对于回归分析，我喜欢使用statsmodels.api或sklearn.linear_model。我还喜欢在熊猫数据框中组织数据和回归结果。这是一种以一种干净有序的方式来做您想要的事情的方法：

使用sklearn或statsmdodels进行绘图：

使用sklearn的代码：

from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import datetime

# data
np.random.seed(123)
numdays=20

X = (np.random.randint(low=-20,high=20,size=numdays).cumsum()+100).tolist()
Y = (np.random.randint(low=-20,size=numdays).cumsum()+100).tolist()
df = pd.DataFrame({'X': X,'Y':Y})

# regression
reg = LinearRegression().fit(np.vstack(df['X']),Y)
df['bestfit'] = reg.predict(np.vstack(df['X']))

# plotly figure setup
fig=go.Figure()
fig.add_trace(go.Scatter(name='X vs Y',x=df['X'],y=df['Y'].values,mode='markers'))
fig.add_trace(go.Scatter(name='line of best fit',x=X,y=df['bestfit'],mode='lines'))

# plotly figure layout
fig.update_layout(xaxis_title = 'X',yaxis_title = 'Y')

fig.show()

使用统计模型的代码：

import plotly.graph_objects as go
import statsmodels.api as sm
import pandas as pd
import numpy as np
import datetime

# data
np.random.seed(123)
numdays=20

X = (np.random.randint(low=-20,size=numdays).cumsum()+100).tolist()

df = pd.DataFrame({'X': X,'Y':Y})

# regression
df['bestfit'] = sm.OLS(df['Y'],sm.add_constant(df['X'])).fit().fittedvalues

# plotly figure setup
fig=go.Figure()
fig.add_trace(go.Scatter(name='X vs Y',mode='lines'))


# plotly figure layout
fig.update_layout(xaxis_title = 'X',yaxis_title = 'Y')

fig.show()

Plotly还带有用于统计模型的本机包装，用于绘制（非）线性线：

引用其文档，网址为：https://plotly.com/python/linear-fits/

import plotly.express as px

df = px.data.tips()
fig = px.scatter(df,x="total_bill",y="tip",trendline="ols")
fig.show()