BUG: (GH4726) bug in getting a cross-sectional using iloc/loc with a duplicate items index

doc/source/release.rst

@@ -307,8 +307,10 @@ See :ref:`Internal Refactoring<whatsnew_0130.refactoring>`
   - Fix boolean comparison with a DataFrame on the lhs, and a list/tuple on the rhs (:issue:`4576`)
   - Fix error/dtype conversion with setitem of ``None`` on ``Series/DataFrame`` (:issue:`4667`)
   - Fix decoding based on a passed in non-default encoding in ``pd.read_stata`` (:issue:`4626`)
-  - Fix some inconsistencies with ``Index.rename`` and ``MultiIndex.rename`` (:issue:`4718`, :issue:`4628`)
   - Fix ``DataFrame.from_records`` with a plain-vanilla ``ndarray``. (:issue:`4727`)
+  - Fix some inconsistencies with ``Index.rename`` and ``MultiIndex.rename``,
+    etc. (:issue:`4718`, :issue:`4628`)
+  - Bug in using ``iloc/loc`` with a cross-sectional and duplicate indicies (:issue:`4726`)
 
 pandas 0.12
 ===========

pandas/core/internals.py

@@ -72,9 +72,6 @@ def __init__(self, values, items, ref_items, ndim=None, fastpath=False, placemen
             self.items = _ensure_index(items)
             self.ref_items = _ensure_index(ref_items)
 
-    def _gi(self, arg):
-        return self.values[arg]
-
     @property
     def _consolidate_key(self):
         return (self._can_consolidate, self.dtype.name)
@@ -1165,9 +1162,6 @@ def __init__(self, values, items, ref_items, fastpath=False, placement=None, **k
         super(DatetimeBlock, self).__init__(values, items, ref_items,
                                             fastpath=True, placement=placement, **kwargs)
 
-    def _gi(self, arg):
-        return lib.Timestamp(self.values[arg])
-
     def _can_hold_element(self, element):
         if is_list_like(element):
             element = np.array(element)
@@ -1200,7 +1194,7 @@ def _try_coerce_result(self, result):
             if result.dtype == 'i8':
                 result = tslib.array_to_datetime(
                     result.astype(object).ravel()).reshape(result.shape)
-        elif isinstance(result, np.integer):
+        elif isinstance(result, (np.integer, np.datetime64)):
             result = lib.Timestamp(result)
         return result
 
@@ -1267,10 +1261,9 @@ def set(self, item, value):
         self.values[loc] = value
 
     def get_values(self, dtype=None):
+        # return object dtype as Timestamps
         if dtype == object:
-            flat_i8 = self.values.ravel().view(np.int64)
-            res = tslib.ints_to_pydatetime(flat_i8)
-            return res.reshape(self.values.shape)
+            return lib.map_infer(self.values.ravel(), lib.Timestamp).reshape(self.values.shape)
         return self.values
 
 
@@ -2272,7 +2265,8 @@ def xs(self, key, axis=1, copy=True):
 
     def fast_2d_xs(self, loc, copy=False):
         """
-
+        get a cross sectional for a given location in the
+        items ; handle dups
         """
         if len(self.blocks) == 1:
             result = self.blocks[0].values[:, loc]
@@ -2284,15 +2278,20 @@ def fast_2d_xs(self, loc, copy=False):
             raise Exception('cannot get view of mixed-type or '
                             'non-consolidated DataFrame')
 
-        dtype = _interleaved_dtype(self.blocks)
-
         items = self.items
+
+        # non-unique (GH4726)
+        if not items.is_unique:
+            return self._interleave(items).ravel()
+
+        # unique
+        dtype = _interleaved_dtype(self.blocks)
         n = len(items)
         result = np.empty(n, dtype=dtype)
         for blk in self.blocks:
             for j, item in enumerate(blk.items):
                 i = items.get_loc(item)
-                result[i] = blk._gi((j, loc))
+                result[i] = blk._try_coerce_result(blk.iget((j, loc)))
 
         return result
 

pandas/tests/test_indexing.py

@@ -1215,6 +1215,20 @@ def test_astype_assignment_with_iloc(self):
         result = df.get_dtype_counts().sort_index()
         expected = Series({ 'int64' : 4, 'float64' : 1, 'object' : 2 }).sort_index()
 
+    def test_dups_loc(self):
+
+        # GH4726
+        # dup indexing with iloc/loc
+        df = DataFrame([[1,2,'foo','bar',Timestamp('20130101')]],
+                       columns=['a','a','a','a','a'],index=[1])
+        expected = Series([1,2,'foo','bar',Timestamp('20130101')],index=['a','a','a','a','a'])
+
+        result = df.iloc[0]
+        assert_series_equal(result,expected)
+
+        result = df.loc[1]
+        assert_series_equal(result,expected)
+
 if __name__ == '__main__':
     import nose
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],