BUG: Issues with DatetimeTZ values in where and combine_first (pandas-dev#21469 + pandas-dev#21546) (pandas-dev#21660)

Author: Liam3851

doc/source/whatsnew/v0.24.0.txt

@@ -255,7 +255,6 @@ Timezones
 - Bug in :class:`Series` constructor which would coerce tz-aware and tz-naive :class:`Timestamp`s to tz-aware (:issue:`13051`)
 - Bug in :class:`Index` with ``datetime64[ns, tz]`` dtype that did not localize integer data correctly (:issue:`20964`)
 - Bug in :class:`DatetimeIndex` where constructing with an integer and tz would not localize correctly (:issue:`12619`)
-- Bug in :func:`DataFrame.fillna` where a ``ValueError`` would raise when one column contained a ``datetime64[ns, tz]`` dtype (:issue:`15522`)
 
 Offsets
 ^^^^^^^
@@ -288,13 +287,18 @@ Indexing
 ^^^^^^^^
 
 - The traceback from a ``KeyError`` when asking ``.loc`` for a single missing label is now shorter and more clear (:issue:`21557`)
-- When ``.ix`` is asked for a missing integer label in a :class:`MultiIndex` with a first level of integer type, it now raises a ``KeyError`` - consistently with the case of a flat :class:`Int64Index` - rather than falling back to positional indexing (:issue:`21593`)
+- When ``.ix`` is asked for a missing integer label in a :class:`MultiIndex` with a first level of integer type, it now raises a ``KeyError``, consistently with the case of a flat :class:`Int64Index, rather than falling back to positional indexing (:issue:`21593`)
 - Bug in :meth:`DatetimeIndex.reindex` when reindexing a tz-naive and tz-aware :class:`DatetimeIndex` (:issue:`8306`)
 - Bug in :class:`DataFrame` when setting values with ``.loc`` and a timezone aware :class:`DatetimeIndex` (:issue:`11365`)
 - Bug when indexing :class:`DatetimeIndex` with nanosecond resolution dates and timezones (:issue:`11679`)
 
 -
 
+Missing
+^^^^^^^
+
+- Bug in :func:`DataFrame.fillna` where a ``ValueError`` would raise when one column contained a ``datetime64[ns, tz]`` dtype (:issue:`15522`)
+
 MultiIndex
 ^^^^^^^^^^
 
@@ -335,6 +339,8 @@ Reshaping
 ^^^^^^^^^
 
 - Bug in :func:`pandas.concat` when joining resampled DataFrames with timezone aware index (:issue:`13783`)
+- Bug in :meth:`Series.combine_first` with ``datetime64[ns, tz]`` dtype which would return tz-naive result (:issue:`21469`)
+- Bug in :meth:`Series.where` and :meth:`DataFrame.where` with ``datetime64[ns, tz]`` dtype (:issue:`21546`)
 -
 -
 

pandas/core/common.py

@@ -14,7 +14,7 @@
 from pandas.compat import long, zip, iteritems, PY36, OrderedDict
 from pandas.core.config import get_option
 from pandas.core.dtypes.generic import ABCSeries, ABCIndex
-from pandas.core.dtypes.common import _NS_DTYPE, is_integer
+from pandas.core.dtypes.common import is_integer
 from pandas.core.dtypes.inference import _iterable_not_string
 from pandas.core.dtypes.missing import isna, isnull, notnull  # noqa
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
@@ -410,19 +410,6 @@ def _apply_if_callable(maybe_callable, obj, **kwargs):
     return maybe_callable
 
 
-def _where_compat(mask, arr1, arr2):
-    if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
-        new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
-        return new_vals.view(_NS_DTYPE)
-
-    if arr1.dtype == _NS_DTYPE:
-        arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
-    if arr2.dtype == _NS_DTYPE:
-        arr2 = tslib.ints_to_pydatetime(arr2.view('i8'))
-
-    return np.where(mask, arr1, arr2)
-
-
 def _dict_compat(d):
     """
     Helper function to convert datetimelike-keyed dicts to Timestamp-keyed dict

pandas/core/internals.py

@@ -1476,14 +1476,16 @@ def where(self, other, cond, align=True, errors='raise',
         if transpose:
             values = values.T
 
-        other = getattr(other, 'values', other)
+        other = getattr(other, '_values', getattr(other, 'values', other))
         cond = getattr(cond, 'values', cond)
 
         # If the default broadcasting would go in the wrong direction, then
         # explicitly reshape other instead
         if getattr(other, 'ndim', 0) >= 1:
             if values.ndim - 1 == other.ndim and axis == 1:
                 other = other.reshape(tuple(other.shape + (1, )))
+            elif transpose and values.ndim == self.ndim - 1:
+                cond = cond.T
 
         if not hasattr(cond, 'shape'):
             raise ValueError("where must have a condition that is ndarray "
@@ -2888,8 +2890,8 @@ def _try_coerce_args(self, values, other):
         elif isinstance(other, self._holder):
             if other.tz != self.values.tz:
                 raise ValueError("incompatible or non tz-aware value")
-            other = other.asi8
-            other_mask = isna(other)
+            other_mask = _block_shape(isna(other), ndim=self.ndim)
+            other = _block_shape(other.asi8, ndim=self.ndim)
         elif isinstance(other, (np.datetime64, datetime, date)):
             other = tslib.Timestamp(other)
             tz = getattr(other, 'tz', None)

pandas/core/series.py

@@ -22,6 +22,7 @@
     is_float_dtype,
     is_extension_type,
     is_extension_array_dtype,
+    is_datetimelike,
     is_datetime64tz_dtype,
     is_timedelta64_dtype,
     is_object_dtype,
@@ -78,6 +79,7 @@
 from pandas._libs import index as libindex, tslib as libts, lib, iNaT
 from pandas.core.config import get_option
 from pandas.core.strings import StringMethods
+from pandas.core.tools.datetimes import to_datetime
 
 import pandas.plotting._core as gfx
 
@@ -2303,10 +2305,10 @@ def combine_first(self, other):
         new_index = self.index.union(other.index)
         this = self.reindex(new_index, copy=False)
         other = other.reindex(new_index, copy=False)
-        # TODO: do we need name?
-        name = ops.get_op_result_name(self, other)  # noqa
-        rs_vals = com._where_compat(isna(this), other._values, this._values)
-        return self._constructor(rs_vals, index=new_index).__finalize__(self)
+        if is_datetimelike(this) and not is_datetimelike(other):
+            other = to_datetime(other)
+
+        return this.where(notna(this), other)
 
     def update(self, other):
         """

pandas/tests/frame/test_indexing.py

@@ -2936,6 +2936,20 @@ def test_where_callable(self):
         tm.assert_frame_equal(result,
                               (df + 2).where((df + 2) > 8, (df + 2) + 10))
 
+    def test_where_tz_values(self, tz_naive_fixture):
+        df1 = DataFrame(DatetimeIndex(['20150101', '20150102', '20150103'],
+                                      tz=tz_naive_fixture),
+                        columns=['date'])
+        df2 = DataFrame(DatetimeIndex(['20150103', '20150104', '20150105'],
+                                      tz=tz_naive_fixture),
+                        columns=['date'])
+        mask = DataFrame([True, True, False], columns=['date'])
+        exp = DataFrame(DatetimeIndex(['20150101', '20150102', '20150105'],
+                                      tz=tz_naive_fixture),
+                        columns=['date'])
+        result = df1.where(mask, df2)
+        assert_frame_equal(exp, result)
+
     def test_mask(self):
         df = DataFrame(np.random.randn(5, 3))
         cond = df > 0

pandas/tests/indexing/test_coercion.py

@@ -580,12 +580,11 @@ def test_where_series_datetime64(self, fill_val, exp_dtype):
         values = pd.Series(pd.date_range(fill_val, periods=4))
         if fill_val.tz:
             exp = pd.Series([pd.Timestamp('2011-01-01'),
-                             pd.Timestamp('2012-01-02 05:00'),
+                             pd.Timestamp('2012-01-02 00:00', tz='US/Eastern'),
                              pd.Timestamp('2011-01-03'),
-                             pd.Timestamp('2012-01-04 05:00')])
-            self._assert_where_conversion(obj, cond, values, exp,
-                                          'datetime64[ns]')
-            pytest.xfail("ToDo: do not coerce to UTC, must be object")
+                             pd.Timestamp('2012-01-04 00:00',
+                                          tz='US/Eastern')])
+            self._assert_where_conversion(obj, cond, values, exp, exp_dtype)
 
         exp = pd.Series([pd.Timestamp('2011-01-01'), values[1],
                          pd.Timestamp('2011-01-03'), values[3]])

pandas/tests/series/indexing/test_boolean.py

@@ -551,6 +551,18 @@ def test_where_datetime_conversion():
     assert_series_equal(rs, expected)
 
 
+def test_where_dt_tz_values(tz_naive_fixture):
+    ser1 = pd.Series(pd.DatetimeIndex(['20150101', '20150102', '20150103'],
+                                      tz=tz_naive_fixture))
+    ser2 = pd.Series(pd.DatetimeIndex(['20160514', '20160515', '20160516'],
+                                      tz=tz_naive_fixture))
+    mask = pd.Series([True, True, False])
+    result = ser1.where(mask, ser2)
+    exp = pd.Series(pd.DatetimeIndex(['20150101', '20150102', '20160516'],
+                                     tz=tz_naive_fixture))
+    assert_series_equal(exp, result)
+
+
 def test_mask():
     # compare with tested results in test_where
     s = Series(np.random.randn(5))

pandas/tests/series/test_combine_concat.py

@@ -170,6 +170,20 @@ def get_result_type(dtype, dtype2):
                                     ]).dtype
                 assert result.kind == expected
 
+    def test_combine_first_dt_tz_values(self, tz_naive_fixture):
+        ser1 = pd.Series(pd.DatetimeIndex(['20150101', '20150102', '20150103'],
+                                          tz=tz_naive_fixture),
+                         name='ser1')
+        ser2 = pd.Series(pd.DatetimeIndex(['20160514', '20160515', '20160516'],
+                                          tz=tz_naive_fixture),
+                         index=[2, 3, 4], name='ser2')
+        result = ser1.combine_first(ser2)
+        exp_vals = pd.DatetimeIndex(['20150101', '20150102', '20150103',
+                                     '20160515', '20160516'],
+                                    tz=tz_naive_fixture)
+        exp = pd.Series(exp_vals, name='ser1')
+        assert_series_equal(exp, result)
+
     def test_concat_empty_series_dtypes(self):
 
         # booleans