REGR: ensure passed binlabels to pd.cut have a compat dtype on output (#10140)

Author: jreback

doc/source/whatsnew/v0.16.2.txt

@@ -69,6 +69,7 @@ Bug Fixes
 - Bung in ``Series`` arithmetic methods may incorrectly hold names (:issue:`10068`)
 
 - Bug in ``DatetimeIndex`` and ``TimedeltaIndex`` names are lost after timedelta arithmetics ( :issue:`9926`)
+- Regression in ``pd.cut` to ensure passed ``binlabels`` have a compat dtype on output (:issue:`10140`)
 
 
 - Bug in `Series.plot(label="LABEL")` not correctly setting the label (:issue:`10119`)

pandas/tools/tests/test_tile.py

@@ -4,7 +4,8 @@
 import numpy as np
 from pandas.compat import zip
 
-from pandas import DataFrame, Series, unique
+from pandas import (DataFrame, Series, unique, Index, Categorical, CategoricalIndex,
+                    DatetimeIndex, TimedeltaIndex)
 import pandas.util.testing as tm
 from pandas.util.testing import assertRaisesRegexp
 import pandas.core.common as com
@@ -97,6 +98,45 @@ def test_label_precision(self):
                      '(0.54, 0.72]']
         self.assert_numpy_array_equal(result.categories, ex_levels)
 
+    def test_label_coercion(self):
+        # GH10140
+
+        df = DataFrame({'x' : 100 * np.random.random(100)})
+        df['y'] = df.x**2
+
+        binedges = np.arange(0,110,10)
+        binlabels = np.arange(5,105,10)
+
+        # passing in an index
+        for bl, expected in [(Index(binlabels), np.dtype('int64')),
+                             (DatetimeIndex(['20130101']*len(binlabels))+TimedeltaIndex(binlabels,unit='D'),np.dtype('M8[ns]')),
+                             (TimedeltaIndex(binlabels,unit='D'),np.dtype('m8[ns]')),
+                             (Categorical(binlabels), 'category'),
+                             (Index(Index(binlabels).map(str)), 'category')]:
+            result = cut(df.x, bins=binedges, labels=bl)
+            self.assertEqual(result.dtype, expected)
+            z = df.groupby(result).y.mean()
+            self.assertEqual(z.index.dtype, expected)
+
+        # passing in a list-like
+        for bl, expected in [(Index(binlabels), np.dtype('int64')),
+                             (Index(Index(binlabels).map(str)), 'category')]:
+            bl = np.asarray(bl)
+            result = cut(df.x, bins=binedges, labels=bl)
+            self.assertEqual(result.dtype, expected)
+            z = df.groupby(result).y.mean()
+            self.assertEqual(z.index.dtype, expected)
+
+        # reversed categories
+        bl = Categorical(binlabels,categories=binlabels[::-1],ordered=True)
+        expected = Index(bl).dtype
+        result = cut(df.x, bins=binedges, labels=bl)
+        self.assertEqual(result.dtype, expected)
+        z = df.groupby(result).y.mean()
+        self.assertEqual(z.index.dtype, expected)
+        tm.assert_index_equal(z.index,
+                              CategoricalIndex(Categorical.from_codes(np.arange(len(bl)),categories=bl.categories,ordered=True),name='x'))
+
     def test_na_handling(self):
         arr = np.arange(0, 0.75, 0.01)
         arr[::3] = np.nan

pandas/tools/tile.py

@@ -2,7 +2,7 @@
 Quantilization functions and related stuff
 """
 
-from pandas.core.api import DataFrame, Series
+from pandas.core.api import DataFrame, Series, Index
 from pandas.core.categorical import Categorical
 from pandas.core.index import _ensure_index
 import pandas.core.algorithms as algos
@@ -195,6 +195,14 @@ def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False,
     has_nas = na_mask.any()
 
     if labels is not False:
+
+        def to_categorical(levels):
+            if com.is_categorical_dtype(levels):
+                levels = levels.categories
+            np.putmask(ids, na_mask, 0)
+            fac = Categorical(ids - 1, levels, ordered=True, name=name, fastpath=True)
+            return fac
+
         if labels is None:
             increases = 0
             while True:
@@ -209,15 +217,21 @@ def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False,
                 else:
                     break
 
+            fac = to_categorical(levels)
+
         else:
             if len(labels) != len(bins) - 1:
                 raise ValueError('Bin labels must be one fewer than '
                                  'the number of bin edges')
-            levels = labels
 
-        levels = np.asarray(levels, dtype=object)
-        np.putmask(ids, na_mask, 0)
-        fac = Categorical(ids - 1, levels, ordered=True, name=name, fastpath=True)
+            # we want to coerce the resultant Categorical to the binlabels type if supplied
+            # if we are passed a Categorical in the binlabels, then use this dtype
+            # 10140
+            labels = _ensure_index(labels)
+            fac = to_categorical(labels)
+            if not (com.is_object_dtype(labels) or com.is_categorical_dtype(labels)):
+                fac = type(labels)(np.asarray(fac))
+
     else:
         fac = ids - 1
         if has_nas: