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Add linear Ordinary Least Squares (OLS) regression trendlines or non-linear Locally Weighted Scatterplot Smoothing (LOEWSS) trendlines to scatterplots in Python.
statistical
python
base
Linear and Non-Linear Trendlines
13
u-guide
python/linear-fits/
thumbnail/linear_fit.jpg

Linear fit trendlines with Plotly Express

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on "tidy" data.

Plotly Express allows you to add Ordinary Least Squares regression trendline to scatterplots with the trendline argument. In order to do so, you will need to install statsmodels and its dependencies. Hovering over the trendline will show the equation of the line and its R-squared value.

import plotly.express as px

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

Fitting multiple lines and retrieving the model parameters

Plotly Express will fit a trendline per trace, and allows you to access the underlying model parameters for all the models.

import plotly.express as px

tips = px.data.tips()
fig = px.scatter(tips, x="total_bill", y="tip", facet_col="smoker", color="sex", trendline="ols")
fig.show()

results = px.get_trendline_results(fig)
print(results)

results.query("sex == 'Male' and smoker == 'Yes'").px_fit_results.iloc[0].summary()

Non-Linear Trendlines

Plotly Express also supports non-linear LOWESS trendlines.

import plotly.express as px

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