ENH: support for ordered factors in groupby, close #292

pandas/core/factor.py

@@ -21,7 +21,7 @@ class Factor(object):
       * labels : ndarray
       * levels : ndarray
     """
-    def __init__(self, labels, levels):
+    def __init__(self, labels, levels, name=None):
         from pandas.core.index import _ensure_index
 
         levels = _ensure_index(levels)
@@ -30,6 +30,7 @@ def __init__(self, labels, levels):
 
         self.labels = labels
         self.levels = levels
+        self.name = name
 
     @classmethod
     def from_array(cls, data):
@@ -51,9 +52,10 @@ def __len__(self):
         return len(self.labels)
 
     def __repr__(self):
-        temp = 'Factor:\n%s\nLevels (%d): %s'
+        temp = 'Factor:%s\n%s\nLevels (%d): %s'
         values = np.asarray(self)
-        return temp % (repr(values), len(self.levels), self.levels)
+        return temp % ('' if self.name is None else self.name,
+                       repr(values), len(self.levels), self.levels)
 
     def __getitem__(self, key):
         if isinstance(key, (int, np.integer)):

pandas/core/groupby.py

@@ -2,6 +2,7 @@
 import types
 import numpy as np
 
+from pandas.core.factor import Factor
 from pandas.core.frame import DataFrame
 from pandas.core.generic import NDFrame
 from pandas.core.index import Index, MultiIndex, _ensure_index
@@ -972,6 +973,17 @@ def __init__(self, index, grouper=None, name=None, level=None,
         else:
             if isinstance(self.grouper, (list, tuple)):
                 self.grouper = com._asarray_tuplesafe(self.grouper)
+            elif isinstance(self.grouper, Factor):
+                factor = self.grouper
+                self._was_factor = True
+
+                # Is there any way to avoid this?
+                self.grouper = np.asarray(factor)
+
+                self._labels = factor.labels
+                self._group_index = factor.levels
+                if self.name is None:
+                    self.name = factor.name
 
             # no level passed
             if not isinstance(self.grouper, np.ndarray):

pandas/tests/test_groupby.py

@@ -7,7 +7,7 @@
 from pandas import bdate_range
 from pandas.core.index import Index, MultiIndex
 from pandas.core.common import rands
-from pandas.core.frame import DataFrame
+from pandas.core.api import Factor, DataFrame
 from pandas.core.groupby import GroupByError
 from pandas.core.series import Series
 from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
@@ -1879,6 +1879,33 @@ def test_no_dummy_key_names(self):
                                   self.df['B'].values]).sum()
         self.assert_(result.index.names == [None, None])
 
+    def test_groupby_factor(self):
+        levels = ['foo', 'bar', 'baz', 'qux']
+        labels = np.random.randint(0, 4, size=100)
+
+        factor = Factor(labels, levels, name='myfactor')
+
+        data = DataFrame(np.random.randn(100, 4))
+
+        result = data.groupby(factor).mean()
+
+        expected = data.groupby(np.asarray(factor)).mean()
+        expected = expected.reindex(levels)
+
+        assert_frame_equal(result, expected)
+        self.assert_(result.index.name == factor.name)
+
+        grouped = data.groupby(factor)
+        desc_result = grouped.describe()
+
+        idx = factor.labels.argsort()
+        ord_labels = np.asarray(factor).take(idx)
+        ord_data = data.take(idx)
+        expected = ord_data.groupby(ord_labels, sort=False).describe()
+        assert_frame_equal(desc_result, expected)
+
+
+
 def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
     tups = map(tuple, df[keys].values)
     tups = com._asarray_tuplesafe(tups)