散點圖與直線拟合

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
           

df1=pd.read_excel('E:/CW/CS.xlsx',sheetname=)
df2=pd.read_excel('E:/CW/CS.xlsx',sheetname=)
df3=pd.read_excel('E:/CW/CS.xlsx',sheetname=)
           

df1_mat=df1[:].as_matrix()
df2_mat=df2[:].as_matrix()
df3_mat=df3[:].as_matrix()
           

a=[]
b=[]
c=[]
           

for i in range():
    for j in range():
        a.append(df1_mat[i][j])
        b.append(df2_mat[i][j])
        c.append(df3_mat[i][j])
           

df1_dr=pd.read_excel('E:/CW/DR.xlsx',sheetname=)
df2_dr=pd.read_excel('E:/CW/DR.xlsx',sheetname=)
df3_dr=pd.read_excel('E:/CW/DR.xlsx',sheetname=)
           

df1dr_mat=df1_dr[:].as_matrix()
df2dr_mat=df2_dr[:].as_matrix()
df3dr_mat=df3_dr[:].as_matrix()
           

x=[]
y=[]
z=[]
           

for i in range():
    for j in range():
        x.append(df1dr_mat[i][j])
        y.append(df2dr_mat[i][j])
        z.append(df3dr_mat[i][j])
           

from sklearn import linear_model
           

LR1=linear_model.LinearRegression()
LR2=linear_model.LinearRegression()
LR3=linear_model.LinearRegression()
           

x1_array=np.array(x).reshape(-, )#将資料轉化成n行1列，對于隻有一個特征時需要該操作
x2_array=np.array(y).reshape(-, )
x3_array=np.array(z).reshape(-, )
           

y1_array=np.array(a)
y2_array=np.array(b)
y3_array=np.array(c)
           

LR1.fit(x1_array,y1_array)
LR2.fit(x2_array,y2_array)
LR3.fit(x3_array,y3_array)
           

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
           

print(LR1.coef_,LR1.intercept_)
print(LR2.coef_,LR2.intercept_)
print(LR3.coef_,LR3.intercept_)
           

(array([ 1.75192196]), -275.48938146151511)
(array([ 1.69977594]), -264.25957272468219)
(array([ 1.64036973]), -251.8584170631708)
           

y1=1.7519x1-275.4893

y2=1.6997x2-264.2595

y3=1.6403x3-251.8584

真正的曲線為：y=1.6667x-258.3333

y1_predict=list(LR1.predict(x1_array))
y2_predict=list(LR2.predict(x2_array))
y3_predict=list(LR3.predict(x3_array))
           

x1_nihe=[x[],x[]]
y1_nihe=[y1_predict[],y1_predict[]]
x2_nihe=[y[],y[]]
y2_nihe=[y2_predict[],y2_predict[]]
x3_nihe=[z[],z[]]
y3_nihe=[y3_predict[],y3_predict[]]
plt.figure()  
plt.plot(x1_nihe,y1_nihe,'r')
plt.plot(x2_nihe,y2_nihe,'g')
plt.plot(x3_nihe,y3_nihe,'b')
plt.scatter(x=x,y=a,color='r',marker='^',label='t=20 Centigrade')
plt.scatter(x=y,y=b,color='g',marker='*',label='t=0 Centigrade')
plt.scatter(x=z,y=c,color='b',marker='.',label='t=-30 Centigrade')
plt.legend(loc=)
plt.title('Humidity curve fitting diagram')
plt.xlabel('Capacity values')
plt.ylabel('Humidity')
           

<matplotlib.text.Text at 0x12335438>
           

plt.show()