REF: DatetimeLikeArray

doc/source/whatsnew/v0.24.0.rst

@@ -1000,6 +1000,7 @@ update the ``ExtensionDtype._metadata`` tuple to match the signature of your
 - :meth:`DataFrame.stack` no longer converts to object dtype for DataFrames where each column has the same extension dtype. The output Series will have the same dtype as the columns (:issue:`23077`).
 - :meth:`Series.unstack` and :meth:`DataFrame.unstack` no longer convert extension arrays to object-dtype ndarrays. Each column in the output ``DataFrame`` will now have the same dtype as the input (:issue:`23077`).
 - Bug when grouping :meth:`Dataframe.groupby()` and aggregating on ``ExtensionArray`` it was not returning the actual ``ExtensionArray`` dtype (:issue:`23227`).
+- A default repr for :class:`ExtensionArray` is now provided (:issue:`23601`).
 
 .. _whatsnew_0240.api.incompatibilities:
 
@@ -1114,6 +1115,7 @@ Deprecations
 - The methods :meth:`Series.str.partition` and :meth:`Series.str.rpartition` have deprecated the ``pat`` keyword in favor of ``sep`` (:issue:`22676`)
 - Deprecated the `nthreads` keyword of :func:`pandas.read_feather` in favor of
   `use_threads` to reflect the changes in pyarrow 0.11.0. (:issue:`23053`)
+- :meth:`ExtensionArray._formatting_values` is deprecated. Use `ExtensionArray._formatter` instead. (:issue:`23601`)
 - :func:`pandas.read_excel` has deprecated accepting ``usecols`` as an integer. Please pass in a list of ints from 0 to ``usecols`` inclusive instead (:issue:`23527`)
 - Constructing a :class:`TimedeltaIndex` from data with ``datetime64``-dtyped data is deprecated, will raise ``TypeError`` in a future version (:issue:`23539`)
 - Constructing a :class:`DatetimeIndex` from data with ``timedelta64``-dtyped data is deprecated, will raise ``TypeError`` in a future version (:issue:`23675`)
@@ -1213,6 +1215,7 @@ Performance Improvements
   The speed increase is both when indexing by label (using .loc) and position(.iloc) (:issue:`20395`)
   Slicing a monotonically increasing :class:`CategoricalIndex` itself (i.e. ``ci[1000:2000]``)
   shows similar speed improvements as above (:issue:`21659`)
+- Improved performance of :meth:`CategoricalIndex.equals` when comparing to another :class:`CategoricalIndex` (:issue:`24023`)
 - Improved performance of :func:`Series.describe` in case of numeric dtpyes (:issue:`21274`)
 - Improved performance of :func:`pandas.core.groupby.GroupBy.rank` when dealing with tied rankings (:issue:`21237`)
 - Improved performance of :func:`DataFrame.set_index` with columns consisting of :class:`Period` objects (:issue:`21582`, :issue:`21606`)
@@ -1278,6 +1281,7 @@ Datetimelike
 - Bug in rounding methods of :class:`DatetimeIndex` (:meth:`~DatetimeIndex.round`, :meth:`~DatetimeIndex.ceil`, :meth:`~DatetimeIndex.floor`) and :class:`Timestamp` (:meth:`~Timestamp.round`, :meth:`~Timestamp.ceil`, :meth:`~Timestamp.floor`) could give rise to loss of precision (:issue:`22591`)
 - Bug in :func:`to_datetime` with an :class:`Index` argument that would drop the ``name`` from the result (:issue:`21697`)
 - Bug in :class:`PeriodIndex` where adding or subtracting a :class:`timedelta` or :class:`Tick` object produced incorrect results (:issue:`22988`)
+- Bug in the :class:`Series` repr with period-dtype data missing a space before the data (:issue:`23601`)
 - Bug in :func:`date_range` when decrementing a start date to a past end date by a negative frequency (:issue:`23270`)
 - Bug in :meth:`Series.min` which would return ``NaN`` instead of ``NaT`` when called on a series of ``NaT`` (:issue:`23282`)
 - Bug in :func:`DataFrame.combine` with datetimelike values raising a TypeError (:issue:`23079`)

pandas/_libs/reduction.pyx

@@ -17,6 +17,7 @@ cnp.import_array()
 
 cimport util
 from lib import maybe_convert_objects
+from tslibs.conversion import NS_DTYPE, TD_DTYPE
 
 
 cdef _get_result_array(object obj, Py_ssize_t size, Py_ssize_t cnt):
@@ -594,7 +595,14 @@ cdef class BlockSlider:
 
         # move and set the index
         self.idx_slider.move(start, end)
-        object.__setattr__(self.index, '_data', self.idx_slider.buf)
+
+        # TODO: unbreak this for other index types, if needed.
+        # I think the problem is that index.values is an ndarray,
+        # but index._data is an ExtensionArray.
+        if self.index.dtype == NS_DTYPE or self.index.dtype == TD_DTYPE:
+            object.__setattr__(self.index._data, '_data', self.idx_slider.buf)
+        else:
+            object.__setattr__(self.index, '_data', self.idx_slider.buf)
         self.index._engine.clear_mapping()
 
     cdef reset(self):

pandas/core/arrays/base.py

@@ -46,10 +46,12 @@ class ExtensionArray(object):
     * copy
     * _concat_same_type
 
-    An additional method is available to satisfy pandas' internal,
-    private block API.
+    A default repr displaying the type, (truncated) data, length,
+    and dtype is provided. It can be customized or replaced by
+    by overriding:
 
-    * _formatting_values
+    * __repr__ : A default repr for the ExtensionArray.
+    * _formatter : Print scalars inside a Series or DataFrame.
 
     Some methods require casting the ExtensionArray to an ndarray of Python
     objects with ``self.astype(object)``, which may be expensive. When
@@ -655,15 +657,65 @@ def copy(self, deep=False):
         raise AbstractMethodError(self)
 
     # ------------------------------------------------------------------------
-    # Block-related methods
+    # Printing
     # ------------------------------------------------------------------------
+    def __repr__(self):
+        from pandas.io.formats.printing import format_object_summary
+
+        template = (
+            u'{class_name}'
+            u'{data}\n'
+            u'Length: {length}, dtype: {dtype}'
+        )
+        # the short repr has no trailing newline, while the truncated
+        # repr does. So we include a newline in our template, and strip
+        # any trailing newlines from format_object_summary
+        data = format_object_summary(self, self._formatter(), name=False,
+                                     trailing_comma=False).rstrip()
+        class_name = u'<{}>\n'.format(self.__class__.__name__)
+        return template.format(class_name=class_name, data=data,
+                               length=len(self),
+                               dtype=self.dtype)
+
+    def _formatter(self, boxed=False):
+        # type: (bool) -> Callable[[Any], Optional[str]]
+        """Formatting function for scalar values.
+
+        This is used in the default '__repr__'. The returned formatting
+        function receives instances of your scalar type.
+
+        Parameters
+        ----------
+        boxed: bool, default False
+            An indicated for whether or not your array is being printed
+            within a Series, DataFrame, or Index (True), or just by
+            itself (False). This may be useful if you want scalar values
+            to appear differently within a Series versus on its own (e.g.
+            quoted or not).
+
+        Returns
+        -------
+        Callable[[Any], str]
+            A callable that gets instances of the scalar type and
+            returns a string. By default, :func:`repr` is used.
+        """
+        return repr
 
     def _formatting_values(self):
         # type: () -> np.ndarray
         # At the moment, this has to be an array since we use result.dtype
-        """An array of values to be printed in, e.g. the Series repr"""
+        """An array of values to be printed in, e.g. the Series repr
+
+        .. deprecated:: 0.24.0
+
+           Use :meth:`ExtensionArray._formatter` instead.
+        """
         return np.array(self)
 
+    # ------------------------------------------------------------------------
+    # Reshaping
+    # ------------------------------------------------------------------------
+
     @classmethod
     def _concat_same_type(cls, to_concat):
         # type: (Sequence[ExtensionArray]) -> ExtensionArray

pandas/core/arrays/categorical.py

@@ -484,6 +484,10 @@ def _constructor(self):
     def _from_sequence(cls, scalars, dtype=None, copy=False):
         return Categorical(scalars, dtype=dtype)
 
+    def _formatter(self, boxed=False):
+        # backwards compat with old printing.
+        return None
+
     def copy(self):
         """ Copy constructor. """
         return self._constructor(values=self._codes.copy(),
@@ -1977,6 +1981,10 @@ def __unicode__(self):
 
         return result
 
+    def __repr__(self):
+        # We want PandasObject.__repr__, which dispatches to __unicode__
+        return super(ExtensionArray, self).__repr__()
+
     def _maybe_coerce_indexer(self, indexer):
         """ return an indexer coerced to the codes dtype """
         if isinstance(indexer, np.ndarray) and indexer.dtype.kind == 'i':
@@ -2325,9 +2333,6 @@ def _concat_same_type(self, to_concat):
 
         return _concat_categorical(to_concat)
 
-    def _formatting_values(self):
-        return self
-
     def isin(self, values):
         """
         Check whether `values` are contained in Categorical.