test_datetimes.py

"""
Tests for DatetimeArray
"""
from datetime import timedelta
import operator

import numpy as np
import pytest

from pandas._libs.tslibs import tz_compare
from pandas._libs.tslibs.dtypes import (
    NpyDatetimeUnit,
    npy_unit_to_abbrev,
)

from pandas.core.dtypes.dtypes import DatetimeTZDtype

import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import (
    DatetimeArray,
    TimedeltaArray,
)


class TestNonNano:
    @pytest.fixture(params=["s", "ms", "us"])
    def unit(self, request):
        """Fixture returning parametrized time units"""
        return request.param

    @pytest.fixture
    def reso(self, unit):
        """Fixture returning datetime resolution for a given time unit"""
        return {
            "s": NpyDatetimeUnit.NPY_FR_s.value,
            "ms": NpyDatetimeUnit.NPY_FR_ms.value,
            "us": NpyDatetimeUnit.NPY_FR_us.value,
        }[unit]

    @pytest.fixture
    def dtype(self, unit, tz_naive_fixture):
        tz = tz_naive_fixture
        if tz is None:
            return np.dtype(f"datetime64[{unit}]")
        else:
            return DatetimeTZDtype(unit=unit, tz=tz)

    @pytest.fixture
    def dta_dti(self, unit, dtype):
        tz = getattr(dtype, "tz", None)

        dti = pd.date_range("2016-01-01", periods=55, freq="D", tz=tz)
        if tz is None:
            arr = np.asarray(dti).astype(f"M8[{unit}]")
        else:
            arr = np.asarray(dti.tz_convert("UTC").tz_localize(None)).astype(
                f"M8[{unit}]"
            )

        dta = DatetimeArray._simple_new(arr, dtype=dtype)
        return dta, dti

    @pytest.fixture
    def dta(self, dta_dti):
        dta, dti = dta_dti
        return dta

    def test_non_nano(self, unit, reso, dtype):
        arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]")
        dta = DatetimeArray._simple_new(arr, dtype=dtype)

        assert dta.dtype == dtype
        assert dta[0]._creso == reso
        assert tz_compare(dta.tz, dta[0].tz)
        assert (dta[0] == dta[:1]).all()

    @pytest.mark.parametrize(
        "field", DatetimeArray._field_ops + DatetimeArray._bool_ops
    )
    def test_fields(self, unit, reso, field, dtype, dta_dti):
        dta, dti = dta_dti

        assert (dti == dta).all()

        res = getattr(dta, field)
        expected = getattr(dti._data, field)
        tm.assert_numpy_array_equal(res, expected)

    def test_normalize(self, unit):
        dti = pd.date_range("2016-01-01 06:00:00", periods=55, freq="D")
        arr = np.asarray(dti).astype(f"M8[{unit}]")

        dta = DatetimeArray._simple_new(arr, dtype=arr.dtype)

        assert not dta.is_normalized

        # TODO: simplify once we can just .astype to other unit
        exp = np.asarray(dti.normalize()).astype(f"M8[{unit}]")
        expected = DatetimeArray._simple_new(exp, dtype=exp.dtype)

        res = dta.normalize()
        tm.assert_extension_array_equal(res, expected)

    def test_simple_new_requires_match(self, unit):
        arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]")
        dtype = DatetimeTZDtype(unit, "UTC")

        dta = DatetimeArray._simple_new(arr, dtype=dtype)
        assert dta.dtype == dtype

        wrong = DatetimeTZDtype("ns", "UTC")
        with pytest.raises(AssertionError, match=""):
            DatetimeArray._simple_new(arr, dtype=wrong)

    def test_std_non_nano(self, unit):
        dti = pd.date_range("2016-01-01", periods=55, freq="D")
        arr = np.asarray(dti).astype(f"M8[{unit}]")

        dta = DatetimeArray._simple_new(arr, dtype=arr.dtype)

        # we should match the nano-reso std, but floored to our reso.
        res = dta.std()
        assert res._creso == dta._creso
        assert res == dti.std().floor(unit)

    @pytest.mark.filterwarnings("ignore:Converting to PeriodArray.*:UserWarning")
    def test_to_period(self, dta_dti):
        dta, dti = dta_dti
        result = dta.to_period("D")
        expected = dti._data.to_period("D")

        tm.assert_extension_array_equal(result, expected)

    def test_iter(self, dta):
        res = next(iter(dta))
        expected = dta[0]

        assert type(res) is pd.Timestamp
        assert res.value == expected.value
        assert res._creso == expected._creso
        assert res == expected

    def test_astype_object(self, dta):
        result = dta.astype(object)
        assert all(x._creso == dta._creso for x in result)
        assert all(x == y for x, y in zip(result, dta))

    def test_to_pydatetime(self, dta_dti):
        dta, dti = dta_dti

        result = dta.to_pydatetime()
        expected = dti.to_pydatetime()
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("meth", ["time", "timetz", "date"])
    def test_time_date(self, dta_dti, meth):
        dta, dti = dta_dti

        result = getattr(dta, meth)
        expected = getattr(dti, meth)
        tm.assert_numpy_array_equal(result, expected)

    def test_format_native_types(self, unit, reso, dtype, dta_dti):
        # In this case we should get the same formatted values with our nano
        #  version dti._data as we do with the non-nano dta
        dta, dti = dta_dti

        res = dta._format_native_types()
        exp = dti._data._format_native_types()
        tm.assert_numpy_array_equal(res, exp)

    def test_repr(self, dta_dti, unit):
        dta, dti = dta_dti

        assert repr(dta) == repr(dti._data).replace("[ns", f"[{unit}")

    # TODO: tests with td64
    def test_compare_mismatched_resolutions(self, comparison_op):
        # comparison that numpy gets wrong bc of silent overflows
        op = comparison_op

        iinfo = np.iinfo(np.int64)
        vals = np.array([iinfo.min, iinfo.min + 1, iinfo.max], dtype=np.int64)

        # Construct so that arr2[1] < arr[1] < arr[2] < arr2[2]
        arr = np.array(vals).view("M8[ns]")
        arr2 = arr.view("M8[s]")

        left = DatetimeArray._simple_new(arr, dtype=arr.dtype)
        right = DatetimeArray._simple_new(arr2, dtype=arr2.dtype)

        if comparison_op is operator.eq:
            expected = np.array([False, False, False])
        elif comparison_op is operator.ne:
            expected = np.array([True, True, True])
        elif comparison_op in [operator.lt, operator.le]:
            expected = np.array([False, False, True])
        else:
            expected = np.array([False, True, False])

        result = op(left, right)
        tm.assert_numpy_array_equal(result, expected)

        result = op(left[1], right)
        tm.assert_numpy_array_equal(result, expected)

        if op not in [operator.eq, operator.ne]:
            # check that numpy still gets this wrong; if it is fixed we may be
            #  able to remove compare_mismatched_resolutions
            np_res = op(left._ndarray, right._ndarray)
            tm.assert_numpy_array_equal(np_res[1:], ~expected[1:])

    def test_add_mismatched_reso_doesnt_downcast(self):
        # https://github.com/pandas-dev/pandas/pull/48748#issuecomment-1260181008
        td = pd.Timedelta(microseconds=1)
        dti = pd.date_range("2016-01-01", periods=3) - td
        dta = dti._data._as_unit("us")

        res = dta + td._as_unit("us")
        # even though the result is an even number of days
        #  (so we _could_ downcast to unit="s"), we do not.
        assert res._unit == "us"

    @pytest.mark.parametrize(
        "scalar",
        [
            timedelta(hours=2),
            pd.Timedelta(hours=2),
            np.timedelta64(2, "h"),
            np.timedelta64(2 * 3600 * 1000, "ms"),
            pd.offsets.Minute(120),
            pd.offsets.Hour(2),
        ],
    )
    def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar):
        dta, dti = dta_dti

        td = pd.Timedelta(scalar)
        exp_reso = max(dta._creso, td._creso)
        exp_unit = npy_unit_to_abbrev(exp_reso)

        expected = (dti + td)._data._as_unit(exp_unit)
        result = dta + scalar
        tm.assert_extension_array_equal(result, expected)

        result = scalar + dta
        tm.assert_extension_array_equal(result, expected)

        expected = (dti - td)._data._as_unit(exp_unit)
        result = dta - scalar
        tm.assert_extension_array_equal(result, expected)

    def test_sub_datetimelike_scalar_mismatch(self):
        dti = pd.date_range("2016-01-01", periods=3)
        dta = dti._data._as_unit("us")

        ts = dta[0]._as_unit("s")

        result = dta - ts
        expected = (dti - dti[0])._data._as_unit("us")
        assert result.dtype == "m8[us]"
        tm.assert_extension_array_equal(result, expected)

    def test_sub_datetime64_reso_mismatch(self):
        dti = pd.date_range("2016-01-01", periods=3)
        left = dti._data._as_unit("s")
        right = left._as_unit("ms")

        result = left - right
        exp_values = np.array([0, 0, 0], dtype="m8[ms]")
        expected = TimedeltaArray._simple_new(
            exp_values,
            dtype=exp_values.dtype,
        )
        tm.assert_extension_array_equal(result, expected)
        result2 = right - left
        tm.assert_extension_array_equal(result2, expected)


class TestDatetimeArrayComparisons:
    # TODO: merge this into tests/arithmetic/test_datetime64 once it is
    #  sufficiently robust

    def test_cmp_dt64_arraylike_tznaive(self, comparison_op):
        # arbitrary tz-naive DatetimeIndex
        op = comparison_op

        dti = pd.date_range("2016-01-1", freq="MS", periods=9, tz=None)
        arr = DatetimeArray(dti)
        assert arr.freq == dti.freq
        assert arr.tz == dti.tz

        right = dti

        expected = np.ones(len(arr), dtype=bool)
        if comparison_op.__name__ in ["ne", "gt", "lt"]:
            # for these the comparisons should be all-False
            expected = ~expected

        result = op(arr, arr)
        tm.assert_numpy_array_equal(result, expected)
        for other in [
            right,
            np.array(right),
            list(right),
            tuple(right),
            right.astype(object),
        ]:
            result = op(arr, other)
            tm.assert_numpy_array_equal(result, expected)

            result = op(other, arr)
            tm.assert_numpy_array_equal(result, expected)


class TestDatetimeArray:
    def test_astype_non_nano_tznaive(self):
        dti = pd.date_range("2016-01-01", periods=3)

        res = dti.astype("M8[s]")
        assert res.dtype == "M8[s]"

        dta = dti._data
        res = dta.astype("M8[s]")
        assert res.dtype == "M8[s]"
        assert isinstance(res, pd.core.arrays.DatetimeArray)  # used to be ndarray

    def test_astype_non_nano_tzaware(self):
        dti = pd.date_range("2016-01-01", periods=3, tz="UTC")

        res = dti.astype("M8[s, US/Pacific]")
        assert res.dtype == "M8[s, US/Pacific]"

        dta = dti._data
        res = dta.astype("M8[s, US/Pacific]")
        assert res.dtype == "M8[s, US/Pacific]"

        # from non-nano to non-nano, preserving reso
        res2 = res.astype("M8[s, UTC]")
        assert res2.dtype == "M8[s, UTC]"
        assert not tm.shares_memory(res2, res)

        res3 = res.astype("M8[s, UTC]", copy=False)
        assert res2.dtype == "M8[s, UTC]"
        assert tm.shares_memory(res3, res)

    def test_astype_to_same(self):
        arr = DatetimeArray._from_sequence(
            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
        )
        result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
        assert result is arr

    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "datetime64[ns, UTC]"])
    @pytest.mark.parametrize(
        "other", ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, CET]"]
    )
    def test_astype_copies(self, dtype, other):
        # https://github.com/pandas-dev/pandas/pull/32490
        ser = pd.Series([1, 2], dtype=dtype)
        orig = ser.copy()

        err = False
        if (dtype == "datetime64[ns]") ^ (other == "datetime64[ns]"):
            # deprecated in favor of tz_localize
            err = True

        if err:
            if dtype == "datetime64[ns]":
                msg = "Use ser.dt.tz_localize instead"
            else:
                msg = "from timezone-aware dtype to timezone-naive dtype"
            with pytest.raises(TypeError, match=msg):
                ser.astype(other)
        else:
            t = ser.astype(other)
            t[:] = pd.NaT
            tm.assert_series_equal(ser, orig)

    @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
    def test_astype_int(self, dtype):
        arr = DatetimeArray._from_sequence([pd.Timestamp("2000"), pd.Timestamp("2001")])

        if np.dtype(dtype).kind == "u":
            expected_dtype = np.dtype("uint64")
        else:
            expected_dtype = np.dtype("int64")
        expected = arr.astype(expected_dtype)

        warn = None
        if dtype != expected_dtype:
            warn = FutureWarning
        msg = " will return exactly the specified dtype"
        with tm.assert_produces_warning(warn, match=msg):
            result = arr.astype(dtype)

        assert result.dtype == expected_dtype
        tm.assert_numpy_array_equal(result, expected)

    def test_tz_setter_raises(self):
        arr = DatetimeArray._from_sequence(
            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
        )
        with pytest.raises(AttributeError, match="tz_localize"):
            arr.tz = "UTC"

    def test_setitem_str_impute_tz(self, tz_naive_fixture):
        # Like for getitem, if we are passed a naive-like string, we impute
        #  our own timezone.
        tz = tz_naive_fixture

        data = np.array([1, 2, 3], dtype="M8[ns]")
        dtype = data.dtype if tz is None else DatetimeTZDtype(tz=tz)
        arr = DatetimeArray(data, dtype=dtype)
        expected = arr.copy()

        ts = pd.Timestamp("2020-09-08 16:50").tz_localize(tz)
        setter = str(ts.tz_localize(None))

        # Setting a scalar tznaive string
        expected[0] = ts
        arr[0] = setter
        tm.assert_equal(arr, expected)

        # Setting a listlike of tznaive strings
        expected[1] = ts
        arr[:2] = [setter, setter]
        tm.assert_equal(arr, expected)

    def test_setitem_different_tz_raises(self):
        # pre-2.0 we required exact tz match, in 2.0 we require only
        #  tzawareness-match
        data = np.array([1, 2, 3], dtype="M8[ns]")
        arr = DatetimeArray(data, copy=False, dtype=DatetimeTZDtype(tz="US/Central"))
        with pytest.raises(TypeError, match="Cannot compare tz-naive and tz-aware"):
            arr[0] = pd.Timestamp("2000")

        ts = pd.Timestamp("2000", tz="US/Eastern")
        arr[0] = ts
        assert arr[0] == ts.tz_convert("US/Central")

    def test_setitem_clears_freq(self):
        a = DatetimeArray(pd.date_range("2000", periods=2, freq="D", tz="US/Central"))
        a[0] = pd.Timestamp("2000", tz="US/Central")
        assert a.freq is None

    @pytest.mark.parametrize(
        "obj",
        [
            pd.Timestamp("2021-01-01"),
            pd.Timestamp("2021-01-01").to_datetime64(),
            pd.Timestamp("2021-01-01").to_pydatetime(),
        ],
    )
    def test_setitem_objects(self, obj):
        # make sure we accept datetime64 and datetime in addition to Timestamp
        dti = pd.date_range("2000", periods=2, freq="D")
        arr = dti._data

        arr[0] = obj
        assert arr[0] == obj

    def test_repeat_preserves_tz(self):
        dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
        arr = DatetimeArray(dti)

        repeated = arr.repeat([1, 1])

        # preserves tz and values, but not freq
        expected = DatetimeArray(arr.asi8, freq=None, dtype=arr.dtype)
        tm.assert_equal(repeated, expected)

    def test_value_counts_preserves_tz(self):
        dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
        arr = DatetimeArray(dti).repeat([4, 3])

        result = arr.value_counts()

        # Note: not tm.assert_index_equal, since `freq`s do not match
        assert result.index.equals(dti)

        arr[-2] = pd.NaT
        result = arr.value_counts(dropna=False)
        expected = pd.Series([4, 2, 1], index=[dti[0], dti[1], pd.NaT])
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("method", ["pad", "backfill"])
    def test_fillna_preserves_tz(self, method):
        dti = pd.date_range("2000-01-01", periods=5, freq="D", tz="US/Central")
        arr = DatetimeArray(dti, copy=True)
        arr[2] = pd.NaT

        fill_val = dti[1] if method == "pad" else dti[3]
        expected = DatetimeArray._from_sequence(
            [dti[0], dti[1], fill_val, dti[3], dti[4]],
            dtype=DatetimeTZDtype(tz="US/Central"),
        )

        result = arr.fillna(method=method)
        tm.assert_extension_array_equal(result, expected)

        # assert that arr and dti were not modified in-place
        assert arr[2] is pd.NaT
        assert dti[2] == pd.Timestamp("2000-01-03", tz="US/Central")

    def test_fillna_2d(self):
        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
        dta = dti._data.reshape(3, 2).copy()
        dta[0, 1] = pd.NaT
        dta[1, 0] = pd.NaT

        res1 = dta.fillna(method="pad")
        expected1 = dta.copy()
        expected1[1, 0] = dta[0, 0]
        tm.assert_extension_array_equal(res1, expected1)

        res2 = dta.fillna(method="backfill")
        expected2 = dta.copy()
        expected2 = dta.copy()
        expected2[1, 0] = dta[2, 0]
        expected2[0, 1] = dta[1, 1]
        tm.assert_extension_array_equal(res2, expected2)

        # with different ordering for underlying ndarray; behavior should
        #  be unchanged
        dta2 = dta._from_backing_data(dta._ndarray.copy(order="F"))
        assert dta2._ndarray.flags["F_CONTIGUOUS"]
        assert not dta2._ndarray.flags["C_CONTIGUOUS"]
        tm.assert_extension_array_equal(dta, dta2)

        res3 = dta2.fillna(method="pad")
        tm.assert_extension_array_equal(res3, expected1)

        res4 = dta2.fillna(method="backfill")
        tm.assert_extension_array_equal(res4, expected2)

        # test the DataFrame method while we're here
        df = pd.DataFrame(dta)
        res = df.fillna(method="pad")
        expected = pd.DataFrame(expected1)
        tm.assert_frame_equal(res, expected)

        res = df.fillna(method="backfill")
        expected = pd.DataFrame(expected2)
        tm.assert_frame_equal(res, expected)

    def test_array_interface_tz(self):
        tz = "US/Central"
        data = DatetimeArray(pd.date_range("2017", periods=2, tz=tz))
        result = np.asarray(data)

        expected = np.array(
            [
                pd.Timestamp("2017-01-01T00:00:00", tz=tz),
                pd.Timestamp("2017-01-02T00:00:00", tz=tz),
            ],
            dtype=object,
        )
        tm.assert_numpy_array_equal(result, expected)

        result = np.asarray(data, dtype=object)
        tm.assert_numpy_array_equal(result, expected)

        result = np.asarray(data, dtype="M8[ns]")

        expected = np.array(
            ["2017-01-01T06:00:00", "2017-01-02T06:00:00"], dtype="M8[ns]"
        )
        tm.assert_numpy_array_equal(result, expected)

    def test_array_interface(self):
        data = DatetimeArray(pd.date_range("2017", periods=2))
        expected = np.array(
            ["2017-01-01T00:00:00", "2017-01-02T00:00:00"], dtype="datetime64[ns]"
        )

        result = np.asarray(data)
        tm.assert_numpy_array_equal(result, expected)

        result = np.asarray(data, dtype=object)
        expected = np.array(
            [pd.Timestamp("2017-01-01T00:00:00"), pd.Timestamp("2017-01-02T00:00:00")],
            dtype=object,
        )
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("index", [True, False])
    def test_searchsorted_different_tz(self, index):
        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
        arr = DatetimeArray(data, freq="D").tz_localize("Asia/Tokyo")
        if index:
            arr = pd.Index(arr)

        expected = arr.searchsorted(arr[2])
        result = arr.searchsorted(arr[2].tz_convert("UTC"))
        assert result == expected

        expected = arr.searchsorted(arr[2:6])
        result = arr.searchsorted(arr[2:6].tz_convert("UTC"))
        tm.assert_equal(result, expected)

    @pytest.mark.parametrize("index", [True, False])
    def test_searchsorted_tzawareness_compat(self, index):
        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
        arr = DatetimeArray(data, freq="D")
        if index:
            arr = pd.Index(arr)

        mismatch = arr.tz_localize("Asia/Tokyo")

        msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
        with pytest.raises(TypeError, match=msg):
            arr.searchsorted(mismatch[0])
        with pytest.raises(TypeError, match=msg):
            arr.searchsorted(mismatch)

        with pytest.raises(TypeError, match=msg):
            mismatch.searchsorted(arr[0])
        with pytest.raises(TypeError, match=msg):
            mismatch.searchsorted(arr)

    @pytest.mark.parametrize(
        "other",
        [
            1,
            np.int64(1),
            1.0,
            np.timedelta64("NaT"),
            pd.Timedelta(days=2),
            "invalid",
            np.arange(10, dtype="i8") * 24 * 3600 * 10**9,
            np.arange(10).view("timedelta64[ns]") * 24 * 3600 * 10**9,
            pd.Timestamp("2021-01-01").to_period("D"),
        ],
    )
    @pytest.mark.parametrize("index", [True, False])
    def test_searchsorted_invalid_types(self, other, index):
        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
        arr = DatetimeArray(data, freq="D")
        if index:
            arr = pd.Index(arr)

        msg = "|".join(
            [
                "searchsorted requires compatible dtype or scalar",
                "value should be a 'Timestamp', 'NaT', or array of those. Got",
            ]
        )
        with pytest.raises(TypeError, match=msg):
            arr.searchsorted(other)

    def test_shift_fill_value(self):
        dti = pd.date_range("2016-01-01", periods=3)

        dta = dti._data
        expected = DatetimeArray(np.roll(dta._data, 1))

        fv = dta[-1]
        for fill_value in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
            result = dta.shift(1, fill_value=fill_value)
            tm.assert_datetime_array_equal(result, expected)

        dta = dta.tz_localize("UTC")
        expected = expected.tz_localize("UTC")
        fv = dta[-1]
        for fill_value in [fv, fv.to_pydatetime()]:
            result = dta.shift(1, fill_value=fill_value)
            tm.assert_datetime_array_equal(result, expected)

    def test_shift_value_tzawareness_mismatch(self):
        dti = pd.date_range("2016-01-01", periods=3)

        dta = dti._data

        fv = dta[-1].tz_localize("UTC")
        for invalid in [fv, fv.to_pydatetime()]:
            with pytest.raises(TypeError, match="Cannot compare"):
                dta.shift(1, fill_value=invalid)

        dta = dta.tz_localize("UTC")
        fv = dta[-1].tz_localize(None)
        for invalid in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
            with pytest.raises(TypeError, match="Cannot compare"):
                dta.shift(1, fill_value=invalid)

    def test_shift_requires_tzmatch(self):
        # pre-2.0 we required exact tz match, in 2.0 we require just
        #  matching tzawareness
        dti = pd.date_range("2016-01-01", periods=3, tz="UTC")
        dta = dti._data

        fill_value = pd.Timestamp("2020-10-18 18:44", tz="US/Pacific")

        result = dta.shift(1, fill_value=fill_value)
        expected = dta.shift(1, fill_value=fill_value.tz_convert("UTC"))
        tm.assert_equal(result, expected)

    def test_tz_localize_t2d(self):
        dti = pd.date_range("1994-05-12", periods=12, tz="US/Pacific")
        dta = dti._data.reshape(3, 4)
        result = dta.tz_localize(None)

        expected = dta.ravel().tz_localize(None).reshape(dta.shape)
        tm.assert_datetime_array_equal(result, expected)

        roundtrip = expected.tz_localize("US/Pacific")
        tm.assert_datetime_array_equal(roundtrip, dta)