How to plot statsmodels linear regression (OLS) cleanly

Question

Problem Statement:

I have some nice data in a pandas dataframe. I\'d like to run simple linear regression on it:

Using statsmodels, I perform my r

Answer 1

As I mentioned in the comments, seaborn is a great choice for statistical data visualization.

import seaborn as sns

sns.regplot(x='motifScore', y='expression', data=motif)

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Alternatively, you can use statsmodels.regression.linear_model.OLS and manually plot a regression line.

import statsmodels.api as sm

# regress "expression" onto "motifScore" (plus an intercept)
model = sm.OLS(motif.expression, sm.add_constant(motif.motifScore))
p = model.fit().params

# generate x-values for your regression line (two is sufficient)
x = np.arange(1, 3)

# scatter-plot data
ax = motif.plot(x='motifScore', y='expression', kind='scatter')

# plot regression line on the same axes, set x-axis limits
ax.plot(x, p.const + p.motifScore * x)
ax.set_xlim([1, 2])

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Yet another solution is statsmodels.graphics.regressionplots.abline_plot which takes away some of the boilerplate from the above approach.

import statsmodels.api as sm
from statsmodels.graphics.regressionplots import abline_plot

# regress "expression" onto "motifScore" (plus an intercept)
model = sm.OLS(motif.expression, sm.add_constant(motif.motifScore))

# scatter-plot data
ax = motif.plot(x='motifScore', y='expression', kind='scatter')

# plot regression line
abline_plot(model_results=model.fit(), ax=ax)