Vs Y')

plt.xlabel('hacim')

plt.ylabel('delay')

plt.show()

最后 plt.show 显示了一个图表，其中有很多行，而不是我想要的单行多项式回归。出了什么问题，我该如何解决这个问题？

数据

,hacim,delay

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

Answer 1

让我们使用 pandas 绘图，它更容易：X=pd.DataFrame(df['hacim'])Y=pd.DataFrame(df['delay'])from sklearn.linear_model import LinearRegressionfrom sklearn.preprocessing import PolynomialFeaturespoly_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：绘制折线图时，散点线的根是未排序的数据。你可以这样做：plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue', marker='o', linestyle='none')输出：