cache and remove boxing

asv_bench/benchmarks/period.py

@@ -49,3 +49,28 @@ def time_value_counts_pindex(self):
         self.i.value_counts()
 
 
+class period_standard_indexing(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.index = PeriodIndex(start='1985', periods=1000, freq='D')
+        self.series = Series(range(1000), index=self.index)
+        self.period = self.index[500]
+
+    def time_get_loc(self):
+        self.index.get_loc(self.period)
+
+    def time_shape(self):
+        self.index.shape
+
+    def time_shallow_copy(self):
+        self.index._shallow_copy()
+
+    def time_series_loc(self):
+        self.series.loc[self.period]
+
+    def time_align(self):
+        DataFrame({'a': self.series, 'b': self.series[:500]})
+
+    def time_intersection(self):
+        self.index[:750].intersection(self.index[250:])

pandas/core/base.py

@@ -842,22 +842,22 @@ def data(self):
     @property
     def itemsize(self):
         """ return the size of the dtype of the item of the underlying data """
-        return self.values.itemsize
+        return self._values.itemsize
 
     @property
     def nbytes(self):
         """ return the number of bytes in the underlying data """
-        return self.values.nbytes
+        return self._values.nbytes
 
     @property
     def strides(self):
         """ return the strides of the underlying data """
-        return self.values.strides
+        return self._values.strides
 
     @property
     def size(self):
         """ return the number of elements in the underlying data """
-        return self.values.size
+        return self._values.size
 
     @property
     def flags(self):

pandas/tseries/period.py

@@ -305,7 +305,7 @@ def _simple_new(cls, values, name=None, freq=None, **kwargs):
             if (len(values) > 0 and is_float_dtype(values)):
                 raise TypeError("PeriodIndex can't take floats")
             else:
-                return PeriodIndex(values, name=name, freq=freq, **kwargs)
+                return cls(values, name=name, freq=freq, **kwargs)
 
         values = np.array(values, dtype='int64', copy=False)
 
@@ -326,6 +326,8 @@ def _shallow_copy(self, values=None, **kwargs):
         if kwargs.get('freq') is None:
             # freq must be provided
             kwargs['freq'] = self.freq
+        if values is None:
+            values = self._values
         return super(PeriodIndex, self)._shallow_copy(values=values, **kwargs)
 
     def _coerce_scalar_to_index(self, item):
@@ -356,9 +358,8 @@ def __contains__(self, key):
     def asi8(self):
         return self._values.view('i8')
 
-    @property
+    @cache_readonly
     def _int64index(self):
-        # do not cache, same as .asi8
         return Int64Index(self.asi8, name=self.name, fastpath=True)
 
     @property