How to get the numerical fitting results when plotting a regression in seaborn?

Question

If I use the seaborn library in Python to plot the result of a linear regression, is there a way to find out the numerical results of the regression? For example, I might wa

Answer 1

Seaborn's creator has unfortunately stated that he won't add such a feature, so here's a workaround.

def regplot(*args, **kwargs):
    # this is the class that `sns.regplot` uses
    plotter = sns.regression._RegressionPlotter(*args, **kwargs)

    # this is essentially the code from `sns.regplot`
    ax = kwargs.get("ax", None)
    if ax is None:
        ax = plt.gca()

    scatter_kws = {} if scatter_kws is None else copy.copy(scatter_kws)
    scatter_kws["marker"] = marker
    line_kws = {} if line_kws is None else copy.copy(line_kws)

    plotter.plot(ax, scatter_kws, line_kws)

    # unfortunately the regression results aren't stored, so we rerun
    grid, yhat, err_bands = plotter.fit_regression(plt.gca())

    # also unfortunately, this doesn't return the parameters, so we infer them
    slope = (yhat[-1] - yhat[0]) / (grid[-1] - grid[0])
    intercept = yhat[0] - slope * grid[0]
    return slope, intercept

Note that this only works for linear regression because it simply infers the slope and intercept from the regression results. The nice thing is that it uses seaborn's own regression class and so the results are guaranteed to be consistent with what's shown. The downside is of course that we're using a private implementation detail in seaborn that can break at any point.