绘图：如何使用绘图绘制回归线？ [英] Plotly: How to plot a regression line using plotly?

问题描述

我有一个数据帧df，其列为pm1和pm25。我想显示这两个信号之间的相关性的图表（带有Plotly）。到目前为止，我已经设法显示了散点图，但是我没有设法画出信号之间的相关性的拟合线。到目前为止，我已经尝试过：

I have a dataframe, df with the columns pm1 and pm25. I want to show a graph(with Plotly) of how correlated these 2 signals are. So far, I have managed to show the scatter plot, but I don't manage to draw the fit line of correlation between the signals. So far, I have tried this:

denominator=df.pm1**2-df.pm1.mean()*df.pm1.sum()
print('denominator',denominator)
m=(df.pm1.dot(df.pm25)-df.pm25.mean()*df.pm1.sum())/denominator
b=(df.pm25.mean()*df.pm1.dot(df.pm1)-df.pm1.mean()*df.pm1.dot(df.pm25))/denominator
y_pred=m*df.pm1+b


lineOfBestFit = go.Scattergl(
    x=df.pm1,
    y=y_pred,
    name='Line of best fit',
    line=dict(
        color='red',
    )
)

data = [dataPoints, lineOfBestFit]
figure = go.Figure(data=data)

figure.show()

图：

如何使lineOfBestFit正确绘制？

How can I make the lineOfBestFit to be drawn properly?

推荐答案

对于我喜欢的回归分析o使用 statsmodels.api 或 sklearn.linear_model 。我还喜欢在熊猫数据框中同时组织数据和回归结果。这是一种以干净有序的方式完成所需工作的方法：

For regression analysis I like to use statsmodels.api or sklearn.linear_model. I also like to organize both the data and regression results in a pandas dataframe. Here's one way to do what you're looking for in a clean and organized way:

使用sklearn或statsmodels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, high=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statsmodels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, high=20, size=numdays).cumsum()+100).tolist()
Y = (np.random.randint(low=-20, high=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', 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()

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