cleanup

pandas/core/dtypes/cast.py

@@ -927,7 +927,7 @@ def try_timedelta(v):
         # will try first with a string & object conversion
         from pandas import to_timedelta
         try:
-            return to_timedelta(v)._values.reshape(shape)
+            return to_timedelta(v)._ndarray_values.reshape(shape)
         except Exception:
             return v.reshape(shape)
 

pandas/core/indexes/category.py

@@ -1,6 +1,5 @@
 import numpy as np
 from pandas._libs import index as libindex
-from pandas._libs import join as libjoin
 
 from pandas import compat
 from pandas.compat.numpy import function as nv
@@ -9,8 +8,6 @@
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
     _ensure_platform_int,
-    _ensure_int32,
-    _ensure_int64,
     is_list_like,
     is_interval_dtype,
     is_scalar)
@@ -217,14 +214,6 @@ def _shallow_copy(self, values=None, categories=None, ordered=None,
             values=values, categories=categories,
             ordered=ordered, **kwargs)
 
-    @cache_readonly
-    def _inner_indexer(self):
-        if self.codes.dtype.itemsize <= 4:
-            # int8, int16, int32
-            return libjoin.inner_join_indexer_int32
-        else:
-            return libjoin.inner_join_indexer_int64
-
     def _is_dtype_compat(self, other):
         """
         *this is an internal non-public method*
@@ -238,7 +227,7 @@ def _is_dtype_compat(self, other):
         """
         if is_categorical_dtype(other):
             if isinstance(other, CategoricalIndex):
-                other = other.values
+                other = other._values
             if not other.is_dtype_equal(self):
                 raise TypeError("categories must match existing categories "
                                 "when appending")

pandas/core/indexes/multi.py

@@ -2507,6 +2507,7 @@ def get_locs(self, seq):
         MultiIndex.slice_locs : Get slice location given start label(s) and
                                 end label(s).
         """
+        from .numeric import Int64Index
 
         # must be lexsorted to at least as many levels
         true_slices = [i for (i, s) in enumerate(com.is_true_slices(seq)) if s]
@@ -2532,7 +2533,6 @@ def _convert_to_indexer(r):
                                      "that is not the same length as the "
                                      "index")
                 r = r.nonzero()[0]
-            from .numeric import Int64Index
             return Int64Index(r)
 
         def _update_indexer(idxr, indexer=indexer):
@@ -2569,7 +2569,6 @@ def _update_indexer(idxr, indexer=indexer):
                 if indexers is not None:
                     indexer = _update_indexer(indexers, indexer=indexer)
                 else:
-                    from .numeric import Int64Index
                     # no matches we are done
                     return Int64Index([])._ndarray_values
 
@@ -2652,9 +2651,8 @@ def equals(self, other):
         for i in range(self.nlevels):
             slabels = self.labels[i]
             slabels = slabels[slabels != -1]
-            svalues = algos.take_nd(
-                np.asarray(self.levels[i]._values),
-                slabels, allow_fill=False)
+            svalues = algos.take_nd(np.asarray(self.levels[i]._values),
+                                    slabels, allow_fill=False)
 
             olabels = other.labels[i]
             olabels = olabels[olabels != -1]

pandas/core/indexes/period.py

@@ -418,11 +418,6 @@ def _int64index(self):
     def values(self):
         return self.astype(object).values
 
-    @property
-    def _values(self):
-        # TODO: return PeriodArray
-        return self.values
-
     @property
     def _ndarray_values(self):
         # Ordinals