Don't fail in regplot on singletons, don't fit regression either (#1969)

* FIX: don't squeeze singletons

* Disable regression fit in case of singleton inputs

* Update release notes

[ci skip]

(cherry picked from commit d59ab10)

doc/releases/v0.10.1.txt

@@ -5,3 +5,5 @@ v0.10.1 (Unreleased)
 This is minor release with bug fixes issues in 0.10.0.
 
 - Fixed a bug that appeared within the bootstrapping algorithm on 32-bit systems.
+
+- Fixed a bug where :func:`regplot` would crash on singleton inputs. Now a crash is avoided and regression estimation/plotting is skipped.

seaborn/regression.py

@@ -48,7 +48,7 @@ def establish_variables(self, data, **kws):
                 vector = np.asarray(val)
             else:
                 vector = val
-            if vector is not None:
+            if vector is not None and vector.shape != (1,):
                 vector = np.squeeze(vector)
             if np.ndim(vector) > 1:
                 err = "regplot inputs must be 1d"
@@ -127,8 +127,13 @@ def __init__(self, x, y, data=None, x_estimator=None, x_bins=None,
         else:
             self.x_discrete = self.x
 
+        # Disable regression in case of singleton inputs
+        if len(self.x) <= 1:
+            self.fit_reg = False
+
         # Save the range of the x variable for the grid later
-        self.x_range = self.x.min(), self.x.max()
+        if self.fit_reg:
+            self.x_range = self.x.min(), self.x.max()
 
     @property
     def scatter_data(self):

seaborn/tests/test_regression.py

@@ -165,6 +165,14 @@ def test_dropna(self):
         p = lm._RegressionPlotter("x", "y_na", data=self.df, dropna=False)
         nt.assert_equal(len(p.x), len(self.df.y_na))
 
+    @pytest.mark.parametrize("x,y",
+                             [([1.5], [2]),
+                              (np.array([1.5]), np.array([2])),
+                              (pd.Series(1.5), pd.Series(2))])
+    def test_singleton(self, x, y):
+        p = lm._RegressionPlotter(x, y)
+        assert not p.fit_reg
+
     def test_ci(self):
 
         p = lm._RegressionPlotter("x", "y", data=self.df, ci=95)