This cannot be done automatically with lmplot , because it is undefined that should match this value in the presence of multiple regression approaches (i.e. using the hue , row or col variable).
But this is part of a similar jointplot function. By default, it shows the correlation coefficient and p value:
import seaborn as sns import numpy as np x, y = np.random.randn(2, 40) sns.jointplot(x, y, kind="reg")
But you can pass any function. If you want R ^ 2, you can do:
from scipy import stats def r2(x, y): return stats.pearsonr(x, y)[0] ** 2 sns.jointplot(x, y, kind="reg", stat_func=r2)

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