--- python3-module-seaborn/seaborn/regression.py.orig	2023-09-11 10:57:15.725198223 +0000

+++ python3-module-seaborn/seaborn/regression.py	2023-09-11 15:05:43.455450574 +0000

@@ -264,14 +264,20 @@ class _RegressionPlotter(_LinearPlotter)

     def fit_statsmodels(self, grid, model, **kwargs):

         """More general regression function using statsmodels objects."""

-        import statsmodels.genmod.generalized_linear_model as glm

+        import statsmodels.tools.sm_exceptions as sme

         X, y = np.c_[np.ones(len(self.x)), self.x], self.y

         grid = np.c_[np.ones(len(grid)), grid]

         def reg_func(_x, _y):

+            err_classes = (sme.PerfectSeparationError,)

             try:

-                yhat = model(_y, _x, **kwargs).fit().predict(grid)

-            except glm.PerfectSeparationError:

+                with warnings.catch_warnings():

+                    if hasattr(sme, "PerfectSeparationWarning"):

+                        # statsmodels>=0.14.0

+                        warnings.simplefilter("error", sme.PerfectSeparationWarning)

+                        err_classes = (*err_classes, sme.PerfectSeparationWarning)

+                    yhat = model(_y, _x, **kwargs).fit().predict(grid)

+            except err_classes:

                 yhat = np.empty(len(grid))

                 yhat.fill(np.nan)

             return yhat