Just moves

pandas/core/indexes/period.py

@@ -162,6 +162,7 @@ class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin,
  _infer_as_myclass = True
 
  _freq = None
+ _data = None # type: PeriodArray
 
  _engine_type = libindex.PeriodEngine
 
@@ -177,47 +178,18 @@ def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
  tz=tz, dtype=dtype, copy=copy, **fields)
  return cls._simple_new(data, name=name)
 
+ # ------------------------------------------------------------------------
+ # Data
  @property
  def _ndarray_values(self):
  return self.values.values
 
- # ------------------------------------------------------------------------
- # Mixin Candidates
- # err. maybe not...
-
- @property
- def _box_func(self):
- def func(x):
- if isinstance(x, Period) or x is tslib.NaT:
- return x
- else:
- return Period._from_ordinal(ordinal=x, freq=self.freq)
- return func
-
- # ------------------------------------------------------------------------
- # Straight Dispatch
-
  @property
- def freq(self):
- """Return the frequency object if it is set, otherwise None"""
- return self.values.freq
+ def values(self):
+ return self._data
 
  # ------------------------------------------------------------------------
- # Dispatch and Wrap
-
- def asfreq(self, freq=None, how='E'):
- result = self.values.asfreq(freq=freq, how=how)
- return self._simple_new(result, name=self.name)
- # ------------------------------------------------------------------------
-
-
- @cache_readonly
- def _engine(self):
- return self._engine_type(lambda: self._ndarray_values, len(self))
-
- @property
- def asi8(self):
- return self.values.asi8
+ # Index Constructors
 
  @classmethod
  def _simple_new(cls, values, name=None, freq=None, **kwargs):
@@ -265,28 +237,67 @@ def _shallow_copy(self, values=None, **kwargs):
  attributes['dtype'] = self.dtype
  return self._simple_new(values, **attributes)
 
+ def _shallow_copy_with_infer(self, values=None, **kwargs):
+ """ we always want to return a PeriodIndex """
+ return self._shallow_copy(values=values, **kwargs)
+
+ # ------------------------------------------------------------------------
+ # Boxing
+ # err. maybe not...
+
+ @property
+ def _box_func(self):
+ def func(x):
+ if isinstance(x, Period) or x is tslib.NaT:
+ return x
+ else:
+ return Period._from_ordinal(ordinal=x, freq=self.freq)
+ return func
+
  def _maybe_box_as_values(self, values, **attribs):
+ """Box an array of ordinals to a PeriodArray
+
+ This is purely for compatibility between PeriodIndex
+ and Datetime/TimedeltaIndex. Once these are all backed by
+ an ExtensionArray, this can be removed
+ """
  freq = attribs['freq']
  return PeriodArray._from_ordinals(values, freq=freq)
 
- def shift(self, n):
- # TODO: May need to
- result = self.values.shift(n)
- return self._simple_new(result, name=self.name)
+ # ------------------------------------------------------------------------
+ # Straight Dispatch
 
- def _shallow_copy_with_infer(self, values=None, **kwargs):
- """ we always want to return a PeriodIndex """
- return self._shallow_copy(values=values, **kwargs)
+ @property
+ def freq(self):
+ """Return the frequency object if it is set, otherwise None"""
+ return self._data.freq
 
- def _coerce_scalar_to_index(self, item):
-  """
- we need to coerce a scalar to a compat for our index type
+ @property
+ def asi8(self):
+ return self._data.asi8
 
- Parameters
- ----------
- item : scalar item to coerce
- """
- return PeriodIndex([item], **self._get_attributes_dict())
+ @property
+ def size(self):
+ # Avoid materializing self._values
+ return self._data.size
+
+ @property
+ def shape(self):
+ # Avoid materializing self._values
+ return self._data.shape
+ # ------------------------------------------------------------------------
+ # Dispatch and Wrap
+
+ def asfreq(self, freq=None, how='E'):
+ result = self._data.asfreq(freq=freq, how=how)
+ return self._simple_new(result, name=self.name)
+
+ # ------------------------------------------------------------------------
+ # Indexing
+ @cache_readonly
+ def _engine(self):
+ # TODO: understand indexing before just changing this.
+ return self._engine_type(lambda: self._ndarray_values, len(self))
 
  @Appender(_index_shared_docs['__contains__'])
  def __contains__(self, key):
@@ -308,9 +319,41 @@ def __contains__(self, key):
  def _int64index(self):
  return Int64Index(self.asi8, name=self.name, fastpath=True)
 
- @property
- def values(self):
- return self._data
+ # ------------------------------------------------------------------------
+ # Index Methods
+
+ def shift(self, n):
+ """
+ Specialized shift which produces a PeriodIndex
+
+ Parameters
+ ----------
+ n : int
+ Periods to shift by
+
+ Returns
+ -------
+ shifted : PeriodIndex
+ """
+ # TODO: docs
+ # Note, this differs from the definition of ExtensionArray.shift
+ # 1. EA.shift takes a single `periods` argument, this accepts array
+ # 2. This accepts a `freq` argument
+ # so we don't dispatch
+ values = self._ndarray_values + n * self.freq.n
+ if self.hasnans:
+ values[self._isnan] = tslib.iNaT
+ return self._shallow_copy(values=values)
+
+ def _coerce_scalar_to_index(self, item):
+ """
+ we need to coerce a scalar to a compat for our index type
+
+ Parameters
+ ----------
+ item : scalar item to coerce
+ """
+ return PeriodIndex([item], **self._get_attributes_dict())
 
  def __array__(self, dtype=None):
  if is_integer_dtype(dtype):
@@ -362,16 +405,6 @@ def _to_embed(self, keep_tz=False, dtype=None):
 
  return self.astype(object).values
 
- @property
- def size(self):
- # Avoid materializing self._values
- return self._ndarray_values.size
-
- @property
- def shape(self):
- # Avoid materializing self._values
- return self._ndarray_values.shape
-
  @property
  def _formatter_func(self):
  return lambda x: "'%s'" % x