Test for datetime input on jointplot, code to pass test (#3664)

seaborn/axisgrid.py

@@ -1704,6 +1704,7 @@ def __init__(
         self.ax_joint = ax_joint
         self.ax_marg_x = ax_marg_x
         self.ax_marg_y = ax_marg_y
+        self.is_datetime_x = False
 
         # Turn off tick visibility for the measure axis on the marginal plots
         plt.setp(ax_marg_x.get_xticklabels(), visible=False)
@@ -1736,6 +1737,9 @@ def get_var(var):
             vector = plot_data.get(var, None)
             if vector is not None:
                 vector = vector.rename(p.variables.get(var, None))
+            if np.issubdtype(vector, np.datetime64):
+                vector = vector.astype(int)
+                self.is_datetime_x = True
             return vector
 
         self.x = get_var("x")
@@ -1833,6 +1837,14 @@ def plot_joint(self, func, **kwargs):
         else:
             func(self.x, self.y, **kwargs)
 
+        if self.is_datetime_x:
+            xtick_arr = self.ax_joint.get_xticks()
+            date_label = xtick_arr.astype("datetime64[ns]")
+            date_label = [str(dt)[0:10] for dt in date_label]
+            self.ax_joint.set_xticks(self.ax_joint.get_xticks())
+            self.ax_joint.set_xticklabels(date_label, rotation=45)
+            self._figure.tight_layout()
+
         return self
 
     def plot_marginals(self, func, **kwargs):

seaborn/distributions.py

@@ -2376,6 +2376,8 @@ def _freedman_diaconis_bins(a):
     """Calculate number of hist bins using Freedman-Diaconis rule."""
     # From https://stats.stackexchange.com/questions/798/
     a = np.asarray(a)
+    if a.dtype.type == np.datetime64:
+        a = a.astype(np.int64)
     if len(a) < 2:
         return 1
     iqr = np.subtract.reduce(np.nanpercentile(a, [75, 25]))

tests/test_axisgrid.py

@@ -1878,3 +1878,9 @@ def test_ax_warning(self, long_df):
         with pytest.warns(UserWarning):
             g = ag.jointplot(data=long_df, x="x", y="y", ax=ax)
         assert g.ax_joint.collections
+
+    def test_datetime_input(self):
+        dates = np.array(
+            ["2023-01-01", "2023-01-02", "2023-01-03"], dtype="datetime64[ns]"
+        )
+        ag.jointplot(x=dates, y=[1, 2, 3], kind="hex")