tipi di operandi non supportati per -: 'str' e 'str' in matplotlib

Question

ecco il link al dataset che ho usato: dataset

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


df = pd.read_excel('enes.xlsx', index='hacim')
X=pd.DataFrame(df['hacim'])
Y=pd.DataFrame(df['delay'])
X

from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, Y)

#first print our linear regression for comparison
plt.scatter(X, Y, color = 'red')
plt.plot(X, lin_reg.predict(X), color = 'blue')
plt.title('X Vs Y')
plt.xlabel('hacim')
plt.ylabel('delay')
plt.show()

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)

X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, Y, color = 'red')
plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
plt.title('hacim vs delay)')
plt.xlabel('hacim')
plt.ylabel('delay')
plt.show()

Quando eseguo l'ultimo blocco del codice in jupyter notebook ottengo il seguente errore f: tipo / i di operandi non supportati per -: 'str' e 'str' in matplotlib cosa c'è che non va e come posso risolvere questo problema?

Errore

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-132-59d77fa4cf3d> in <module>
     13 lin_reg_2.fit(X_poly, Y)
     14 
---> 15 X_grid = np.arange(min(X), max(X), 0.1)
     16 X_grid = X_grid.reshape((len(X_grid), 1))
     17 plt.scatter(X, Y, color = 'red')

TypeError: unsupported operand type(s) for -: 'str' and 'str'

Dati

,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

Provare:

X_grid = np.arange(X.min()[0], X.max()[0], 0.1)

Produzione: