Respect pandas categorical order in category plots

This will include levels that appear in the `category` list, but that
do not appear in the data.

See #361

seaborn/categorical.py

@@ -13,9 +13,10 @@
 from .external.six.moves import range
 
 from . import utils
-from .utils import desaturate, iqr
+from .utils import desaturate, iqr, categorical_order
 from .algorithms import bootstrap
 from .palettes import color_palette, husl_palette, light_palette
+from .axisgrid import FacetGrid
 
 
 class _CategoricalPlotter(object):
@@ -189,7 +190,7 @@ def establish_variables(self, x=None, y=None, hue=None, data=None,
                     group_label = groups.name
 
                 # Get the order on the categorical axis
-                group_names = self._category_order(groups, order)
+                group_names = categorical_order(groups, order)
 
                 # Group the numeric data
                 plot_data, value_label = self._group_longform(vals, groups,
@@ -203,7 +204,7 @@ def establish_variables(self, x=None, y=None, hue=None, data=None,
                 else:
 
                     # Get the order of the hue levels
-                    hue_names = self._category_order(hue, hue_order)
+                    hue_names = categorical_order(hue, hue_order)
 
                     # Group the hue data
                     plot_hues, hue_title = self._group_longform(hue, groups,
@@ -228,15 +229,6 @@ def establish_variables(self, x=None, y=None, hue=None, data=None,
         self.hue_names = hue_names
         self.plot_units = plot_units
 
-    def _category_order(self, data, order):
-        """Get the order of levels for a categorical variable."""
-        if order is None:
-            try:
-                order = data.unique()
-            except AttributeError:
-                order = pd.unique(data)
-        return list(order)
-
     def _group_longform(self, vals, grouper, order):
         """Group a long-form variable by another with correct order."""
         # Ensure that the groupby will work
@@ -2050,10 +2042,6 @@ def barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None,
         kwargs.pop("dropna")
         warn = True
 
-    if "label" in kwargs:
-        kwargs.pop("label")
-        warn = True
-
     if "x_order" in kwargs:
         order = kwargs.pop("x_order")
         warn = True

seaborn/tests/test_categorical.py

@@ -290,6 +290,11 @@ def test_order(self):
             for group, vals in zip(["c", "b", "a"], p.plot_data):
                 npt.assert_array_equal(vals, self.y[self.g == group])
 
+            df.g = (df.g.cat.add_categories("d")
+                        .cat.reorder_categories(["c", "b", "d", "a"]))
+            p.establish_variables("g", "y", data=df)
+            nt.assert_equal(p.group_names, ["c", "b", "d", "a"])
+
     def test_hue_order(self):
 
         p = cat._CategoricalPlotter()
@@ -311,6 +316,11 @@ def test_hue_order(self):
             p.establish_variables("g", "y", "h", data=df)
             nt.assert_equal(p.hue_names, ["n", "m"])
 
+            df.h = (df.h.cat.add_categories("o")
+                        .cat.reorder_categories(["o", "m", "n"]))
+            p.establish_variables("g", "y", "h", data=df)
+            nt.assert_equal(p.hue_names, ["o", "m", "n"])
+
     def test_plot_units(self):
 
         p = cat._CategoricalPlotter()

seaborn/tests/test_utils.py

@@ -2,12 +2,16 @@
 import warnings
 
 import numpy as np
+import pandas as pd
 import matplotlib.pyplot as plt
 from numpy.testing import assert_array_equal
 import nose
 import nose.tools as nt
 from nose.tools import assert_equal, raises
 
+from distutils.version import LooseVersion
+pandas_has_categoricals = LooseVersion(pd.__version__) >= "0.15"
+
 from .. import utils, rcmod
 
 
@@ -244,3 +248,36 @@ def test_ticklabels_overlap():
     x, y = utils.axes_ticklabels_overlap(ax)
     assert x
     assert not y
+
+
+def test_category_order():
+
+    x = ["a", "c", "c", "b", "a", "d"]
+    order = ["a", "b", "c", "d"]
+
+    out = utils.categorical_order(x)
+    nt.assert_equal(out, ["a", "c", "b", "d"])
+
+    out = utils.categorical_order(x, order)
+    nt.assert_equal(out, order)
+
+    out = utils.categorical_order(x, ["b", "a"])
+    nt.assert_equal(out, ["b", "a"])
+
+    out = utils.categorical_order(np.array(x))
+    nt.assert_equal(out, ["a", "c", "b", "d"])
+
+    out = utils.categorical_order(pd.Series(x))
+    nt.assert_equal(out, ["a", "c", "b", "d"])
+
+    if pandas_has_categoricals:
+        x = pd.Categorical(x, order)
+        out = utils.categorical_order(x)
+        nt.assert_equal(out, list(x.categories))
+
+        x = pd.Series(x)
+        out = utils.categorical_order(x)
+        nt.assert_equal(out, list(x.cat.categories))
+
+        out = utils.categorical_order(x, ["b", "a"])
+        nt.assert_equal(out, ["b", "a"])

seaborn/utils.py

@@ -414,3 +414,21 @@ def axes_ticklabels_overlap(ax):
     """
     return (axis_ticklabels_overlap(ax.get_xticklabels()),
             axis_ticklabels_overlap(ax.get_yticklabels()))
+
+
+def categorical_order(values, order=None):
+    """Return a list of unique data values.
+
+    """
+    if order is None:
+        if hasattr(values, "cat"):
+            order = values.cat.categories
+        elif hasattr(values, "categories"):
+            order = values.categories
+        else:
+            try:
+                order = values.unique()
+            except AttributeError:
+                order = pd.unique(values)
+
+    return list(order)