update docs, cleanup

doc/source/whatsnew/v0.24.0.rst

@@ -994,7 +994,6 @@ update the ``ExtensionDtype._metadata`` tuple to match the signature of your
 - :meth:`Series.astype` and :meth:`DataFrame.astype` now dispatch to :meth:`ExtensionArray.astype` (:issue:`21185:`).
 - Slicing a single row of a ``DataFrame`` with multiple ExtensionArrays of the same type now preserves the dtype, rather than coercing to object (:issue:`22784`)
 - Added :meth:`pandas.api.types.register_extension_dtype` to register an extension type with pandas (:issue:`22664`)
-- Added :meth:`pandas.api.extensions.ExtensionArray.where` (:issue:`24077`)
 - Bug when concatenating multiple ``Series`` with different extension dtypes not casting to object dtype (:issue:`22994`)
 - Series backed by an ``ExtensionArray`` now work with :func:`util.hash_pandas_object` (:issue:`23066`)
 - Updated the ``.type`` attribute for ``PeriodDtype``, ``DatetimeTZDtype``, and ``IntervalDtype`` to be instances of the dtype (``Period``, ``Timestamp``, and ``Interval`` respectively) (:issue:`22938`)

pandas/core/arrays/base.py

@@ -64,7 +64,6 @@ class ExtensionArray(object):
     * unique
     * factorize / _values_for_factorize
     * argsort / _values_for_argsort
-    * where
 
     The remaining methods implemented on this class should be performant,
     as they only compose abstract methods. Still, a more efficient
@@ -221,6 +220,8 @@ def __setitem__(self, key, value):
         #   example, a string like '2018-01-01' is coerced to a datetime
         #   when setting on a datetime64ns array. In general, if the
         #   __init__ method coerces that value, then so should __setitem__
+        # Note, also, that Series/DataFrame.where internally use __setitem__
+        # on a copy of the data.
         raise NotImplementedError(_not_implemented_message.format(
             type(self), '__setitem__')
         )

pandas/core/arrays/period.py

@@ -241,11 +241,6 @@ def _generate_range(cls, start, end, periods, freq, fields):
 
         return subarr, freq
 
-    def _check_compatible_with(self, other):
-        if self.freqstr != other.freqstr:
-            msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
-            raise IncompatibleFrequency(msg)
-
     # --------------------------------------------------------------------
     # Data / Attributes
 

pandas/core/dtypes/base.py

@@ -26,11 +26,6 @@ class _DtypeOpsMixin(object):
     na_value = np.nan
     _metadata = ()
 
-    @property
-    def _ndarray_na_value(self):
-        """Private method internal to pandas"""
-        raise AbstractMethodError(self)
-
     def __eq__(self, other):
         """Check whether 'other' is equal to self.
 

pandas/core/indexes/category.py

@@ -501,7 +501,14 @@ def _can_reindex(self, indexer):
 
     @Appender(_index_shared_docs['where'])
     def where(self, cond, other=None):
-        cat = self.values.where(cond, other=other)
+        # TODO: Investigate an alternative implementation with
+        # 1. copy the underyling Categorical
+        # 2. setitem with `cond` and `other`
+        # 3. Rebuild CategoricalIndex.
+        if other is None:
+            other = self._na_value
+        values = np.where(cond, self.values, other)
+        cat = Categorical(values, dtype=self.dtype)
         return self._shallow_copy(cat, **self._get_attributes_dict())
 
     def reindex(self, target, method=None, level=None, limit=None,

pandas/core/internals/blocks.py

@@ -1970,7 +1970,6 @@ def shift(self, periods, axis=0):
 
     def where(self, other, cond, align=True, errors='raise',
               try_cast=False, axis=0, transpose=False):
-        # rough attempt to see if
         if isinstance(other, (ABCIndexClass, ABCSeries)):
             other = other.array
 
@@ -2004,8 +2003,8 @@ def where(self, other, cond, align=True, errors='raise',
         else:
             dtype = self.dtype
 
+        # rough heuristic to see if the other array implements setitem
         if self._holder.__setitem__ is ExtensionArray.__setitem__:
-            # the array doesn't implement setitem, so convert to ndarray
             result = self._holder._from_sequence(
                 np.where(cond, self.values, other),
                 dtype=dtype,