Fix hue_order in PairGrid

This fixes #472.

This also changes the default `hue_order` to use the same `category_order`
rules as elsewhere in seaborn (cf #361).

doc/releases/v0.6.0.txt

@@ -71,3 +71,5 @@ Bug fixes
 - Fixed a bug in :class:`FacetGrid` and :class:`PairGrid` that lead to incorrect legend labels when levels of the ``hue`` variable appeared in ``hue_order`` but not in the data.
 
 - Fixed a bug in :meth:`FacetGrid.set_xticklabels` or :meth:`FacetGrid.set_yticklabels` when ``col_wrap`` is being used.
+
+- Fixed a bug in :class:`PairGrid` where the ``hue_order`` parameter was ignored.

seaborn/axisgrid.py

@@ -915,14 +915,11 @@ def __init__(self, data, hue=None, hue_order=None, palette=None,
         # Sort out the hue variable
         self._hue_var = hue
         if hue is None:
-            self.hue_names = None
+            self.hue_names = ["_nolegend_"]
             self.hue_vals = pd.Series(["_nolegend_"] * len(data),
                                       index=data.index)
         else:
-            if hue_order is None:
-                hue_names = np.unique(np.sort(data[hue]))
-            else:
-                hue_names = hue_order
+            hue_names = utils.categorical_order(data[hue], hue_order)
             if dropna:
                 # Filter NA from the list of unique hue names
                 hue_names = list(filter(pd.notnull, hue_names))
@@ -954,7 +951,14 @@ def map(self, func, **kwargs):
         for i, y_var in enumerate(self.y_vars):
             for j, x_var in enumerate(self.x_vars):
                 hue_grouped = self.data.groupby(self.hue_vals)
-                for k, (label_k, data_k) in enumerate(hue_grouped):
+                for k, label_k in enumerate(self.hue_names):
+
+                    # Attempt to get data for this level, allowing for empty
+                    try:
+                        data_k = hue_grouped.get_group(label_k)
+                    except KeyError:
+                        data_k = np.array([])
+
                     ax = self.axes[i, j]
                     plt.sca(ax)
 
@@ -1008,11 +1012,22 @@ def map_diag(self, func, **kwargs):
             # Special-case plt.hist with stacked bars
             if func is plt.hist:
                 plt.sca(ax)
-                vals = [v.values for g, v in hue_grouped]
+                vals = []
+                for label in self.hue_names:
+                    # Attempt to get data for this level, allowing for empty
+                    try:
+                        vals.append(hue_grouped.get_group(label))
+                    except KeyError:
+                        vals.append(np.array([]))
                 func(vals, color=self.palette, histtype="barstacked",
                      **kwargs)
             else:
-                for k, (label_k, data_k) in enumerate(hue_grouped):
+                for k, label_k in enumerate(self.hue_names):
+                    # Attempt to get data for this level, allowing for empty
+                    try:
+                        data_k = hue_grouped.get_group(label_k)
+                    except KeyError:
+                        data_k = np.array([])
                     plt.sca(ax)
                     func(data_k, label=label_k,
                          color=self.palette[k], **kwargs)
@@ -1034,7 +1049,13 @@ def map_lower(self, func, **kwargs):
         kw_color = kwargs.pop("color", None)
         for i, j in zip(*np.tril_indices_from(self.axes, -1)):
             hue_grouped = self.data.groupby(self.hue_vals)
-            for k, (label_k, data_k) in enumerate(hue_grouped):
+            for k, label_k in enumerate(self.hue_names):
+
+                # Attempt to get data for this level, allowing for empty
+                try:
+                    data_k = hue_grouped.get_group(label_k)
+                except KeyError:
+                    data_k = np.array([])
 
                 ax = self.axes[i, j]
                 plt.sca(ax)
@@ -1071,7 +1092,14 @@ def map_upper(self, func, **kwargs):
         for i, j in zip(*np.triu_indices_from(self.axes, 1)):
 
             hue_grouped = self.data.groupby(self.hue_vals)
-            for k, (label_k, data_k) in enumerate(hue_grouped):
+
+            for k, label_k in enumerate(self.hue_names):
+
+                # Attempt to get data for this level, allowing for empty
+                try:
+                    data_k = hue_grouped.get_group(label_k)
+                except KeyError:
+                    data_k = np.array([])
 
                 ax = self.axes[i, j]
                 plt.sca(ax)

seaborn/tests/test_axisgrid.py

@@ -891,6 +891,58 @@ def test_hue_kws(self):
         for line, marker in zip(g.axes[0, 0].lines, kws["marker"]):
             nt.assert_equal(line.get_marker(), marker)
 
+        g = ag.PairGrid(self.df, hue="a", hue_kws=kws,
+                        hue_order=list("dcab"))
+        g.map(plt.plot)
+
+        for line, marker in zip(g.axes[0, 0].lines, kws["marker"]):
+            nt.assert_equal(line.get_marker(), marker)
+
+        plt.close("all")
+
+    def test_hue_order(self):
+
+        order = list("dcab")
+        g = ag.PairGrid(self.df, hue="a", hue_order=order)
+        g.map(plt.plot)
+
+        for line, level in zip(g.axes[1, 0].lines, order):
+            x, y = line.get_xydata().T
+            npt.assert_array_equal(x, self.df.loc[self.df.a == level, "x"])
+            npt.assert_array_equal(y, self.df.loc[self.df.a == level, "y"])
+
+        plt.close("all")
+
+        g = ag.PairGrid(self.df, hue="a", hue_order=order)
+        g.map_diag(plt.plot)
+
+        for line, level in zip(g.axes[0, 0].lines, order):
+            x, y = line.get_xydata().T
+            npt.assert_array_equal(x, self.df.loc[self.df.a == level, "x"])
+            npt.assert_array_equal(y, self.df.loc[self.df.a == level, "x"])
+
+        plt.close("all")
+
+        g = ag.PairGrid(self.df, hue="a", hue_order=order)
+        g.map_lower(plt.plot)
+
+        for line, level in zip(g.axes[1, 0].lines, order):
+            x, y = line.get_xydata().T
+            npt.assert_array_equal(x, self.df.loc[self.df.a == level, "x"])
+            npt.assert_array_equal(y, self.df.loc[self.df.a == level, "y"])
+
+        plt.close("all")
+
+        g = ag.PairGrid(self.df, hue="a", hue_order=order)
+        g.map_upper(plt.plot)
+
+        for line, level in zip(g.axes[0, 1].lines, order):
+            x, y = line.get_xydata().T
+            npt.assert_array_equal(x, self.df.loc[self.df.a == level, "y"])
+            npt.assert_array_equal(y, self.df.loc[self.df.a == level, "x"])
+
+        plt.close("all")
+
     def test_nondefault_index(self):
 
         df = self.df.copy().set_index("b")