情节:如何使用情节和情节表达来绘制回归线? [英] Plotly: How to plot a regression line using plotly and plotly express?

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:

 分母= df.pm1 ** 2-df.pm1.mean()* df.pm1.sum()打印('分母'，分母)m =(df.pm1.dot(df.pm25)-df.pm25.mean()* df.pm1.sum())/分母b =(df.pm25.mean()* df.pm1.dot(df.pm1)-df.pm1.mean()* df.pm1.dot(df.pm25))/分母y_pred = m * df.pm1 + blineOfBestFit = go.Scattergl(x = df.pm1，y = y_pred，name =最合适的线"，线=字典(颜色=红色"，))数据= [dataPoints，lineOfBestFit]Figure = go.Figure(data = data)Figure.show() 

情节:

如何正确绘制lineOfBestFit?

解决方案

更新1:

现在，以情节表示的方式可以处理

如果您希望回归线脱颖而出，则可以通过以下方式直接编辑线的颜色:

  fig.data [1] .line.color ='红色' 

您可以通过访问诸如 alpha 和beta 的回归参数:

  model = px.get_trendline_results(fig)alpha = model.iloc [0] ["px_fit_results"].params [0]beta = model.iloc [0] ["px_fit_results"].params [1] 

您甚至可以通过以下方式请求非线性拟合:

  fig = px.scatter(df，x ='X'，y ='Y'，trendline ="lowess") 

那那些长格式呢?那就是密谋表达揭示其某些真正力量的地方.如果以内置数据集 px.data.gapminder 为例，则可以通过指定 color ="continent" 来触发一组国家/地区的各行:

完整的长格式代码段

  import plotly.express as pxdf = px.data.gapminder().query(年份== 2007")图= px.scatter(df，x ="gdpPercap"，y ="lifeExp"，color =大陆"，趋势线="lowess")图show() 

如果您希望在模型选择和输出方面具有更大的灵活性，可以随时使用我对下面这篇文章的原始回答.但是首先，这是我回答开始时这些示例的完整摘录:

完整的数据段摘录

 直接导入plotly.graph_objects导入plotly.express为px导入statsmodels.api作为sm将熊猫作为pd导入将numpy导入为np导入日期时间# 数据np.random.seed(123)numdays = 20X =(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})#回归图#fig = px.scatter(df，x ='X'，y ='Y'，trendline ="ols")图= px.scatter(df，x ='X'，y ='Y'，趋势线="lowess")#使回归线突出fig.data [1] .line.color ='红色'#绘图布局fig.update_layout(xaxis_title ='X'，yaxis_title ='Y')图show() 

---

原始答案:

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

使用sklearn或stats模型进行绘制:

使用sklearn的代码:

从sklearn.linear_model

 导入LinearRegression随地导入plotly.graph_objects将熊猫作为pd导入将numpy导入为np导入日期时间# 数据np.random.seed(123)numdays = 20X =(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})#回归reg = LinearRegression().fit(np.vstack(df ['X'])，Y)df ['bestfit'] = reg.predict(np.vstack(df ['X']))#绘图设置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 ='最合适的线'，x = X，y = df ['最合适的']，mode ='线'))#绘图布局fig.update_layout(xaxis_title ='X'，yaxis_title ='Y')图show() 

使用统计模型的代码:

 直接导入plotly.graph_objects导入statsmodels.api作为sm将熊猫作为pd导入将numpy导入为np导入日期时间# 数据np.random.seed(123)numdays = 20X =(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})#回归df ['bestfit'] = sm.OLS(df ['Y']，sm.add_constant(df ['X'])).fit().fittedvalues#绘图设置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 ='最合适的线'，x = X，y = df ['最合适的']，mode ='线'))#绘图布局fig.update_layout(xaxis_title ='X'，yaxis_title ='Y')图show() 

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()

Plot:

How can I make the lineOfBestFit to be drawn properly?

解决方案

Update 1:

Now that plotly express handles data of both long and wide format (the latter in your case) like a breeze, the only thing you need to plot a regression line is:

fig = px.scatter(df, x='X', y='Y', trendline="ols")

Complete code snippet for wide data at the end of the question

If you'd like the regression line to stand out, you can edit the line color directly through:

fig.data[1].line.color = 'red'

You can access regression parameters like alpha and beta through:

model = px.get_trendline_results(fig)
alpha = model.iloc[0]["px_fit_results"].params[0]
beta = model.iloc[0]["px_fit_results"].params[1]

And you can even request non-linear fit through:

fig = px.scatter(df, x='X', y='Y', trendline="lowess")

And what about those long formats? That's where plotly express reveals some of its real powers. If you take the built-in dataset px.data.gapminder as an example, you can trigger individual lines for an array of countries by specifying color="continent":

Complete snippet for long format

import plotly.express as px

df = px.data.gapminder().query("year == 2007")
fig = px.scatter(df, x="gdpPercap", y="lifeExp", color="continent", trendline="lowess")
fig.show()

And if you'd like even more flexibility with regards to model choice and output, you can always resort to my original answer to this post below. But first, here's a complete snippet for those examples at the start of my answer:

Complete snippet for wide data

import plotly.graph_objects as go
import plotly.express as px
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})

# figure with regression
# fig = px.scatter(df, x='X', y='Y', trendline="ols")
fig = px.scatter(df, x='X', y='Y', trendline="lowess")

# make the regression line stand out
fig.data[1].line.color = 'red'

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

fig.show()

---

Original answer:

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:

Plot using sklearn or statsmodels:

Code using sklearn:

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()

Code using statsmodels:

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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