API: Added ExtensionArray.where

We need some way to do `.where` on EA object for DatetimeArray. Adding it
to the interface is, I think, the easiest way.

Initially I started to write a version on ExtensionBlock, but it proved
to be unwieldy. to write a version that performed well for all types.
It *may* be possible to do using `_ndarray_values` but we'd need a few more
things around that (missing values, converting an arbitrary array to the
"same' ndarary_values, error handling, re-constructing). It seemed easier
to push this down to the array.

The implementation on ExtensionArray is readable, but likely slow since
it'll involve a conversion to object-dtype.

Closes pandas-dev#24077

Author: TomAugspurger

doc/source/whatsnew/v0.24.0.rst

@@ -994,6 +994,7 @@ 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`)
@@ -1236,6 +1237,7 @@ Performance Improvements
 - Improved performance of :meth:`DatetimeIndex.normalize` and :meth:`Timestamp.normalize` for timezone naive or UTC datetimes (:issue:`23634`)
 - Improved performance of :meth:`DatetimeIndex.tz_localize` and various ``DatetimeIndex`` attributes with dateutil UTC timezone (:issue:`23772`)
 - Improved performance of :class:`Categorical` constructor for `Series` objects (:issue:`23814`)
+- Improved performance of :meth:`~DataFrame.where` for Categorical data (:issue:`24077`)
 
 .. _whatsnew_0240.docs:
 

pandas/core/arrays/base.py

@@ -64,6 +64,7 @@ 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
@@ -661,6 +662,40 @@ def take(self, indices, allow_fill=False, fill_value=None):
         # pandas.api.extensions.take
         raise AbstractMethodError(self)
 
+    def where(self, cond, other):
+        """
+        Replace values where the condition is False.
+
+        Parameters
+        ----------
+        cond : ndarray or ExtensionArray
+            The mask indicating which values should be kept (True)
+            or replaced from `other` (False).
+
+        other : ndarray, ExtensionArray, or scalar
+            Entries where `cond` is False are replaced with
+            corresponding value from `other`.
+
+        Notes
+        -----
+        Note that `cond` and `other` *cannot* be a Series, Index, or callable.
+        When used from, e.g., :meth:`Series.where`, pandas will unbox
+        Series and Indexes, and will apply callables before they arrive here.
+
+        Returns
+        -------
+        ExtensionArray
+            Same dtype as the original.
+
+        See Also
+        --------
+        Series.where : Similar method for Series.
+        DataFrame.where : Similar method for DataFrame.
+        """
+        return type(self)._from_sequence(np.where(cond, self, other),
+                                         dtype=self.dtype,
+                                         copy=False)
+
     def copy(self, deep=False):
         # type: (bool) -> ExtensionArray
         """

pandas/core/arrays/categorical.py

@@ -1906,6 +1906,34 @@ def take_nd(self, indexer, allow_fill=None, fill_value=None):
 
     take = take_nd
 
+    def where(self, cond, other):
+        # n.b. this now preserves the type
+        codes = self._codes
+
+        if is_scalar(other) and isna(other):
+            other = -1
+        elif is_scalar(other):
+            item = self.categories.get_indexer([other]).item()
+
+            if item == -1:
+                raise ValueError("The value '{}' is not present in "
+                                 "this Categorical's categories".format(other))
+            other = item
+
+        elif is_categorical_dtype(other):
+            if not is_dtype_equal(self, other):
+                raise TypeError("The type of 'other' does not match.")
+            other = _get_codes_for_values(other, self.categories)
+            # get the codes from other that match our categories
+            pass
+        else:
+            other = np.where(isna(other), -1, other)
+
+        new_codes = np.where(cond, codes, other)
+        return type(self).from_codes(new_codes,
+                                     categories=self.categories,
+                                     ordered=self.ordered)
+
     def _slice(self, slicer):
         """
         Return a slice of myself.

pandas/core/arrays/interval.py

@@ -777,6 +777,18 @@ def take(self, indices, allow_fill=False, fill_value=None, axis=None,
 
         return self._shallow_copy(left_take, right_take)
 
+    def where(self, cond, other):
+        if is_scalar(other) and isna(other):
+            lother = other
+            rother = other
+        else:
+            self._check_closed_matches(other, name='other')
+            lother = other.left
+            rother = other.right
+        left = np.where(cond, self.left, lother)
+        right = np.where(cond, self.right, rother)
+        return self._shallow_copy(left, right)
+
     def value_counts(self, dropna=True):
         """
         Returns a Series containing counts of each interval.

pandas/core/arrays/period.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 
+from pandas._libs import lib
 from pandas._libs.tslibs import NaT, iNaT, period as libperiod
 from pandas._libs.tslibs.fields import isleapyear_arr
 from pandas._libs.tslibs.period import (
@@ -241,6 +242,11 @@ 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
 
@@ -341,6 +347,22 @@ def to_timestamp(self, freq=None, how='start'):
     # --------------------------------------------------------------------
     # Array-like / EA-Interface Methods
 
+    def where(self, cond, other):
+        # TODO(DatetimeArray): move to DatetimeLikeArrayMixin
+        # n.b. _ndarray_values candidate.
+        i8 = self.asi8
+        if lib.is_scalar(other):
+            if isna(other):
+                other = iNaT
+            elif isinstance(other, Period):
+                self._check_compatible_with(other)
+                other = other.ordinal
+        elif isinstance(other, type(self)):
+            self._check_compatible_with(other)
+            other = other.asi8
+        result = np.where(cond, i8, other)
+        return type(self)._simple_new(result, dtype=self.dtype)
+
     def _formatter(self, boxed=False):
         if boxed:
             return str

pandas/core/arrays/sparse.py

@@ -1063,6 +1063,20 @@ def take(self, indices, allow_fill=False, fill_value=None):
         return type(self)(result, fill_value=self.fill_value, kind=self.kind,
                           **kwargs)
 
+    def where(self, cond, other):
+        if is_scalar(other):
+            result_dtype = np.result_type(self.dtype.subtype, other)
+        elif isinstance(other, type(self)):
+            result_dtype = np.result_type(self.dtype.subtype,
+                                          other.dtype.subtype)
+        else:
+            result_dtype = np.result_type(self.dtype.subtype, other.dtype)
+
+        dtype = self.dtype.update_dtype(result_dtype)
+        # TODO: avoid converting to dense.
+        values = np.where(cond, self, other)
+        return type(self)(values, dtype=dtype)
+
     def _take_with_fill(self, indices, fill_value=None):
         if fill_value is None:
             fill_value = self.dtype.na_value

pandas/core/dtypes/base.py

@@ -26,6 +26,11 @@ 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

@@ -501,11 +501,7 @@ def _can_reindex(self, indexer):
 
     @Appender(_index_shared_docs['where'])
     def where(self, cond, other=None):
-        if other is None:
-            other = self._na_value
-        values = np.where(cond, self.values, other)
-
-        cat = Categorical(values, dtype=self.dtype)
+        cat = self.values.where(cond, other=other)
         return self._shallow_copy(cat, **self._get_attributes_dict())
 
     def reindex(self, target, method=None, level=None, limit=None,

pandas/core/internals/blocks.py

@@ -28,7 +28,8 @@
 from pandas.core.dtypes.dtypes import (
     CategoricalDtype, DatetimeTZDtype, ExtensionDtype, PandasExtensionDtype)
 from pandas.core.dtypes.generic import (
-    ABCDatetimeIndex, ABCExtensionArray, ABCIndexClass, ABCSeries)
+    ABCDataFrame, ABCDatetimeIndex, ABCExtensionArray, ABCIndexClass,
+    ABCSeries)
 from pandas.core.dtypes.missing import (
     _isna_compat, array_equivalent, is_null_datelike_scalar, isna, notna)
 
@@ -1967,6 +1968,30 @@ def shift(self, periods, axis=0):
                                            placement=self.mgr_locs,
                                            ndim=self.ndim)]
 
+    def where(self, other, cond, align=True, errors='raise',
+              try_cast=False, axis=0, transpose=False):
+        if isinstance(other, (ABCIndexClass, ABCSeries)):
+            other = other.array
+
+        if isinstance(cond, ABCDataFrame):
+            assert cond.shape[1] == 1
+            cond = cond.iloc[:, 0].array
+
+        if isinstance(other, ABCDataFrame):
+            assert other.shape[1] == 1
+            other = other.iloc[:, 0].array
+
+        if isinstance(cond, (ABCIndexClass, ABCSeries)):
+            cond = cond.array
+
+        if lib.is_scalar(other) and isna(other):
+            # The default `other` for Series / Frame is np.nan
+            # we want to replace that with the correct NA value
+            # for the type
+            other = self.dtype.na_value
+        result = self.values.where(cond, other)
+        return self.make_block_same_class(result, placement=self.mgr_locs)
+
     @property
     def _ftype(self):
         return getattr(self.values, '_pandas_ftype', Block._ftype)

pandas/tests/arrays/categorical/test_indexing.py

@@ -122,6 +122,32 @@ def test_get_indexer_non_unique(self, idx_values, key_values, key_class):
             tm.assert_numpy_array_equal(expected, result)
             tm.assert_numpy_array_equal(exp_miss, res_miss)
 
+    def test_where_raises(self):
+        arr = Categorical(['a', 'b', 'c'])
+        with pytest.raises(ValueError, match="The value 'd'"):
+            arr.where([True, False, True], 'd')
+
+    def test_where_unobserved_categories(self):
+        arr = Categorical(['a', 'b', 'c'], categories=['d', 'c', 'b', 'a'])
+        result = arr.where([True, True, False], other='b')
+        expected = Categorical(['a', 'b', 'b'], categories=arr.categories)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_where_other_categorical(self):
+        arr = Categorical(['a', 'b', 'c'], categories=['d', 'c', 'b', 'a'])
+        other = Categorical(['b', 'c', 'a'], categories=['a', 'c', 'b', 'd'])
+        result = arr.where([True, False, True], other)
+        expected = Categorical(['a', 'c', 'c'], dtype=arr.dtype)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_where_ordered_differs_rasies(self):
+        arr = Categorical(['a', 'b', 'c'], categories=['d', 'c', 'b', 'a'],
+                          ordered=True)
+        other = Categorical(['b', 'c', 'a'], categories=['a', 'c', 'b', 'd'],
+                            ordered=True)
+        with pytest.raises(TypeError, match="The type of"):
+            arr.where([True, False, True], other)
+
 
 @pytest.mark.parametrize("index", [True, False])
 def test_mask_with_boolean(index):

pandas/tests/arrays/interval/test_interval.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pytest
 
-from pandas import Index, IntervalIndex, date_range, timedelta_range
+from pandas import Index, Interval, IntervalIndex, date_range, timedelta_range
 from pandas.core.arrays import IntervalArray
 import pandas.util.testing as tm
 
@@ -50,6 +50,16 @@ def test_set_closed(self, closed, new_closed):
         expected = IntervalArray.from_breaks(range(10), closed=new_closed)
         tm.assert_extension_array_equal(result, expected)
 
+    @pytest.mark.parametrize('other', [
+        Interval(0, 1, closed='right'),
+        IntervalArray.from_breaks([1, 2, 3, 4], closed='right'),
+    ])
+    def test_where_raises(self, other):
+        arr = IntervalArray.from_breaks([1, 2, 3, 4], closed='left')
+        match = "'other.closed' is 'right', expected 'left'."
+        with pytest.raises(ValueError, match=match):
+            arr.where([True, False, True], other=other)
+
 
 class TestSetitem(object):
 

pandas/tests/arrays/test_period.py

@@ -197,6 +197,21 @@ def test_sub_period():
         arr - other
 
 
+# ----------------------------------------------------------------------------
+# Methods
+
+@pytest.mark.parametrize('other', [
+    pd.Period('2000', freq='H'),
+    period_array(['2000', '2001', '2000'], freq='H')
+])
+def test_where_different_freq_raises(other):
+    arr = period_array(['2000', '2001', '2002'], freq='D')
+    cond = np.array([True, False, True])
+    with pytest.raises(IncompatibleFrequency,
+                       match="Input has different freq=H"):
+        arr.where(cond, other)
+
+
 # ----------------------------------------------------------------------------
 # Printing
 

pandas/tests/extension/base/methods.py

@@ -198,3 +198,66 @@ def test_hash_pandas_object_works(self, data, as_frame):
         a = pd.util.hash_pandas_object(data)
         b = pd.util.hash_pandas_object(data)
         self.assert_equal(a, b)
+
+    @pytest.mark.parametrize("as_frame", [True, False])
+    def test_where_series(self, data, na_value, as_frame):
+        assert data[0] != data[1]
+        cls = type(data)
+        a, b = data[:2]
+
+        ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
+        cond = np.array([True, True, False, False])
+
+        if as_frame:
+            ser = ser.to_frame(name='a')
+            # TODO: alignment is broken for ndarray `cond`
+            cond = pd.DataFrame({"a": cond})
+
+        result = ser.where(cond)
+        expected = pd.Series(cls._from_sequence([a, a, na_value, na_value],
+                                                dtype=data.dtype))
+
+        if as_frame:
+            expected = expected.to_frame(name='a')
+        self.assert_equal(result, expected)
+
+        # array other
+        cond = np.array([True, False, True, True])
+        other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
+        if as_frame:
+            # TODO: alignment is broken for ndarray `cond`
+            other = pd.DataFrame({"a": other})
+            # TODO: alignment is broken for array `other`
+            cond = pd.DataFrame({"a": cond})
+        result = ser.where(cond, other)
+        expected = pd.Series(cls._from_sequence([a, b, b, b],
+                                                dtype=data.dtype))
+        if as_frame:
+            expected = expected.to_frame(name='a')
+        self.assert_equal(result, expected)
+    #
+    # def test_where_frame(self, data, na_value):
+    #     assert data[0] != data[1]
+    #     cls = type(data)
+    #     a, b = data[:2]
+    #
+    #     df = pd.DataFrame({
+    #         "A": cls._from_sequence([a, a, b, b], dtype=data.dtype)
+    #     })
+    #
+    #     cond = np.array([True, True, False, False]).reshape(-1, 1)
+    #     result = df.where(cond)
+    #     expected = pd.DataFrame({
+    #         "A": cls._from_sequence([a, a, na_value, na_value],
+    #                                 dtype=data.dtype)
+    #     })
+    #     self.assert_frame_equal(result, expected)
+    #
+    #     other = cls._from_sequence([a, b, a, b])
+    #     cond = np.array([True, False, True, True])
+    #     result = ser.where(cond, other)
+    #     expected = pd.Series(cls._from_sequence([a, b, b, b]))
+    #     self.assert_series_equal(result, expected)
+    #
+    #     # df = ser.to_frame(name='A')
+    #     # result = terr