BUG: Series.mode with dt64tz or PeriodDtype

doc/source/whatsnew/v1.4.0.rst

@@ -547,6 +547,7 @@ Datetimelike
 - Bug in addition with a :class:`Tick` object and a ``np.timedelta64`` object incorrectly raising instead of returning :class:`Timedelta` (:issue:`44474`)
 - Bug in adding a ``np.timedelta64`` object to a :class:`BusinessDay` or :class:`CustomBusinessDay` object incorrectly raising (:issue:`44532`)
 - Bug in :meth:`Index.insert` for inserting ``np.datetime64``, ``np.timedelta64`` or ``tuple`` into :class:`Index` with ``dtype='object'`` with negative loc addine ``None`` and replacing existing value (:issue:`44509`)
+- Bug in :meth:`Series.mode` with ``DatetimeTZDtype`` incorrectly returning timezone-naive and ``PeriodDtype`` incorrectly raising (:issue:`41927`)
 -
 
 Timedelta

pandas/core/algorithms.py

@@ -215,7 +215,7 @@ def _reconstruct_data(
         if isinstance(values, cls) and values.dtype == dtype:
             return values
 
-        values = cls._from_sequence(values)
+        values = cls._from_sequence(values, dtype=dtype)
     elif is_bool_dtype(dtype):
         values = values.astype(dtype, copy=False)
 
@@ -960,15 +960,18 @@ def mode(values, dropna: bool = True) -> Series:
     original = values
 
     # categorical is a fast-path
-    if is_categorical_dtype(values):
+    if is_categorical_dtype(values.dtype):
         if isinstance(values, Series):
             # TODO: should we be passing `name` below?
             return Series(values._values.mode(dropna=dropna), name=values.name)
         return values.mode(dropna=dropna)
 
-    if dropna and needs_i8_conversion(values.dtype):
-        mask = values.isnull()
-        values = values[~mask]
+    if needs_i8_conversion(values.dtype):
+        if dropna:
+            mask = values.isna()
+            values = values[~mask]
+        modes = mode(values.view("i8"))
+        return modes.view(original.dtype)
 
     values = _ensure_data(values)
 

pandas/core/arrays/_mixins.py

@@ -16,6 +16,8 @@
 from pandas._libs import lib
 from pandas._libs.arrays import NDArrayBacked
 from pandas._typing import (
+    ArrayLike,
+    Dtype,
     F,
     PositionalIndexer2D,
     PositionalIndexerTuple,
@@ -34,8 +36,15 @@
     validate_insert_loc,
 )
 
-from pandas.core.dtypes.common import is_dtype_equal
-from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.common import (
+    is_dtype_equal,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
 from pandas.core.dtypes.missing import array_equivalent
 
 from pandas.core import missing
@@ -101,6 +110,41 @@ def _validate_scalar(self, value):
 
     # ------------------------------------------------------------------------
 
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        # We handle datetime64, datetime64tz, timedelta64, and period
+        #  dtypes here. Everything else we pass through to the underlying
+        #  ndarray.
+        if dtype is None or dtype is self.dtype:
+            return self._from_backing_data(self._ndarray)
+
+        if isinstance(dtype, type):
+            # we sometimes pass non-dtype objects, e.g np.ndarray;
+            #  pass those through to the underlying ndarray
+            return self._ndarray.view(dtype)
+
+        dtype = pandas_dtype(dtype)
+        arr = self._ndarray
+
+        if isinstance(dtype, (PeriodDtype, DatetimeTZDtype)):
+            cls = dtype.construct_array_type()
+            return cls(arr.view("i8"), dtype=dtype)
+        elif dtype == "M8[ns]":
+            from pandas.core.arrays import DatetimeArray
+
+            return DatetimeArray(arr.view("i8"), dtype=dtype)
+        elif dtype == "m8[ns]":
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray(arr.view("i8"), dtype=dtype)
+
+        # error: Incompatible return value type (got "ndarray", expected
+        # "ExtensionArray")
+        # error: Argument "dtype" to "view" of "_ArrayOrScalarCommon" has incompatible
+        # type "Union[ExtensionDtype, dtype[Any]]"; expected "Union[dtype[Any], None,
+        # type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any, Union[int,
+        # Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
+        return arr.view(dtype=dtype)  # type: ignore[return-value,arg-type]
+
     def take(
         self: NDArrayBackedExtensionArrayT,
         indices: TakeIndexer,

pandas/core/arrays/datetimelike.py

@@ -85,11 +85,7 @@
     is_unsigned_integer_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.dtypes import (
-    DatetimeTZDtype,
-    ExtensionDtype,
-    PeriodDtype,
-)
+from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.missing import (
     is_valid_na_for_dtype,
     isna,
@@ -461,36 +457,7 @@ def view(self, dtype: Dtype | None = ...) -> ArrayLike:
         ...
 
     def view(self, dtype: Dtype | None = None) -> ArrayLike:
-        # We handle datetime64, datetime64tz, timedelta64, and period
-        #  dtypes here. Everything else we pass through to the underlying
-        #  ndarray.
-        if dtype is None or dtype is self.dtype:
-            return type(self)(self._ndarray, dtype=self.dtype)
-
-        if isinstance(dtype, type):
-            # we sometimes pass non-dtype objects, e.g np.ndarray;
-            #  pass those through to the underlying ndarray
-            return self._ndarray.view(dtype)
-
-        dtype = pandas_dtype(dtype)
-        if isinstance(dtype, (PeriodDtype, DatetimeTZDtype)):
-            cls = dtype.construct_array_type()
-            return cls(self.asi8, dtype=dtype)
-        elif dtype == "M8[ns]":
-            from pandas.core.arrays import DatetimeArray
-
-            return DatetimeArray(self.asi8, dtype=dtype)
-        elif dtype == "m8[ns]":
-            from pandas.core.arrays import TimedeltaArray
-
-            return TimedeltaArray(self.asi8, dtype=dtype)
-        # error: Incompatible return value type (got "ndarray", expected
-        # "ExtensionArray")
-        # error: Argument "dtype" to "view" of "_ArrayOrScalarCommon" has incompatible
-        # type "Union[ExtensionDtype, dtype[Any]]"; expected "Union[dtype[Any], None,
-        # type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any, Union[int,
-        # Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
-        return self._ndarray.view(dtype=dtype)  # type: ignore[return-value,arg-type]
+        return super().view(dtype)
 
     # ------------------------------------------------------------------
     # ExtensionArray Interface

pandas/core/indexes/base.py

@@ -947,7 +947,6 @@ def view(self, cls=None):
                     # e.g. m8[s]
                     return self._data.view(cls)
 
-                arr = self._data.view("i8")
                 idx_cls = self._dtype_to_subclass(dtype)
                 arr_cls = idx_cls._data_cls
                 arr = arr_cls(self._data.view("i8"), dtype=dtype)

pandas/core/series.py

@@ -802,9 +802,11 @@ def view(self, dtype: Dtype | None = None) -> Series:
         4      2
         dtype: int8
         """
-        return self._constructor(
-            self._values.view(dtype), index=self.index
-        ).__finalize__(self, method="view")
+        # self.array instead of self._values so we piggyback on PandasArray
+        #  implementation
+        res_values = self.array.view(dtype)
+        res_ser = self._constructor(res_values, index=self.index)
+        return res_ser.__finalize__(self, method="view")
 
     # ----------------------------------------------------------------------
     # NDArray Compat
@@ -1975,7 +1977,7 @@ def count(self, level=None):
             self, method="count"
         )
 
-    def mode(self, dropna=True) -> Series:
+    def mode(self, dropna: bool = True) -> Series:
         """
         Return the mode(s) of the Series.
 

pandas/tests/series/methods/test_view.py

@@ -11,6 +11,18 @@
 
 
 class TestView:
+    def test_view_i8_to_datetimelike(self):
+        dti = date_range("2000", periods=4, tz="US/Central")
+        ser = Series(dti.asi8)
+
+        result = ser.view(dti.dtype)
+        tm.assert_datetime_array_equal(result._values, dti._data._with_freq(None))
+
+        pi = dti.tz_localize(None).to_period("D")
+        ser = Series(pi.asi8)
+        result = ser.view(pi.dtype)
+        tm.assert_period_array_equal(result._values, pi._data)
+
     def test_view_tz(self):
         # GH#24024
         ser = Series(date_range("2000", periods=4, tz="US/Central"))

pandas/tests/series/test_reductions.py

@@ -9,6 +9,27 @@
 import pandas._testing as tm
 
 
+@pytest.mark.parametrize("as_period", [True, False])
+def test_mode_extension_dtype(as_period):
+    # GH#41927 preserve dt64tz dtype
+    ser = Series([pd.Timestamp(1979, 4, n) for n in range(1, 5)])
+
+    if as_period:
+        ser = ser.dt.to_period("D")
+    else:
+        ser = ser.dt.tz_localize("US/Central")
+
+    res = ser.mode()
+    assert res.dtype == ser.dtype
+    tm.assert_series_equal(res, ser)
+
+    res = pd.core.algorithms.mode(ser._values)
+    tm.assert_series_equal(res, ser)
+
+    res = pd.core.algorithms.mode(pd.Index(ser))
+    tm.assert_series_equal(res, ser)
+
+
 def test_reductions_td64_with_nat():
     # GH#8617
     ser = Series([0, pd.NaT], dtype="m8[ns]")