""" test to_datetime """

import calendar
from collections import deque
from datetime import datetime, timedelta
import locale

from dateutil.parser import parse
from dateutil.tz.tz import tzoffset
import numpy as np
import pytest
import pytz

from pandas._libs import tslib
from pandas._libs.tslibs import iNaT, parsing
from pandas.errors import OutOfBoundsDatetime
import pandas.util._test_decorators as td

from pandas.core.dtypes.common import is_datetime64_ns_dtype

import pandas as pd
from pandas import (
    DataFrame,
    DatetimeIndex,
    Index,
    NaT,
    Series,
    Timestamp,
    date_range,
    isna,
    to_datetime,
)
import pandas._testing as tm
from pandas.core.arrays import DatetimeArray
from pandas.core.tools import datetimes as tools


class TestTimeConversionFormats:
    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_format(self, cache):
        values = ["1/1/2000", "1/2/2000", "1/3/2000"]

        results1 = [Timestamp("20000101"), Timestamp("20000201"), Timestamp("20000301")]
        results2 = [Timestamp("20000101"), Timestamp("20000102"), Timestamp("20000103")]
        for vals, expecteds in [
            (values, (Index(results1), Index(results2))),
            (Series(values), (Series(results1), Series(results2))),
            (values[0], (results1[0], results2[0])),
            (values[1], (results1[1], results2[1])),
            (values[2], (results1[2], results2[2])),
        ]:

            for i, fmt in enumerate(["%d/%m/%Y", "%m/%d/%Y"]):
                result = to_datetime(vals, format=fmt, cache=cache)
                expected = expecteds[i]

                if isinstance(expected, Series):
                    tm.assert_series_equal(result, Series(expected))
                elif isinstance(expected, Timestamp):
                    assert result == expected
                else:
                    tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_format_YYYYMMDD(self, cache):
        s = Series([19801222, 19801222] + [19810105] * 5)
        expected = Series([Timestamp(x) for x in s.apply(str)])

        result = to_datetime(s, format="%Y%m%d", cache=cache)
        tm.assert_series_equal(result, expected)

        result = to_datetime(s.apply(str), format="%Y%m%d", cache=cache)
        tm.assert_series_equal(result, expected)

        # with NaT
        expected = Series(
            [Timestamp("19801222"), Timestamp("19801222")] + [Timestamp("19810105")] * 5
        )
        expected[2] = np.nan
        s[2] = np.nan

        result = to_datetime(s, format="%Y%m%d", cache=cache)
        tm.assert_series_equal(result, expected)

        # string with NaT
        s = s.apply(str)
        s[2] = "nat"
        result = to_datetime(s, format="%Y%m%d", cache=cache)
        tm.assert_series_equal(result, expected)

        # coercion
        # GH 7930
        s = Series([20121231, 20141231, 99991231])
        result = pd.to_datetime(s, format="%Y%m%d", errors="ignore", cache=cache)
        expected = Series(
            [datetime(2012, 12, 31), datetime(2014, 12, 31), datetime(9999, 12, 31)],
            dtype=object,
        )
        tm.assert_series_equal(result, expected)

        result = pd.to_datetime(s, format="%Y%m%d", errors="coerce", cache=cache)
        expected = Series(["20121231", "20141231", "NaT"], dtype="M8[ns]")
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "input_s",
        [
            # Null values with Strings
            ["19801222", "20010112", None],
            ["19801222", "20010112", np.nan],
            ["19801222", "20010112", pd.NaT],
            ["19801222", "20010112", "NaT"],
            # Null values with Integers
            [19801222, 20010112, None],
            [19801222, 20010112, np.nan],
            [19801222, 20010112, pd.NaT],
            [19801222, 20010112, "NaT"],
        ],
    )
    def test_to_datetime_format_YYYYMMDD_with_none(self, input_s):
        # GH 30011
        # format='%Y%m%d'
        # with None
        expected = Series([Timestamp("19801222"), Timestamp("20010112"), pd.NaT])
        result = Series(pd.to_datetime(input_s, format="%Y%m%d"))
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "input_s, expected",
        [
            # NaN before strings with invalid date values
            [
                Series(["19801222", np.nan, "20010012", "10019999"]),
                Series([Timestamp("19801222"), np.nan, np.nan, np.nan]),
            ],
            # NaN after strings with invalid date values
            [
                Series(["19801222", "20010012", "10019999", np.nan]),
                Series([Timestamp("19801222"), np.nan, np.nan, np.nan]),
            ],
            # NaN before integers with invalid date values
            [
                Series([20190813, np.nan, 20010012, 20019999]),
                Series([Timestamp("20190813"), np.nan, np.nan, np.nan]),
            ],
            # NaN after integers with invalid date values
            [
                Series([20190813, 20010012, np.nan, 20019999]),
                Series([Timestamp("20190813"), np.nan, np.nan, np.nan]),
            ],
        ],
    )
    def test_to_datetime_format_YYYYMMDD_overflow(self, input_s, expected):
        # GH 25512
        # format='%Y%m%d', errors='coerce'
        result = pd.to_datetime(input_s, format="%Y%m%d", errors="coerce")
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_format_integer(self, cache):
        # GH 10178
        s = Series([2000, 2001, 2002])
        expected = Series([Timestamp(x) for x in s.apply(str)])

        result = to_datetime(s, format="%Y", cache=cache)
        tm.assert_series_equal(result, expected)

        s = Series([200001, 200105, 200206])
        expected = Series([Timestamp(x[:4] + "-" + x[4:]) for x in s.apply(str)])

        result = to_datetime(s, format="%Y%m", cache=cache)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "int_date, expected",
        [
            # valid date, length == 8
            [20121030, datetime(2012, 10, 30)],
            # short valid date, length == 6
            [199934, datetime(1999, 3, 4)],
            # long integer date partially parsed to datetime(2012,1,1), length > 8
            [2012010101, 2012010101],
            # invalid date partially parsed to datetime(2012,9,9), length == 8
            [20129930, 20129930],
            # short integer date partially parsed to datetime(2012,9,9), length < 8
            [2012993, 2012993],
            # short invalid date, length == 4
            [2121, 2121],
        ],
    )
    def test_int_to_datetime_format_YYYYMMDD_typeerror(self, int_date, expected):
        # GH 26583
        result = to_datetime(int_date, format="%Y%m%d", errors="ignore")
        assert result == expected

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_format_microsecond(self, cache):

        # these are locale dependent
        lang, _ = locale.getlocale()
        month_abbr = calendar.month_abbr[4]
        val = f"01-{month_abbr}-2011 00:00:01.978"

        format = "%d-%b-%Y %H:%M:%S.%f"
        result = to_datetime(val, format=format, cache=cache)
        exp = datetime.strptime(val, format)
        assert result == exp

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_format_time(self, cache):
        data = [
            ["01/10/2010 15:20", "%m/%d/%Y %H:%M", Timestamp("2010-01-10 15:20")],
            ["01/10/2010 05:43", "%m/%d/%Y %I:%M", Timestamp("2010-01-10 05:43")],
            [
                "01/10/2010 13:56:01",
                "%m/%d/%Y %H:%M:%S",
                Timestamp("2010-01-10 13:56:01"),
            ]  # ,
            # ['01/10/2010 08:14 PM', '%m/%d/%Y %I:%M %p',
            #  Timestamp('2010-01-10 20:14')],
            # ['01/10/2010 07:40 AM', '%m/%d/%Y %I:%M %p',
            #  Timestamp('2010-01-10 07:40')],
            # ['01/10/2010 09:12:56 AM', '%m/%d/%Y %I:%M:%S %p',
            #  Timestamp('2010-01-10 09:12:56')]
        ]
        for s, format, dt in data:
            assert to_datetime(s, format=format, cache=cache) == dt

    @td.skip_if_has_locale
    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_with_non_exact(self, cache):
        # GH 10834
        # 8904
        # exact kw
        s = Series(
            ["19MAY11", "foobar19MAY11", "19MAY11:00:00:00", "19MAY11 00:00:00Z"]
        )
        result = to_datetime(s, format="%d%b%y", exact=False, cache=cache)
        expected = to_datetime(
            s.str.extract(r"(\d+\w+\d+)", expand=False), format="%d%b%y", cache=cache
        )
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_parse_nanoseconds_with_formula(self, cache):

        # GH8989
        # truncating the nanoseconds when a format was provided
        for v in [
            "2012-01-01 09:00:00.000000001",
            "2012-01-01 09:00:00.000001",
            "2012-01-01 09:00:00.001",
            "2012-01-01 09:00:00.001000",
            "2012-01-01 09:00:00.001000000",
        ]:
            expected = pd.to_datetime(v, cache=cache)
            result = pd.to_datetime(v, format="%Y-%m-%d %H:%M:%S.%f", cache=cache)
            assert result == expected

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_format_weeks(self, cache):
        data = [
            ["2009324", "%Y%W%w", Timestamp("2009-08-13")],
            ["2013020", "%Y%U%w", Timestamp("2013-01-13")],
        ]
        for s, format, dt in data:
            assert to_datetime(s, format=format, cache=cache) == dt

    @pytest.mark.parametrize(
        "fmt,dates,expected_dates",
        [
            [
                "%Y-%m-%d %H:%M:%S %Z",
                ["2010-01-01 12:00:00 UTC"] * 2,
                [pd.Timestamp("2010-01-01 12:00:00", tz="UTC")] * 2,
            ],
            [
                "%Y-%m-%d %H:%M:%S %Z",
                [
                    "2010-01-01 12:00:00 UTC",
                    "2010-01-01 12:00:00 GMT",
                    "2010-01-01 12:00:00 US/Pacific",
                ],
                [
                    pd.Timestamp("2010-01-01 12:00:00", tz="UTC"),
                    pd.Timestamp("2010-01-01 12:00:00", tz="GMT"),
                    pd.Timestamp("2010-01-01 12:00:00", tz="US/Pacific"),
                ],
            ],
            [
                "%Y-%m-%d %H:%M:%S%z",
                ["2010-01-01 12:00:00+0100"] * 2,
                [pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(60))] * 2,
            ],
            [
                "%Y-%m-%d %H:%M:%S %z",
                ["2010-01-01 12:00:00 +0100"] * 2,
                [pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(60))] * 2,
            ],
            [
                "%Y-%m-%d %H:%M:%S %z",
                ["2010-01-01 12:00:00 +0100", "2010-01-01 12:00:00 -0100"],
                [
                    pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(60)),
                    pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(-60)),
                ],
            ],
            [
                "%Y-%m-%d %H:%M:%S %z",
                ["2010-01-01 12:00:00 Z", "2010-01-01 12:00:00 Z"],
                [
                    pd.Timestamp(
                        "2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(0)
                    ),  # pytz coerces to UTC
                    pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(0)),
                ],
            ],
        ],
    )
    def test_to_datetime_parse_tzname_or_tzoffset(self, fmt, dates, expected_dates):
        # GH 13486
        result = pd.to_datetime(dates, format=fmt)
        expected = pd.Index(expected_dates)
        tm.assert_equal(result, expected)

    def test_to_datetime_parse_tzname_or_tzoffset_different_tz_to_utc(self):
        # GH 32792
        dates = [
            "2010-01-01 12:00:00 +0100",
            "2010-01-01 12:00:00 -0100",
            "2010-01-01 12:00:00 +0300",
            "2010-01-01 12:00:00 +0400",
        ]
        expected_dates = [
            "2010-01-01 11:00:00+00:00",
            "2010-01-01 13:00:00+00:00",
            "2010-01-01 09:00:00+00:00",
            "2010-01-01 08:00:00+00:00",
        ]
        fmt = "%Y-%m-%d %H:%M:%S %z"

        result = pd.to_datetime(dates, format=fmt, utc=True)
        expected = pd.DatetimeIndex(expected_dates)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "offset", ["+0", "-1foo", "UTCbar", ":10", "+01:000:01", ""]
    )
    def test_to_datetime_parse_timezone_malformed(self, offset):
        fmt = "%Y-%m-%d %H:%M:%S %z"
        date = "2010-01-01 12:00:00 " + offset
        with pytest.raises(ValueError):
            pd.to_datetime([date], format=fmt)

    def test_to_datetime_parse_timezone_keeps_name(self):
        # GH 21697
        fmt = "%Y-%m-%d %H:%M:%S %z"
        arg = pd.Index(["2010-01-01 12:00:00 Z"], name="foo")
        result = pd.to_datetime(arg, format=fmt)
        expected = pd.DatetimeIndex(["2010-01-01 12:00:00"], tz="UTC", name="foo")
        tm.assert_index_equal(result, expected)


class TestToDatetime:
    @pytest.mark.parametrize(
        "s, _format, dt",
        [
            ["2015-1-1", "%G-%V-%u", datetime(2014, 12, 29, 0, 0)],
            ["2015-1-4", "%G-%V-%u", datetime(2015, 1, 1, 0, 0)],
            ["2015-1-7", "%G-%V-%u", datetime(2015, 1, 4, 0, 0)],
        ],
    )
    def test_to_datetime_iso_week_year_format(self, s, _format, dt):
        # See GH#16607
        assert to_datetime(s, format=_format) == dt

    @pytest.mark.parametrize(
        "msg, s, _format",
        [
            [
                "ISO week directive '%V' must be used with the ISO year directive "
                "'%G' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "1999 50",
                "%Y %V",
            ],
            [
                "ISO year directive '%G' must be used with the ISO week directive "
                "'%V' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "1999 51",
                "%G %V",
            ],
            [
                "ISO year directive '%G' must be used with the ISO week directive "
                "'%V' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "1999 Monday",
                "%G %A",
            ],
            [
                "ISO year directive '%G' must be used with the ISO week directive "
                "'%V' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "1999 Mon",
                "%G %a",
            ],
            [
                "ISO year directive '%G' must be used with the ISO week directive "
                "'%V' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "1999 6",
                "%G %w",
            ],
            [
                "ISO year directive '%G' must be used with the ISO week directive "
                "'%V' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "1999 6",
                "%G %u",
            ],
            [
                "ISO year directive '%G' must be used with the ISO week directive "
                "'%V' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "2051",
                "%G",
            ],
            [
                "Day of the year directive '%j' is not compatible with ISO year "
                "directive '%G'. Use '%Y' instead.",
                "1999 51 6 256",
                "%G %V %u %j",
            ],
            [
                "ISO week directive '%V' is incompatible with the year directive "
                "'%Y'. Use the ISO year '%G' instead.",
                "1999 51 Sunday",
                "%Y %V %A",
            ],
            [
                "ISO week directive '%V' is incompatible with the year directive "
                "'%Y'. Use the ISO year '%G' instead.",
                "1999 51 Sun",
                "%Y %V %a",
            ],
            [
                "ISO week directive '%V' is incompatible with the year directive "
                "'%Y'. Use the ISO year '%G' instead.",
                "1999 51 1",
                "%Y %V %w",
            ],
            [
                "ISO week directive '%V' is incompatible with the year directive "
                "'%Y'. Use the ISO year '%G' instead.",
                "1999 51 1",
                "%Y %V %u",
            ],
            [
                "ISO week directive '%V' must be used with the ISO year directive "
                "'%G' and a weekday directive '%A', '%a', '%w', or '%u'.",
                "20",
                "%V",
            ],
        ],
    )
    def test_error_iso_week_year(self, msg, s, _format):
        # See GH#16607
        # This test checks for errors thrown when giving the wrong format
        # However, as discussed on PR#25541, overriding the locale
        # causes a different error to be thrown due to the format being
        # locale specific, but the test data is in english.
        # Therefore, the tests only run when locale is not overwritten,
        # as a sort of solution to this problem.
        if locale.getlocale() != ("zh_CN", "UTF-8") and locale.getlocale() != (
            "it_IT",
            "UTF-8",
        ):
            with pytest.raises(ValueError, match=msg):
                to_datetime(s, format=_format)

    @pytest.mark.parametrize("tz", [None, "US/Central"])
    def test_to_datetime_dtarr(self, tz):
        # DatetimeArray
        dti = date_range("1965-04-03", periods=19, freq="2W", tz=tz)
        arr = DatetimeArray(dti)

        result = to_datetime(arr)
        assert result is arr

        result = to_datetime(arr)
        assert result is arr

    def test_to_datetime_pydatetime(self):
        actual = pd.to_datetime(datetime(2008, 1, 15))
        assert actual == datetime(2008, 1, 15)

    def test_to_datetime_YYYYMMDD(self):
        actual = pd.to_datetime("20080115")
        assert actual == datetime(2008, 1, 15)

    def test_to_datetime_unparseable_ignore(self):
        # unparseable
        s = "Month 1, 1999"
        assert pd.to_datetime(s, errors="ignore") == s

    @td.skip_if_windows  # `tm.set_timezone` does not work in windows
    def test_to_datetime_now(self):
        # See GH#18666
        with tm.set_timezone("US/Eastern"):
            npnow = np.datetime64("now").astype("datetime64[ns]")
            pdnow = pd.to_datetime("now")
            pdnow2 = pd.to_datetime(["now"])[0]

            # These should all be equal with infinite perf; this gives
            # a generous margin of 10 seconds
            assert abs(pdnow.value - npnow.astype(np.int64)) < 1e10
            assert abs(pdnow2.value - npnow.astype(np.int64)) < 1e10

            assert pdnow.tzinfo is None
            assert pdnow2.tzinfo is None

    @td.skip_if_windows  # `tm.set_timezone` does not work in windows
    def test_to_datetime_today(self):
        # See GH#18666
        # Test with one timezone far ahead of UTC and another far behind, so
        # one of these will _almost_ always be in a different day from UTC.
        # Unfortunately this test between 12 and 1 AM Samoa time
        # this both of these timezones _and_ UTC will all be in the same day,
        # so this test will not detect the regression introduced in #18666.
        with tm.set_timezone("Pacific/Auckland"):  # 12-13 hours ahead of UTC
            nptoday = np.datetime64("today").astype("datetime64[ns]").astype(np.int64)
            pdtoday = pd.to_datetime("today")
            pdtoday2 = pd.to_datetime(["today"])[0]

            tstoday = pd.Timestamp("today")
            tstoday2 = pd.Timestamp.today()

            # These should all be equal with infinite perf; this gives
            # a generous margin of 10 seconds
            assert abs(pdtoday.normalize().value - nptoday) < 1e10
            assert abs(pdtoday2.normalize().value - nptoday) < 1e10
            assert abs(pdtoday.value - tstoday.value) < 1e10
            assert abs(pdtoday.value - tstoday2.value) < 1e10

            assert pdtoday.tzinfo is None
            assert pdtoday2.tzinfo is None

        with tm.set_timezone("US/Samoa"):  # 11 hours behind UTC
            nptoday = np.datetime64("today").astype("datetime64[ns]").astype(np.int64)
            pdtoday = pd.to_datetime("today")
            pdtoday2 = pd.to_datetime(["today"])[0]

            # These should all be equal with infinite perf; this gives
            # a generous margin of 10 seconds
            assert abs(pdtoday.normalize().value - nptoday) < 1e10
            assert abs(pdtoday2.normalize().value - nptoday) < 1e10

            assert pdtoday.tzinfo is None
            assert pdtoday2.tzinfo is None

    def test_to_datetime_today_now_unicode_bytes(self):
        to_datetime(["now"])
        to_datetime(["today"])

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_dt64s(self, cache):
        in_bound_dts = [np.datetime64("2000-01-01"), np.datetime64("2000-01-02")]

        for dt in in_bound_dts:
            assert pd.to_datetime(dt, cache=cache) == Timestamp(dt)

    @pytest.mark.parametrize(
        "dt", [np.datetime64("1000-01-01"), np.datetime64("5000-01-02")]
    )
    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_dt64s_out_of_bounds(self, cache, dt):
        msg = f"Out of bounds nanosecond timestamp: {dt}"
        with pytest.raises(OutOfBoundsDatetime, match=msg):
            pd.to_datetime(dt, errors="raise")
        with pytest.raises(OutOfBoundsDatetime, match=msg):
            Timestamp(dt)
        assert pd.to_datetime(dt, errors="coerce", cache=cache) is NaT

    @pytest.mark.parametrize("cache", [True, False])
    @pytest.mark.parametrize("unit", ["s", "D"])
    def test_to_datetime_array_of_dt64s(self, cache, unit):
        # https://github.com/pandas-dev/pandas/issues/31491
        # Need at least 50 to ensure cache is used.
        dts = [
            np.datetime64("2000-01-01", unit),
            np.datetime64("2000-01-02", unit),
        ] * 30
        # Assuming all datetimes are in bounds, to_datetime() returns
        # an array that is equal to Timestamp() parsing
        tm.assert_index_equal(
            pd.to_datetime(dts, cache=cache),
            pd.DatetimeIndex([Timestamp(x).asm8 for x in dts]),
        )

        # A list of datetimes where the last one is out of bounds
        dts_with_oob = dts + [np.datetime64("9999-01-01")]

        msg = "Out of bounds nanosecond timestamp: 9999-01-01 00:00:00"
        with pytest.raises(OutOfBoundsDatetime, match=msg):
            pd.to_datetime(dts_with_oob, errors="raise")

        tm.assert_index_equal(
            pd.to_datetime(dts_with_oob, errors="coerce", cache=cache),
            pd.DatetimeIndex(
                [Timestamp(dts_with_oob[0]).asm8, Timestamp(dts_with_oob[1]).asm8] * 30
                + [pd.NaT],
            ),
        )

        # With errors='ignore', out of bounds datetime64s
        # are converted to their .item(), which depending on the version of
        # numpy is either a python datetime.datetime or datetime.date
        tm.assert_index_equal(
            pd.to_datetime(dts_with_oob, errors="ignore", cache=cache),
            pd.Index([dt.item() for dt in dts_with_oob]),
        )

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_tz(self, cache):

        # xref 8260
        # uniform returns a DatetimeIndex
        arr = [
            pd.Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
            pd.Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"),
        ]
        result = pd.to_datetime(arr, cache=cache)
        expected = DatetimeIndex(
            ["2013-01-01 13:00:00", "2013-01-02 14:00:00"], tz="US/Pacific"
        )
        tm.assert_index_equal(result, expected)

        # mixed tzs will raise
        arr = [
            pd.Timestamp("2013-01-01 13:00:00", tz="US/Pacific"),
            pd.Timestamp("2013-01-02 14:00:00", tz="US/Eastern"),
        ]
        msg = (
            "Tz-aware datetime.datetime cannot be "
            "converted to datetime64 unless utc=True"
        )
        with pytest.raises(ValueError, match=msg):
            pd.to_datetime(arr, cache=cache)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_different_offsets(self, cache):
        # inspired by asv timeseries.ToDatetimeNONISO8601 benchmark
        # see GH-26097 for more
        ts_string_1 = "March 1, 2018 12:00:00+0400"
        ts_string_2 = "March 1, 2018 12:00:00+0500"
        arr = [ts_string_1] * 5 + [ts_string_2] * 5
        expected = pd.Index([parse(x) for x in arr])
        result = pd.to_datetime(arr, cache=cache)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_tz_pytz(self, cache):
        # see gh-8260
        us_eastern = pytz.timezone("US/Eastern")
        arr = np.array(
            [
                us_eastern.localize(
                    datetime(year=2000, month=1, day=1, hour=3, minute=0)
                ),
                us_eastern.localize(
                    datetime(year=2000, month=6, day=1, hour=3, minute=0)
                ),
            ],
            dtype=object,
        )
        result = pd.to_datetime(arr, utc=True, cache=cache)
        expected = DatetimeIndex(
            ["2000-01-01 08:00:00+00:00", "2000-06-01 07:00:00+00:00"],
            dtype="datetime64[ns, UTC]",
            freq=None,
        )
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    @pytest.mark.parametrize(
        "init_constructor, end_constructor, test_method",
        [
            (Index, DatetimeIndex, tm.assert_index_equal),
            (list, DatetimeIndex, tm.assert_index_equal),
            (np.array, DatetimeIndex, tm.assert_index_equal),
            (Series, Series, tm.assert_series_equal),
        ],
    )
    def test_to_datetime_utc_true(
        self, cache, init_constructor, end_constructor, test_method
    ):
        # See gh-11934 & gh-6415
        data = ["20100102 121314", "20100102 121315"]
        expected_data = [
            pd.Timestamp("2010-01-02 12:13:14", tz="utc"),
            pd.Timestamp("2010-01-02 12:13:15", tz="utc"),
        ]

        result = pd.to_datetime(
            init_constructor(data), format="%Y%m%d %H%M%S", utc=True, cache=cache
        )
        expected = end_constructor(expected_data)
        test_method(result, expected)

        # Test scalar case as well
        for scalar, expected in zip(data, expected_data):
            result = pd.to_datetime(
                scalar, format="%Y%m%d %H%M%S", utc=True, cache=cache
            )
            assert result == expected

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_utc_true_with_series_single_value(self, cache):
        # GH 15760 UTC=True with Series
        ts = 1.5e18
        result = pd.to_datetime(pd.Series([ts]), utc=True, cache=cache)
        expected = pd.Series([pd.Timestamp(ts, tz="utc")])
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_utc_true_with_series_tzaware_string(self, cache):
        ts = "2013-01-01 00:00:00-01:00"
        expected_ts = "2013-01-01 01:00:00"
        data = pd.Series([ts] * 3)
        result = pd.to_datetime(data, utc=True, cache=cache)
        expected = pd.Series([pd.Timestamp(expected_ts, tz="utc")] * 3)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    @pytest.mark.parametrize(
        "date, dtype",
        [
            ("2013-01-01 01:00:00", "datetime64[ns]"),
            ("2013-01-01 01:00:00", "datetime64[ns, UTC]"),
        ],
    )
    def test_to_datetime_utc_true_with_series_datetime_ns(self, cache, date, dtype):
        expected = pd.Series([pd.Timestamp("2013-01-01 01:00:00", tz="UTC")])
        result = pd.to_datetime(pd.Series([date], dtype=dtype), utc=True, cache=cache)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    @td.skip_if_no("psycopg2")
    def test_to_datetime_tz_psycopg2(self, cache):

        # xref 8260
        import psycopg2

        # misc cases
        tz1 = psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)
        tz2 = psycopg2.tz.FixedOffsetTimezone(offset=-240, name=None)
        arr = np.array(
            [
                datetime(2000, 1, 1, 3, 0, tzinfo=tz1),
                datetime(2000, 6, 1, 3, 0, tzinfo=tz2),
            ],
            dtype=object,
        )

        result = pd.to_datetime(arr, errors="coerce", utc=True, cache=cache)
        expected = DatetimeIndex(
            ["2000-01-01 08:00:00+00:00", "2000-06-01 07:00:00+00:00"],
            dtype="datetime64[ns, UTC]",
            freq=None,
        )
        tm.assert_index_equal(result, expected)

        # dtype coercion
        i = pd.DatetimeIndex(
            ["2000-01-01 08:00:00"],
            tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None),
        )
        assert is_datetime64_ns_dtype(i)

        # tz coercion
        result = pd.to_datetime(i, errors="coerce", cache=cache)
        tm.assert_index_equal(result, i)

        result = pd.to_datetime(i, errors="coerce", utc=True, cache=cache)
        expected = pd.DatetimeIndex(
            ["2000-01-01 13:00:00"], dtype="datetime64[ns, UTC]"
        )
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_datetime_bool(self, cache):
        # GH13176
        with pytest.raises(TypeError):
            to_datetime(False)
        assert to_datetime(False, errors="coerce", cache=cache) is NaT
        assert to_datetime(False, errors="ignore", cache=cache) is False
        with pytest.raises(TypeError):
            to_datetime(True)
        assert to_datetime(True, errors="coerce", cache=cache) is NaT
        assert to_datetime(True, errors="ignore", cache=cache) is True
        with pytest.raises(TypeError):
            to_datetime([False, datetime.today()], cache=cache)
        with pytest.raises(TypeError):
            to_datetime(["20130101", True], cache=cache)
        tm.assert_index_equal(
            to_datetime([0, False, NaT, 0.0], errors="coerce", cache=cache),
            DatetimeIndex(
                [to_datetime(0, cache=cache), NaT, NaT, to_datetime(0, cache=cache)]
            ),
        )

    def test_datetime_invalid_datatype(self):
        # GH13176

        with pytest.raises(TypeError):
            pd.to_datetime(bool)
        with pytest.raises(TypeError):
            pd.to_datetime(pd.to_datetime)

    @pytest.mark.parametrize("value", ["a", "00:01:99"])
    @pytest.mark.parametrize("infer", [True, False])
    @pytest.mark.parametrize("format", [None, "H%:M%:S%"])
    def test_datetime_invalid_scalar(self, value, format, infer):
        # GH24763
        res = pd.to_datetime(
            value, errors="ignore", format=format, infer_datetime_format=infer
        )
        assert res == value

        res = pd.to_datetime(
            value, errors="coerce", format=format, infer_datetime_format=infer
        )
        assert res is pd.NaT

        with pytest.raises(ValueError):
            pd.to_datetime(
                value, errors="raise", format=format, infer_datetime_format=infer
            )

    @pytest.mark.parametrize("value", ["3000/12/11 00:00:00"])
    @pytest.mark.parametrize("infer", [True, False])
    @pytest.mark.parametrize("format", [None, "H%:M%:S%"])
    def test_datetime_outofbounds_scalar(self, value, format, infer):
        # GH24763
        res = pd.to_datetime(
            value, errors="ignore", format=format, infer_datetime_format=infer
        )
        assert res == value

        res = pd.to_datetime(
            value, errors="coerce", format=format, infer_datetime_format=infer
        )
        assert res is pd.NaT

        if format is not None:
            with pytest.raises(ValueError):
                pd.to_datetime(
                    value, errors="raise", format=format, infer_datetime_format=infer
                )
        else:
            with pytest.raises(OutOfBoundsDatetime):
                pd.to_datetime(
                    value, errors="raise", format=format, infer_datetime_format=infer
                )

    @pytest.mark.parametrize("values", [["a"], ["00:01:99"], ["a", "b", "99:00:00"]])
    @pytest.mark.parametrize("infer", [True, False])
    @pytest.mark.parametrize("format", [None, "H%:M%:S%"])
    def test_datetime_invalid_index(self, values, format, infer):
        # GH24763
        res = pd.to_datetime(
            values, errors="ignore", format=format, infer_datetime_format=infer
        )
        tm.assert_index_equal(res, pd.Index(values))

        res = pd.to_datetime(
            values, errors="coerce", format=format, infer_datetime_format=infer
        )
        tm.assert_index_equal(res, pd.DatetimeIndex([pd.NaT] * len(values)))

        with pytest.raises(ValueError):
            pd.to_datetime(
                values, errors="raise", format=format, infer_datetime_format=infer
            )

    @pytest.mark.parametrize("utc", [True, None])
    @pytest.mark.parametrize("format", ["%Y%m%d %H:%M:%S", None])
    @pytest.mark.parametrize("constructor", [list, tuple, np.array, pd.Index, deque])
    def test_to_datetime_cache(self, utc, format, constructor):
        date = "20130101 00:00:00"
        test_dates = [date] * 10 ** 5
        data = constructor(test_dates)

        result = pd.to_datetime(data, utc=utc, format=format, cache=True)
        expected = pd.to_datetime(data, utc=utc, format=format, cache=False)

        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "listlike",
        [
            (deque([pd.Timestamp("2010-06-02 09:30:00")] * 51)),
            ([pd.Timestamp("2010-06-02 09:30:00")] * 51),
            (tuple([pd.Timestamp("2010-06-02 09:30:00")] * 51)),
        ],
    )
    def test_no_slicing_errors_in_should_cache(self, listlike):
        # GH 29403
        assert tools.should_cache(listlike) is True

    def test_to_datetime_from_deque(self):
        # GH 29403
        result = pd.to_datetime(deque([pd.Timestamp("2010-06-02 09:30:00")] * 51))
        expected = pd.to_datetime([pd.Timestamp("2010-06-02 09:30:00")] * 51)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("utc", [True, None])
    @pytest.mark.parametrize("format", ["%Y%m%d %H:%M:%S", None])
    def test_to_datetime_cache_series(self, utc, format):
        date = "20130101 00:00:00"
        test_dates = [date] * 10 ** 5
        data = pd.Series(test_dates)
        result = pd.to_datetime(data, utc=utc, format=format, cache=True)
        expected = pd.to_datetime(data, utc=utc, format=format, cache=False)
        tm.assert_series_equal(result, expected)

    def test_to_datetime_cache_scalar(self):
        date = "20130101 00:00:00"
        result = pd.to_datetime(date, cache=True)
        expected = pd.Timestamp("20130101 00:00:00")
        assert result == expected

    @pytest.mark.parametrize(
        "date, format",
        [
            ("2017-20", "%Y-%W"),
            ("20 Sunday", "%W %A"),
            ("20 Sun", "%W %a"),
            ("2017-21", "%Y-%U"),
            ("20 Sunday", "%U %A"),
            ("20 Sun", "%U %a"),
        ],
    )
    def test_week_without_day_and_calendar_year(self, date, format):
        # GH16774

        msg = "Cannot use '%W' or '%U' without day and year"
        with pytest.raises(ValueError, match=msg):
            pd.to_datetime(date, format=format)

    def test_to_datetime_coerce(self):
        # GH 26122
        ts_strings = [
            "March 1, 2018 12:00:00+0400",
            "March 1, 2018 12:00:00+0500",
            "20100240",
        ]
        result = to_datetime(ts_strings, errors="coerce")
        expected = Index(
            [
                datetime(2018, 3, 1, 12, 0, tzinfo=tzoffset(None, 14400)),
                datetime(2018, 3, 1, 12, 0, tzinfo=tzoffset(None, 18000)),
                NaT,
            ]
        )
        tm.assert_index_equal(result, expected)

    def test_to_datetime_coerce_malformed(self):
        # GH 28299
        ts_strings = ["200622-12-31", "111111-24-11"]
        result = to_datetime(ts_strings, errors="coerce")
        expected = Index([NaT, NaT])
        tm.assert_index_equal(result, expected)

    def test_iso_8601_strings_with_same_offset(self):
        # GH 17697, 11736
        ts_str = "2015-11-18 15:30:00+05:30"
        result = to_datetime(ts_str)
        expected = Timestamp(ts_str)
        assert result == expected

        expected = DatetimeIndex([Timestamp(ts_str)] * 2)
        result = to_datetime([ts_str] * 2)
        tm.assert_index_equal(result, expected)

        result = DatetimeIndex([ts_str] * 2)
        tm.assert_index_equal(result, expected)

    def test_iso_8601_strings_with_different_offsets(self):
        # GH 17697, 11736
        ts_strings = ["2015-11-18 15:30:00+05:30", "2015-11-18 16:30:00+06:30", NaT]
        result = to_datetime(ts_strings)
        expected = np.array(
            [
                datetime(2015, 11, 18, 15, 30, tzinfo=tzoffset(None, 19800)),
                datetime(2015, 11, 18, 16, 30, tzinfo=tzoffset(None, 23400)),
                NaT,
            ],
            dtype=object,
        )
        # GH 21864
        expected = Index(expected)
        tm.assert_index_equal(result, expected)

        result = to_datetime(ts_strings, utc=True)
        expected = DatetimeIndex(
            [Timestamp(2015, 11, 18, 10), Timestamp(2015, 11, 18, 10), NaT], tz="UTC"
        )
        tm.assert_index_equal(result, expected)

    def test_iso8601_strings_mixed_offsets_with_naive(self):
        # GH 24992
        result = pd.to_datetime(
            [
                "2018-11-28T00:00:00",
                "2018-11-28T00:00:00+12:00",
                "2018-11-28T00:00:00",
                "2018-11-28T00:00:00+06:00",
                "2018-11-28T00:00:00",
            ],
            utc=True,
        )
        expected = pd.to_datetime(
            [
                "2018-11-28T00:00:00",
                "2018-11-27T12:00:00",
                "2018-11-28T00:00:00",
                "2018-11-27T18:00:00",
                "2018-11-28T00:00:00",
            ],
            utc=True,
        )
        tm.assert_index_equal(result, expected)

        items = ["2018-11-28T00:00:00+12:00", "2018-11-28T00:00:00"]
        result = pd.to_datetime(items, utc=True)
        expected = pd.to_datetime(list(reversed(items)), utc=True)[::-1]
        tm.assert_index_equal(result, expected)

    def test_mixed_offsets_with_native_datetime_raises(self):
        # GH 25978
        s = pd.Series(
            [
                "nan",
                pd.Timestamp("1990-01-01"),
                "2015-03-14T16:15:14.123-08:00",
                "2019-03-04T21:56:32.620-07:00",
                None,
            ]
        )
        with pytest.raises(ValueError, match="Tz-aware datetime.datetime"):
            pd.to_datetime(s)

    def test_non_iso_strings_with_tz_offset(self):
        result = to_datetime(["March 1, 2018 12:00:00+0400"] * 2)
        expected = DatetimeIndex(
            [datetime(2018, 3, 1, 12, tzinfo=pytz.FixedOffset(240))] * 2
        )
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "ts, expected",
        [
            (Timestamp("2018-01-01"), Timestamp("2018-01-01", tz="UTC")),
            (
                Timestamp("2018-01-01", tz="US/Pacific"),
                Timestamp("2018-01-01 08:00", tz="UTC"),
            ),
        ],
    )
    def test_timestamp_utc_true(self, ts, expected):
        # GH 24415
        result = to_datetime(ts, utc=True)
        assert result == expected

    @pytest.mark.parametrize("dt_str", ["00010101", "13000101", "30000101", "99990101"])
    def test_to_datetime_with_format_out_of_bounds(self, dt_str):
        # GH 9107
        with pytest.raises(OutOfBoundsDatetime):
            pd.to_datetime(dt_str, format="%Y%m%d")

    def test_to_datetime_utc(self):
        arr = np.array([parse("2012-06-13T01:39:00Z")], dtype=object)

        result = to_datetime(arr, utc=True)
        assert result.tz is pytz.utc

    def test_to_datetime_fixed_offset(self):
        from pandas.tests.indexes.datetimes.test_timezones import fixed_off

        dates = [
            datetime(2000, 1, 1, tzinfo=fixed_off),
            datetime(2000, 1, 2, tzinfo=fixed_off),
            datetime(2000, 1, 3, tzinfo=fixed_off),
        ]
        result = to_datetime(dates)
        assert result.tz == fixed_off


class TestToDatetimeUnit:
    @pytest.mark.parametrize("cache", [True, False])
    def test_unit(self, cache):
        # GH 11758
        # test proper behavior with errors

        with pytest.raises(ValueError):
            to_datetime([1], unit="D", format="%Y%m%d", cache=cache)

        values = [11111111, 1, 1.0, iNaT, NaT, np.nan, "NaT", ""]
        result = to_datetime(values, unit="D", errors="ignore", cache=cache)
        expected = Index(
            [
                11111111,
                Timestamp("1970-01-02"),
                Timestamp("1970-01-02"),
                NaT,
                NaT,
                NaT,
                NaT,
                NaT,
            ],
            dtype=object,
        )
        tm.assert_index_equal(result, expected)

        result = to_datetime(values, unit="D", errors="coerce", cache=cache)
        expected = DatetimeIndex(
            ["NaT", "1970-01-02", "1970-01-02", "NaT", "NaT", "NaT", "NaT", "NaT"]
        )
        tm.assert_index_equal(result, expected)

        with pytest.raises(tslib.OutOfBoundsDatetime):
            to_datetime(values, unit="D", errors="raise", cache=cache)

        values = [1420043460000, iNaT, NaT, np.nan, "NaT"]

        result = to_datetime(values, errors="ignore", unit="s", cache=cache)
        expected = Index([1420043460000, NaT, NaT, NaT, NaT], dtype=object)
        tm.assert_index_equal(result, expected)

        result = to_datetime(values, errors="coerce", unit="s", cache=cache)
        expected = DatetimeIndex(["NaT", "NaT", "NaT", "NaT", "NaT"])
        tm.assert_index_equal(result, expected)

        with pytest.raises(tslib.OutOfBoundsDatetime):
            to_datetime(values, errors="raise", unit="s", cache=cache)

        # if we have a string, then we raise a ValueError
        # and NOT an OutOfBoundsDatetime
        for val in ["foo", Timestamp("20130101")]:
            try:
                to_datetime(val, errors="raise", unit="s", cache=cache)
            except tslib.OutOfBoundsDatetime as err:
                raise AssertionError("incorrect exception raised") from err
            except ValueError:
                pass

    @pytest.mark.parametrize("cache", [True, False])
    def test_unit_consistency(self, cache):

        # consistency of conversions
        expected = Timestamp("1970-05-09 14:25:11")
        result = pd.to_datetime(11111111, unit="s", errors="raise", cache=cache)
        assert result == expected
        assert isinstance(result, Timestamp)

        result = pd.to_datetime(11111111, unit="s", errors="coerce", cache=cache)
        assert result == expected
        assert isinstance(result, Timestamp)

        result = pd.to_datetime(11111111, unit="s", errors="ignore", cache=cache)
        assert result == expected
        assert isinstance(result, Timestamp)

    @pytest.mark.parametrize("cache", [True, False])
    def test_unit_with_numeric(self, cache):

        # GH 13180
        # coercions from floats/ints are ok
        expected = DatetimeIndex(["2015-06-19 05:33:20", "2015-05-27 22:33:20"])
        arr1 = [1.434692e18, 1.432766e18]
        arr2 = np.array(arr1).astype("int64")
        for errors in ["ignore", "raise", "coerce"]:
            result = pd.to_datetime(arr1, errors=errors, cache=cache)
            tm.assert_index_equal(result, expected)

            result = pd.to_datetime(arr2, errors=errors, cache=cache)
            tm.assert_index_equal(result, expected)

        # but we want to make sure that we are coercing
        # if we have ints/strings
        expected = DatetimeIndex(["NaT", "2015-06-19 05:33:20", "2015-05-27 22:33:20"])
        arr = ["foo", 1.434692e18, 1.432766e18]
        result = pd.to_datetime(arr, errors="coerce", cache=cache)
        tm.assert_index_equal(result, expected)

        expected = DatetimeIndex(
            ["2015-06-19 05:33:20", "2015-05-27 22:33:20", "NaT", "NaT"]
        )
        arr = [1.434692e18, 1.432766e18, "foo", "NaT"]
        result = pd.to_datetime(arr, errors="coerce", cache=cache)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_unit_mixed(self, cache):

        # mixed integers/datetimes
        expected = DatetimeIndex(["2013-01-01", "NaT", "NaT"])
        arr = [pd.Timestamp("20130101"), 1.434692e18, 1.432766e18]
        result = pd.to_datetime(arr, errors="coerce", cache=cache)
        tm.assert_index_equal(result, expected)

        with pytest.raises(ValueError):
            pd.to_datetime(arr, errors="raise", cache=cache)

        expected = DatetimeIndex(["NaT", "NaT", "2013-01-01"])
        arr = [1.434692e18, 1.432766e18, pd.Timestamp("20130101")]
        result = pd.to_datetime(arr, errors="coerce", cache=cache)
        tm.assert_index_equal(result, expected)

        with pytest.raises(ValueError):
            pd.to_datetime(arr, errors="raise", cache=cache)

    @pytest.mark.parametrize("cache", [True, False])
    def test_unit_rounding(self, cache):
        # GH 14156: argument will incur floating point errors but no
        # premature rounding
        result = pd.to_datetime(1434743731.8770001, unit="s", cache=cache)
        expected = pd.Timestamp("2015-06-19 19:55:31.877000093")
        assert result == expected

    @pytest.mark.parametrize("cache", [True, False])
    def test_unit_ignore_keeps_name(self, cache):
        # GH 21697
        expected = pd.Index([15e9] * 2, name="name")
        result = pd.to_datetime(expected, errors="ignore", unit="s", cache=cache)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_dataframe(self, cache):

        df = DataFrame(
            {
                "year": [2015, 2016],
                "month": [2, 3],
                "day": [4, 5],
                "hour": [6, 7],
                "minute": [58, 59],
                "second": [10, 11],
                "ms": [1, 1],
                "us": [2, 2],
                "ns": [3, 3],
            }
        )

        result = to_datetime(
            {"year": df["year"], "month": df["month"], "day": df["day"]}, cache=cache
        )
        expected = Series(
            [Timestamp("20150204 00:00:00"), Timestamp("20160305 00:0:00")]
        )
        tm.assert_series_equal(result, expected)

        # dict-like
        result = to_datetime(df[["year", "month", "day"]].to_dict(), cache=cache)
        tm.assert_series_equal(result, expected)

        # dict but with constructable
        df2 = df[["year", "month", "day"]].to_dict()
        df2["month"] = 2
        result = to_datetime(df2, cache=cache)
        expected2 = Series(
            [Timestamp("20150204 00:00:00"), Timestamp("20160205 00:0:00")]
        )
        tm.assert_series_equal(result, expected2)

        # unit mappings
        units = [
            {
                "year": "years",
                "month": "months",
                "day": "days",
                "hour": "hours",
                "minute": "minutes",
                "second": "seconds",
            },
            {
                "year": "year",
                "month": "month",
                "day": "day",
                "hour": "hour",
                "minute": "minute",
                "second": "second",
            },
        ]

        for d in units:
            result = to_datetime(df[list(d.keys())].rename(columns=d), cache=cache)
            expected = Series(
                [Timestamp("20150204 06:58:10"), Timestamp("20160305 07:59:11")]
            )
            tm.assert_series_equal(result, expected)

        d = {
            "year": "year",
            "month": "month",
            "day": "day",
            "hour": "hour",
            "minute": "minute",
            "second": "second",
            "ms": "ms",
            "us": "us",
            "ns": "ns",
        }

        result = to_datetime(df.rename(columns=d), cache=cache)
        expected = Series(
            [
                Timestamp("20150204 06:58:10.001002003"),
                Timestamp("20160305 07:59:11.001002003"),
            ]
        )
        tm.assert_series_equal(result, expected)

        # coerce back to int
        result = to_datetime(df.astype(str), cache=cache)
        tm.assert_series_equal(result, expected)

        # passing coerce
        df2 = DataFrame({"year": [2015, 2016], "month": [2, 20], "day": [4, 5]})

        msg = (
            "cannot assemble the datetimes: time data .+ does not "
            r"match format '%Y%m%d' \(match\)"
        )
        with pytest.raises(ValueError, match=msg):
            to_datetime(df2, cache=cache)
        result = to_datetime(df2, errors="coerce", cache=cache)
        expected = Series([Timestamp("20150204 00:00:00"), NaT])
        tm.assert_series_equal(result, expected)

        # extra columns
        msg = r"extra keys have been passed to the datetime assemblage: \[foo\]"
        with pytest.raises(ValueError, match=msg):
            df2 = df.copy()
            df2["foo"] = 1
            to_datetime(df2, cache=cache)

        # not enough
        msg = (
            r"to assemble mappings requires at least that \[year, month, "
            r"day\] be specified: \[.+\] is missing"
        )
        for c in [
            ["year"],
            ["year", "month"],
            ["year", "month", "second"],
            ["month", "day"],
            ["year", "day", "second"],
        ]:
            with pytest.raises(ValueError, match=msg):
                to_datetime(df[c], cache=cache)

        # duplicates
        msg = "cannot assemble with duplicate keys"
        df2 = DataFrame({"year": [2015, 2016], "month": [2, 20], "day": [4, 5]})
        df2.columns = ["year", "year", "day"]
        with pytest.raises(ValueError, match=msg):
            to_datetime(df2, cache=cache)

        df2 = DataFrame(
            {"year": [2015, 2016], "month": [2, 20], "day": [4, 5], "hour": [4, 5]}
        )
        df2.columns = ["year", "month", "day", "day"]
        with pytest.raises(ValueError, match=msg):
            to_datetime(df2, cache=cache)

    @pytest.mark.parametrize("cache", [True, False])
    def test_dataframe_dtypes(self, cache):
        # #13451
        df = DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})

        # int16
        result = to_datetime(df.astype("int16"), cache=cache)
        expected = Series(
            [Timestamp("20150204 00:00:00"), Timestamp("20160305 00:00:00")]
        )
        tm.assert_series_equal(result, expected)

        # mixed dtypes
        df["month"] = df["month"].astype("int8")
        df["day"] = df["day"].astype("int8")
        result = to_datetime(df, cache=cache)
        expected = Series(
            [Timestamp("20150204 00:00:00"), Timestamp("20160305 00:00:00")]
        )
        tm.assert_series_equal(result, expected)

        # float
        df = DataFrame({"year": [2000, 2001], "month": [1.5, 1], "day": [1, 1]})
        with pytest.raises(ValueError):
            to_datetime(df, cache=cache)

    def test_dataframe_utc_true(self):
        # GH 23760
        df = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
        result = pd.to_datetime(df, utc=True)
        expected = pd.Series(
            np.array(["2015-02-04", "2016-03-05"], dtype="datetime64[ns]")
        ).dt.tz_localize("UTC")
        tm.assert_series_equal(result, expected)

    def test_to_datetime_errors_ignore_utc_true(self):
        # GH 23758
        result = pd.to_datetime([1], unit="s", utc=True, errors="ignore")
        expected = DatetimeIndex(["1970-01-01 00:00:01"], tz="UTC")
        tm.assert_index_equal(result, expected)

    # TODO: this is moved from tests.series.test_timeseries, may be redundant
    def test_to_datetime_unit(self):

        epoch = 1370745748
        s = Series([epoch + t for t in range(20)])
        result = to_datetime(s, unit="s")
        expected = Series(
            [Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t) for t in range(20)]
        )
        tm.assert_series_equal(result, expected)

        s = Series([epoch + t for t in range(20)]).astype(float)
        result = to_datetime(s, unit="s")
        expected = Series(
            [Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t) for t in range(20)]
        )
        tm.assert_series_equal(result, expected)

        s = Series([epoch + t for t in range(20)] + [iNaT])
        result = to_datetime(s, unit="s")
        expected = Series(
            [Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t) for t in range(20)]
            + [NaT]
        )
        tm.assert_series_equal(result, expected)

        s = Series([epoch + t for t in range(20)] + [iNaT]).astype(float)
        result = to_datetime(s, unit="s")
        expected = Series(
            [Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t) for t in range(20)]
            + [NaT]
        )
        tm.assert_series_equal(result, expected)

        # GH13834
        s = Series([epoch + t for t in np.arange(0, 2, 0.25)] + [iNaT]).astype(float)
        result = to_datetime(s, unit="s")
        expected = Series(
            [
                Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t)
                for t in np.arange(0, 2, 0.25)
            ]
            + [NaT]
        )
        tm.assert_series_equal(result, expected)

        s = pd.concat(
            [Series([epoch + t for t in range(20)]).astype(float), Series([np.nan])],
            ignore_index=True,
        )
        result = to_datetime(s, unit="s")
        expected = Series(
            [Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t) for t in range(20)]
            + [NaT]
        )
        tm.assert_series_equal(result, expected)

        result = to_datetime([1, 2, "NaT", pd.NaT, np.nan], unit="D")
        expected = DatetimeIndex(
            [Timestamp("1970-01-02"), Timestamp("1970-01-03")] + ["NaT"] * 3
        )
        tm.assert_index_equal(result, expected)

        msg = "non convertible value foo with the unit 'D'"
        with pytest.raises(ValueError, match=msg):
            to_datetime([1, 2, "foo"], unit="D")
        msg = "cannot convert input 111111111 with the unit 'D'"
        with pytest.raises(OutOfBoundsDatetime, match=msg):
            to_datetime([1, 2, 111111111], unit="D")

        # coerce we can process
        expected = DatetimeIndex(
            [Timestamp("1970-01-02"), Timestamp("1970-01-03")] + ["NaT"] * 1
        )
        result = to_datetime([1, 2, "foo"], unit="D", errors="coerce")
        tm.assert_index_equal(result, expected)

        result = to_datetime([1, 2, 111111111], unit="D", errors="coerce")
        tm.assert_index_equal(result, expected)


class TestToDatetimeMisc:
    def test_to_datetime_barely_out_of_bounds(self):
        # GH#19529
        # GH#19382 close enough to bounds that dropping nanos would result
        # in an in-bounds datetime
        arr = np.array(["2262-04-11 23:47:16.854775808"], dtype=object)

        with pytest.raises(OutOfBoundsDatetime):
            to_datetime(arr)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_iso8601(self, cache):
        result = to_datetime(["2012-01-01 00:00:00"], cache=cache)
        exp = Timestamp("2012-01-01 00:00:00")
        assert result[0] == exp

        result = to_datetime(["20121001"], cache=cache)  # bad iso 8601
        exp = Timestamp("2012-10-01")
        assert result[0] == exp

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_default(self, cache):
        rs = to_datetime("2001", cache=cache)
        xp = datetime(2001, 1, 1)
        assert rs == xp

        # dayfirst is essentially broken

        # to_datetime('01-13-2012', dayfirst=True)
        # pytest.raises(ValueError, to_datetime('01-13-2012',
        #                   dayfirst=True))

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_on_datetime64_series(self, cache):
        # #2699
        s = Series(date_range("1/1/2000", periods=10))

        result = to_datetime(s, cache=cache)
        assert result[0] == s[0]

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_with_space_in_series(self, cache):
        # GH 6428
        s = Series(["10/18/2006", "10/18/2008", " "])
        msg = r"(\(')?String does not contain a date(:', ' '\))?"
        with pytest.raises(ValueError, match=msg):
            to_datetime(s, errors="raise", cache=cache)
        result_coerce = to_datetime(s, errors="coerce", cache=cache)
        expected_coerce = Series([datetime(2006, 10, 18), datetime(2008, 10, 18), NaT])
        tm.assert_series_equal(result_coerce, expected_coerce)
        result_ignore = to_datetime(s, errors="ignore", cache=cache)
        tm.assert_series_equal(result_ignore, s)

    @td.skip_if_has_locale
    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_with_apply(self, cache):
        # this is only locale tested with US/None locales
        # GH 5195
        # with a format and coerce a single item to_datetime fails
        td = Series(["May 04", "Jun 02", "Dec 11"], index=[1, 2, 3])
        expected = pd.to_datetime(td, format="%b %y", cache=cache)
        result = td.apply(pd.to_datetime, format="%b %y", cache=cache)
        tm.assert_series_equal(result, expected)

        td = pd.Series(["May 04", "Jun 02", ""], index=[1, 2, 3])
        msg = r"time data '' does not match format '%b %y' \(match\)"
        with pytest.raises(ValueError, match=msg):
            pd.to_datetime(td, format="%b %y", errors="raise", cache=cache)
        with pytest.raises(ValueError, match=msg):
            td.apply(pd.to_datetime, format="%b %y", errors="raise", cache=cache)
        expected = pd.to_datetime(td, format="%b %y", errors="coerce", cache=cache)

        result = td.apply(
            lambda x: pd.to_datetime(x, format="%b %y", errors="coerce", cache=cache)
        )
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_types(self, cache):

        # empty string
        result = to_datetime("", cache=cache)
        assert result is NaT

        result = to_datetime(["", ""], cache=cache)
        assert isna(result).all()

        # ints
        result = Timestamp(0)
        expected = to_datetime(0, cache=cache)
        assert result == expected

        # GH 3888 (strings)
        expected = to_datetime(["2012"], cache=cache)[0]
        result = to_datetime("2012", cache=cache)
        assert result == expected

        # array = ['2012','20120101','20120101 12:01:01']
        array = ["20120101", "20120101 12:01:01"]
        expected = list(to_datetime(array, cache=cache))
        result = [Timestamp(date_str) for date_str in array]
        tm.assert_almost_equal(result, expected)

        # currently fails ###
        # result = Timestamp('2012')
        # expected = to_datetime('2012')
        # assert result == expected

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_unprocessable_input(self, cache):
        # GH 4928
        # GH 21864
        result = to_datetime([1, "1"], errors="ignore", cache=cache)

        expected = Index(np.array([1, "1"], dtype="O"))
        tm.assert_equal(result, expected)
        msg = "invalid string coercion to datetime"
        with pytest.raises(TypeError, match=msg):
            to_datetime([1, "1"], errors="raise", cache=cache)

    def test_to_datetime_other_datetime64_units(self):
        # 5/25/2012
        scalar = np.int64(1337904000000000).view("M8[us]")
        as_obj = scalar.astype("O")

        index = DatetimeIndex([scalar])
        assert index[0] == scalar.astype("O")

        value = Timestamp(scalar)
        assert value == as_obj

    def test_to_datetime_list_of_integers(self):
        rng = date_range("1/1/2000", periods=20)
        rng = DatetimeIndex(rng.values)

        ints = list(rng.asi8)

        result = DatetimeIndex(ints)

        tm.assert_index_equal(rng, result)

    def test_to_datetime_overflow(self):
        # gh-17637
        # we are overflowing Timedelta range here

        with pytest.raises(OverflowError):
            date_range(start="1/1/1700", freq="B", periods=100000)

    @pytest.mark.parametrize("cache", [True, False])
    def test_string_na_nat_conversion(self, cache):
        # GH #999, #858

        strings = np.array(
            ["1/1/2000", "1/2/2000", np.nan, "1/4/2000, 12:34:56"], dtype=object
        )

        expected = np.empty(4, dtype="M8[ns]")
        for i, val in enumerate(strings):
            if isna(val):
                expected[i] = iNaT
            else:
                expected[i] = parse(val)

        result = tslib.array_to_datetime(strings)[0]
        tm.assert_almost_equal(result, expected)

        result2 = to_datetime(strings, cache=cache)
        assert isinstance(result2, DatetimeIndex)
        tm.assert_numpy_array_equal(result, result2.values)

        malformed = np.array(["1/100/2000", np.nan], dtype=object)

        # GH 10636, default is now 'raise'
        msg = r"Unknown string format:|day is out of range for month"
        with pytest.raises(ValueError, match=msg):
            to_datetime(malformed, errors="raise", cache=cache)

        result = to_datetime(malformed, errors="ignore", cache=cache)
        # GH 21864
        expected = Index(malformed)
        tm.assert_index_equal(result, expected)

        with pytest.raises(ValueError, match=msg):
            to_datetime(malformed, errors="raise", cache=cache)

        idx = ["a", "b", "c", "d", "e"]
        series = Series(
            ["1/1/2000", np.nan, "1/3/2000", np.nan, "1/5/2000"], index=idx, name="foo"
        )
        dseries = Series(
            [
                to_datetime("1/1/2000", cache=cache),
                np.nan,
                to_datetime("1/3/2000", cache=cache),
                np.nan,
                to_datetime("1/5/2000", cache=cache),
            ],
            index=idx,
            name="foo",
        )

        result = to_datetime(series, cache=cache)
        dresult = to_datetime(dseries, cache=cache)

        expected = Series(np.empty(5, dtype="M8[ns]"), index=idx)
        for i in range(5):
            x = series[i]
            if isna(x):
                expected[i] = pd.NaT
            else:
                expected[i] = to_datetime(x, cache=cache)

        tm.assert_series_equal(result, expected, check_names=False)
        assert result.name == "foo"

        tm.assert_series_equal(dresult, expected, check_names=False)
        assert dresult.name == "foo"

    @pytest.mark.parametrize(
        "dtype",
        [
            "datetime64[h]",
            "datetime64[m]",
            "datetime64[s]",
            "datetime64[ms]",
            "datetime64[us]",
            "datetime64[ns]",
        ],
    )
    @pytest.mark.parametrize("cache", [True, False])
    def test_dti_constructor_numpy_timeunits(self, cache, dtype):
        # GH 9114
        base = pd.to_datetime(
            ["2000-01-01T00:00", "2000-01-02T00:00", "NaT"], cache=cache
        )

        values = base.values.astype(dtype)

        tm.assert_index_equal(DatetimeIndex(values), base)
        tm.assert_index_equal(to_datetime(values, cache=cache), base)

    @pytest.mark.parametrize("cache", [True, False])
    def test_dayfirst(self, cache):
        # GH 5917
        arr = ["10/02/2014", "11/02/2014", "12/02/2014"]
        expected = DatetimeIndex(
            [datetime(2014, 2, 10), datetime(2014, 2, 11), datetime(2014, 2, 12)]
        )
        idx1 = DatetimeIndex(arr, dayfirst=True)
        idx2 = DatetimeIndex(np.array(arr), dayfirst=True)
        idx3 = to_datetime(arr, dayfirst=True, cache=cache)
        idx4 = to_datetime(np.array(arr), dayfirst=True, cache=cache)
        idx5 = DatetimeIndex(Index(arr), dayfirst=True)
        idx6 = DatetimeIndex(Series(arr), dayfirst=True)
        tm.assert_index_equal(expected, idx1)
        tm.assert_index_equal(expected, idx2)
        tm.assert_index_equal(expected, idx3)
        tm.assert_index_equal(expected, idx4)
        tm.assert_index_equal(expected, idx5)
        tm.assert_index_equal(expected, idx6)

    @pytest.mark.parametrize("klass", [DatetimeIndex, DatetimeArray])
    def test_to_datetime_dta_tz(self, klass):
        # GH#27733
        dti = date_range("2015-04-05", periods=3).rename("foo")
        expected = dti.tz_localize("UTC")

        obj = klass(dti)
        expected = klass(expected)

        result = to_datetime(obj, utc=True)
        tm.assert_equal(result, expected)


class TestGuessDatetimeFormat:
    @td.skip_if_not_us_locale
    def test_guess_datetime_format_for_array(self):
        expected_format = "%Y-%m-%d %H:%M:%S.%f"
        dt_string = datetime(2011, 12, 30, 0, 0, 0).strftime(expected_format)

        test_arrays = [
            np.array([dt_string, dt_string, dt_string], dtype="O"),
            np.array([np.nan, np.nan, dt_string], dtype="O"),
            np.array([dt_string, "random_string"], dtype="O"),
        ]

        for test_array in test_arrays:
            assert tools._guess_datetime_format_for_array(test_array) == expected_format

        format_for_string_of_nans = tools._guess_datetime_format_for_array(
            np.array([np.nan, np.nan, np.nan], dtype="O")
        )
        assert format_for_string_of_nans is None


class TestToDatetimeInferFormat:
    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_infer_datetime_format_consistent_format(self, cache):
        s = pd.Series(pd.date_range("20000101", periods=50, freq="H"))

        test_formats = ["%m-%d-%Y", "%m/%d/%Y %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%f"]

        for test_format in test_formats:
            s_as_dt_strings = s.apply(lambda x: x.strftime(test_format))

            with_format = pd.to_datetime(
                s_as_dt_strings, format=test_format, cache=cache
            )
            no_infer = pd.to_datetime(
                s_as_dt_strings, infer_datetime_format=False, cache=cache
            )
            yes_infer = pd.to_datetime(
                s_as_dt_strings, infer_datetime_format=True, cache=cache
            )

            # Whether the format is explicitly passed, it is inferred, or
            # it is not inferred, the results should all be the same
            tm.assert_series_equal(with_format, no_infer)
            tm.assert_series_equal(no_infer, yes_infer)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_infer_datetime_format_inconsistent_format(self, cache):
        s = pd.Series(
            np.array(
                ["01/01/2011 00:00:00", "01-02-2011 00:00:00", "2011-01-03T00:00:00"]
            )
        )

        # When the format is inconsistent, infer_datetime_format should just
        # fallback to the default parsing
        tm.assert_series_equal(
            pd.to_datetime(s, infer_datetime_format=False, cache=cache),
            pd.to_datetime(s, infer_datetime_format=True, cache=cache),
        )

        s = pd.Series(np.array(["Jan/01/2011", "Feb/01/2011", "Mar/01/2011"]))

        tm.assert_series_equal(
            pd.to_datetime(s, infer_datetime_format=False, cache=cache),
            pd.to_datetime(s, infer_datetime_format=True, cache=cache),
        )

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_infer_datetime_format_series_with_nans(self, cache):
        s = pd.Series(
            np.array(["01/01/2011 00:00:00", np.nan, "01/03/2011 00:00:00", np.nan])
        )
        tm.assert_series_equal(
            pd.to_datetime(s, infer_datetime_format=False, cache=cache),
            pd.to_datetime(s, infer_datetime_format=True, cache=cache),
        )

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_infer_datetime_format_series_start_with_nans(self, cache):
        s = pd.Series(
            np.array(
                [
                    np.nan,
                    np.nan,
                    "01/01/2011 00:00:00",
                    "01/02/2011 00:00:00",
                    "01/03/2011 00:00:00",
                ]
            )
        )

        tm.assert_series_equal(
            pd.to_datetime(s, infer_datetime_format=False, cache=cache),
            pd.to_datetime(s, infer_datetime_format=True, cache=cache),
        )

    @pytest.mark.parametrize(
        "tz_name, offset", [("UTC", 0), ("UTC-3", 180), ("UTC+3", -180)]
    )
    def test_infer_datetime_format_tz_name(self, tz_name, offset):
        # GH 33133
        s = pd.Series([f"2019-02-02 08:07:13 {tz_name}"])
        result = to_datetime(s, infer_datetime_format=True)
        expected = pd.Series(
            [pd.Timestamp("2019-02-02 08:07:13").tz_localize(pytz.FixedOffset(offset))]
        )
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("cache", [True, False])
    def test_to_datetime_iso8601_noleading_0s(self, cache):
        # GH 11871
        s = pd.Series(["2014-1-1", "2014-2-2", "2015-3-3"])
        expected = pd.Series(
            [
                pd.Timestamp("2014-01-01"),
                pd.Timestamp("2014-02-02"),
                pd.Timestamp("2015-03-03"),
            ]
        )
        tm.assert_series_equal(pd.to_datetime(s, cache=cache), expected)
        tm.assert_series_equal(
            pd.to_datetime(s, format="%Y-%m-%d", cache=cache), expected
        )


class TestDaysInMonth:
    # tests for issue #10154

    @pytest.mark.parametrize("cache", [True, False])
    def test_day_not_in_month_coerce(self, cache):
        assert isna(to_datetime("2015-02-29", errors="coerce", cache=cache))
        assert isna(
            to_datetime("2015-02-29", format="%Y-%m-%d", errors="coerce", cache=cache)
        )
        assert isna(
            to_datetime("2015-02-32", format="%Y-%m-%d", errors="coerce", cache=cache)
        )
        assert isna(
            to_datetime("2015-04-31", format="%Y-%m-%d", errors="coerce", cache=cache)
        )

    @pytest.mark.parametrize("cache", [True, False])
    def test_day_not_in_month_raise(self, cache):
        msg = "day is out of range for month"
        with pytest.raises(ValueError, match=msg):
            to_datetime("2015-02-29", errors="raise", cache=cache)

        msg = "time data 2015-02-29 doesn't match format specified"
        with pytest.raises(ValueError, match=msg):
            to_datetime("2015-02-29", errors="raise", format="%Y-%m-%d", cache=cache)

        msg = "time data 2015-02-32 doesn't match format specified"
        with pytest.raises(ValueError, match=msg):
            to_datetime("2015-02-32", errors="raise", format="%Y-%m-%d", cache=cache)

        msg = "time data 2015-04-31 doesn't match format specified"
        with pytest.raises(ValueError, match=msg):
            to_datetime("2015-04-31", errors="raise", format="%Y-%m-%d", cache=cache)

    @pytest.mark.parametrize("cache", [True, False])
    def test_day_not_in_month_ignore(self, cache):
        assert to_datetime("2015-02-29", errors="ignore", cache=cache) == "2015-02-29"
        assert (
            to_datetime("2015-02-29", errors="ignore", format="%Y-%m-%d", cache=cache)
            == "2015-02-29"
        )
        assert (
            to_datetime("2015-02-32", errors="ignore", format="%Y-%m-%d", cache=cache)
            == "2015-02-32"
        )
        assert (
            to_datetime("2015-04-31", errors="ignore", format="%Y-%m-%d", cache=cache)
            == "2015-04-31"
        )


class TestDatetimeParsingWrappers:
    @pytest.mark.parametrize(
        "date_str,expected",
        list(
            {
                "2011-01-01": datetime(2011, 1, 1),
                "2Q2005": datetime(2005, 4, 1),
                "2Q05": datetime(2005, 4, 1),
                "2005Q1": datetime(2005, 1, 1),
                "05Q1": datetime(2005, 1, 1),
                "2011Q3": datetime(2011, 7, 1),
                "11Q3": datetime(2011, 7, 1),
                "3Q2011": datetime(2011, 7, 1),
                "3Q11": datetime(2011, 7, 1),
                # quarterly without space
                "2000Q4": datetime(2000, 10, 1),
                "00Q4": datetime(2000, 10, 1),
                "4Q2000": datetime(2000, 10, 1),
                "4Q00": datetime(2000, 10, 1),
                "2000q4": datetime(2000, 10, 1),
                "2000-Q4": datetime(2000, 10, 1),
                "00-Q4": datetime(2000, 10, 1),
                "4Q-2000": datetime(2000, 10, 1),
                "4Q-00": datetime(2000, 10, 1),
                "00q4": datetime(2000, 10, 1),
                "2005": datetime(2005, 1, 1),
                "2005-11": datetime(2005, 11, 1),
                "2005 11": datetime(2005, 11, 1),
                "11-2005": datetime(2005, 11, 1),
                "11 2005": datetime(2005, 11, 1),
                "200511": datetime(2020, 5, 11),
                "20051109": datetime(2005, 11, 9),
                "20051109 10:15": datetime(2005, 11, 9, 10, 15),
                "20051109 08H": datetime(2005, 11, 9, 8, 0),
                "2005-11-09 10:15": datetime(2005, 11, 9, 10, 15),
                "2005-11-09 08H": datetime(2005, 11, 9, 8, 0),
                "2005/11/09 10:15": datetime(2005, 11, 9, 10, 15),
                "2005/11/09 08H": datetime(2005, 11, 9, 8, 0),
                "Thu Sep 25 10:36:28 2003": datetime(2003, 9, 25, 10, 36, 28),
                "Thu Sep 25 2003": datetime(2003, 9, 25),
                "Sep 25 2003": datetime(2003, 9, 25),
                "January 1 2014": datetime(2014, 1, 1),
                # GHE10537
                "2014-06": datetime(2014, 6, 1),
                "06-2014": datetime(2014, 6, 1),
                "2014-6": datetime(2014, 6, 1),
                "6-2014": datetime(2014, 6, 1),
                "20010101 12": datetime(2001, 1, 1, 12),
                "20010101 1234": datetime(2001, 1, 1, 12, 34),
                "20010101 123456": datetime(2001, 1, 1, 12, 34, 56),
            }.items()
        ),
    )
    @pytest.mark.parametrize("cache", [True, False])
    def test_parsers(self, date_str, expected, cache):

        # dateutil >= 2.5.0 defaults to yearfirst=True
        # https://github.com/dateutil/dateutil/issues/217
        yearfirst = True

        result1, _ = parsing.parse_time_string(date_str, yearfirst=yearfirst)
        result2 = to_datetime(date_str, yearfirst=yearfirst)
        result3 = to_datetime([date_str], yearfirst=yearfirst)
        # result5 is used below
        result4 = to_datetime(
            np.array([date_str], dtype=object), yearfirst=yearfirst, cache=cache
        )
        result6 = DatetimeIndex([date_str], yearfirst=yearfirst)
        # result7 is used below
        result8 = DatetimeIndex(Index([date_str]), yearfirst=yearfirst)
        result9 = DatetimeIndex(Series([date_str]), yearfirst=yearfirst)

        for res in [result1, result2]:
            assert res == expected
        for res in [result3, result4, result6, result8, result9]:
            exp = DatetimeIndex([pd.Timestamp(expected)])
            tm.assert_index_equal(res, exp)

        # these really need to have yearfirst, but we don't support
        if not yearfirst:
            result5 = Timestamp(date_str)
            assert result5 == expected
            result7 = date_range(date_str, freq="S", periods=1, yearfirst=yearfirst)
            assert result7 == expected

    @pytest.mark.parametrize("cache", [True, False])
    def test_na_values_with_cache(
        self, cache, unique_nulls_fixture, unique_nulls_fixture2
    ):
        # GH22305
        expected = Index([NaT, NaT], dtype="datetime64[ns]")
        result = to_datetime([unique_nulls_fixture, unique_nulls_fixture2], cache=cache)
        tm.assert_index_equal(result, expected)

    def test_parsers_nat(self):
        # Test that each of several string-accepting methods return pd.NaT
        result1, _ = parsing.parse_time_string("NaT")
        result2 = to_datetime("NaT")
        result3 = Timestamp("NaT")
        result4 = DatetimeIndex(["NaT"])[0]
        assert result1 is NaT
        assert result2 is NaT
        assert result3 is NaT
        assert result4 is NaT

    @pytest.mark.parametrize("cache", [True, False])
    def test_parsers_dayfirst_yearfirst(self, cache):
        # OK
        # 2.5.1 10-11-12   [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00
        # 2.5.2 10-11-12   [dayfirst=0, yearfirst=1] -> 2012-10-11 00:00:00
        # 2.5.3 10-11-12   [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00

        # OK
        # 2.5.1 10-11-12   [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00
        # 2.5.2 10-11-12   [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00
        # 2.5.3 10-11-12   [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00

        # bug fix in 2.5.2
        # 2.5.1 10-11-12   [dayfirst=1, yearfirst=1] -> 2010-11-12 00:00:00
        # 2.5.2 10-11-12   [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00
        # 2.5.3 10-11-12   [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00

        # OK
        # 2.5.1 10-11-12   [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00
        # 2.5.2 10-11-12   [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00
        # 2.5.3 10-11-12   [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00

        # OK
        # 2.5.1 20/12/21   [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00
        # 2.5.2 20/12/21   [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00
        # 2.5.3 20/12/21   [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00

        # OK
        # 2.5.1 20/12/21   [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00
        # 2.5.2 20/12/21   [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00
        # 2.5.3 20/12/21   [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00

        # revert of bug in 2.5.2
        # 2.5.1 20/12/21   [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00
        # 2.5.2 20/12/21   [dayfirst=1, yearfirst=1] -> month must be in 1..12
        # 2.5.3 20/12/21   [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00

        # OK
        # 2.5.1 20/12/21   [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00
        # 2.5.2 20/12/21   [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00
        # 2.5.3 20/12/21   [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00

        # str : dayfirst, yearfirst, expected
        cases = {
            "10-11-12": [
                (False, False, datetime(2012, 10, 11)),
                (True, False, datetime(2012, 11, 10)),
                (False, True, datetime(2010, 11, 12)),
                (True, True, datetime(2010, 12, 11)),
            ],
            "20/12/21": [
                (False, False, datetime(2021, 12, 20)),
                (True, False, datetime(2021, 12, 20)),
                (False, True, datetime(2020, 12, 21)),
                (True, True, datetime(2020, 12, 21)),
            ],
        }

        for date_str, values in cases.items():
            for dayfirst, yearfirst, expected in values:

                # compare with dateutil result
                dateutil_result = parse(
                    date_str, dayfirst=dayfirst, yearfirst=yearfirst
                )
                assert dateutil_result == expected

                result1, _ = parsing.parse_time_string(
                    date_str, dayfirst=dayfirst, yearfirst=yearfirst
                )

                # we don't support dayfirst/yearfirst here:
                if not dayfirst and not yearfirst:
                    result2 = Timestamp(date_str)
                    assert result2 == expected

                result3 = to_datetime(
                    date_str, dayfirst=dayfirst, yearfirst=yearfirst, cache=cache
                )

                result4 = DatetimeIndex(
                    [date_str], dayfirst=dayfirst, yearfirst=yearfirst
                )[0]

                assert result1 == expected
                assert result3 == expected
                assert result4 == expected

    @pytest.mark.parametrize("cache", [True, False])
    def test_parsers_timestring(self, cache):
        # must be the same as dateutil result
        cases = {
            "10:15": (parse("10:15"), datetime(1, 1, 1, 10, 15)),
            "9:05": (parse("9:05"), datetime(1, 1, 1, 9, 5)),
        }

        for date_str, (exp_now, exp_def) in cases.items():
            result1, _ = parsing.parse_time_string(date_str)
            result2 = to_datetime(date_str)
            result3 = to_datetime([date_str])
            result4 = Timestamp(date_str)
            result5 = DatetimeIndex([date_str])[0]
            # parse time string return time string based on default date
            # others are not, and can't be changed because it is used in
            # time series plot
            assert result1 == exp_def
            assert result2 == exp_now
            assert result3 == exp_now
            assert result4 == exp_now
            assert result5 == exp_now

    @pytest.mark.parametrize("cache", [True, False])
    @pytest.mark.parametrize(
        "dt_string, tz, dt_string_repr",
        [
            (
                "2013-01-01 05:45+0545",
                pytz.FixedOffset(345),
                "Timestamp('2013-01-01 05:45:00+0545', tz='pytz.FixedOffset(345)')",
            ),
            (
                "2013-01-01 05:30+0530",
                pytz.FixedOffset(330),
                "Timestamp('2013-01-01 05:30:00+0530', tz='pytz.FixedOffset(330)')",
            ),
        ],
    )
    def test_parsers_timezone_minute_offsets_roundtrip(
        self, cache, dt_string, tz, dt_string_repr
    ):
        # GH11708
        base = to_datetime("2013-01-01 00:00:00", cache=cache)
        base = base.tz_localize("UTC").tz_convert(tz)
        dt_time = to_datetime(dt_string, cache=cache)
        assert base == dt_time
        assert dt_string_repr == repr(dt_time)


@pytest.fixture(params=["D", "s", "ms", "us", "ns"])
def units(request):
    """Day and some time units.

    * D
    * s
    * ms
    * us
    * ns
    """
    return request.param


@pytest.fixture
def epoch_1960():
    """Timestamp at 1960-01-01."""
    return Timestamp("1960-01-01")


@pytest.fixture
def units_from_epochs():
    return list(range(5))


@pytest.fixture(params=["timestamp", "pydatetime", "datetime64", "str_1960"])
def epochs(epoch_1960, request):
    """Timestamp at 1960-01-01 in various forms.

    * pd.Timestamp
    * datetime.datetime
    * numpy.datetime64
    * str
    """
    assert request.param in {"timestamp", "pydatetime", "datetime64", "str_1960"}
    if request.param == "timestamp":
        return epoch_1960
    elif request.param == "pydatetime":
        return epoch_1960.to_pydatetime()
    elif request.param == "datetime64":
        return epoch_1960.to_datetime64()
    else:
        return str(epoch_1960)


@pytest.fixture
def julian_dates():
    return pd.date_range("2014-1-1", periods=10).to_julian_date().values


class TestOrigin:
    def test_to_basic(self, julian_dates):
        # gh-11276, gh-11745
        # for origin as julian

        result = Series(pd.to_datetime(julian_dates, unit="D", origin="julian"))
        expected = Series(
            pd.to_datetime(julian_dates - pd.Timestamp(0).to_julian_date(), unit="D")
        )
        tm.assert_series_equal(result, expected)

        result = Series(pd.to_datetime([0, 1, 2], unit="D", origin="unix"))
        expected = Series(
            [Timestamp("1970-01-01"), Timestamp("1970-01-02"), Timestamp("1970-01-03")]
        )
        tm.assert_series_equal(result, expected)

        # default
        result = Series(pd.to_datetime([0, 1, 2], unit="D"))
        expected = Series(
            [Timestamp("1970-01-01"), Timestamp("1970-01-02"), Timestamp("1970-01-03")]
        )
        tm.assert_series_equal(result, expected)

    def test_julian_round_trip(self):
        result = pd.to_datetime(2456658, origin="julian", unit="D")
        assert result.to_julian_date() == 2456658

        # out-of-bounds
        with pytest.raises(ValueError):
            pd.to_datetime(1, origin="julian", unit="D")

    def test_invalid_unit(self, units, julian_dates):

        # checking for invalid combination of origin='julian' and unit != D
        if units != "D":
            with pytest.raises(ValueError):
                pd.to_datetime(julian_dates, unit=units, origin="julian")

    def test_invalid_origin(self):

        # need to have a numeric specified
        with pytest.raises(ValueError):
            pd.to_datetime("2005-01-01", origin="1960-01-01")

        with pytest.raises(ValueError):
            pd.to_datetime("2005-01-01", origin="1960-01-01", unit="D")

    def test_epoch(self, units, epochs, epoch_1960, units_from_epochs):

        expected = Series(
            [pd.Timedelta(x, unit=units) + epoch_1960 for x in units_from_epochs]
        )

        result = Series(pd.to_datetime(units_from_epochs, unit=units, origin=epochs))
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "origin, exc",
        [
            ("random_string", ValueError),
            ("epoch", ValueError),
            ("13-24-1990", ValueError),
            (datetime(1, 1, 1), tslib.OutOfBoundsDatetime),
        ],
    )
    def test_invalid_origins(self, origin, exc, units, units_from_epochs):

        with pytest.raises(exc):
            pd.to_datetime(units_from_epochs, unit=units, origin=origin)

    def test_invalid_origins_tzinfo(self):
        # GH16842
        with pytest.raises(ValueError):
            pd.to_datetime(1, unit="D", origin=datetime(2000, 1, 1, tzinfo=pytz.utc))

    @pytest.mark.parametrize("format", [None, "%Y-%m-%d %H:%M:%S"])
    def test_to_datetime_out_of_bounds_with_format_arg(self, format):
        # see gh-23830
        msg = "Out of bounds nanosecond timestamp"
        with pytest.raises(OutOfBoundsDatetime, match=msg):
            to_datetime("2417-10-27 00:00:00", format=format)

    def test_processing_order(self):
        # make sure we handle out-of-bounds *before*
        # constructing the dates

        result = pd.to_datetime(200 * 365, unit="D")
        expected = Timestamp("2169-11-13 00:00:00")
        assert result == expected

        result = pd.to_datetime(200 * 365, unit="D", origin="1870-01-01")
        expected = Timestamp("2069-11-13 00:00:00")
        assert result == expected

        result = pd.to_datetime(300 * 365, unit="D", origin="1870-01-01")
        expected = Timestamp("2169-10-20 00:00:00")
        assert result == expected

    @pytest.mark.parametrize(
        "offset,utc,exp",
        [
            ["Z", True, "2019-01-01T00:00:00.000Z"],
            ["Z", None, "2019-01-01T00:00:00.000Z"],
            ["-01:00", True, "2019-01-01T01:00:00.000Z"],
            ["-01:00", None, "2019-01-01T00:00:00.000-01:00"],
        ],
    )
    def test_arg_tz_ns_unit(self, offset, utc, exp):
        # GH 25546
        arg = "2019-01-01T00:00:00.000" + offset
        result = to_datetime([arg], unit="ns", utc=utc)
        expected = to_datetime([exp])
        tm.assert_index_equal(result, expected)


@pytest.mark.parametrize(
    "listlike,do_caching",
    [([1, 2, 3, 4, 5, 6, 7, 8, 9, 0], False), ([1, 1, 1, 1, 4, 5, 6, 7, 8, 9], True)],
)
def test_should_cache(listlike, do_caching):
    assert (
        tools.should_cache(listlike, check_count=len(listlike), unique_share=0.7)
        == do_caching
    )


@pytest.mark.parametrize(
    "unique_share,check_count, err_message",
    [
        (0.5, 11, r"check_count must be in next bounds: \[0; len\(arg\)\]"),
        (10, 2, r"unique_share must be in next bounds: \(0; 1\)"),
    ],
)
def test_should_cache_errors(unique_share, check_count, err_message):
    arg = [5] * 10

    with pytest.raises(AssertionError, match=err_message):
        tools.should_cache(arg, unique_share, check_count)


def test_nullable_integer_to_datetime():
    # Test for #30050
    ser = pd.Series([1, 2, None, 2 ** 61, None])
    ser = ser.astype("Int64")
    ser_copy = ser.copy()

    res = pd.to_datetime(ser, unit="ns")

    expected = pd.Series(
        [
            np.datetime64("1970-01-01 00:00:00.000000001"),
            np.datetime64("1970-01-01 00:00:00.000000002"),
            np.datetime64("NaT"),
            np.datetime64("2043-01-25 23:56:49.213693952"),
            np.datetime64("NaT"),
        ]
    )
    tm.assert_series_equal(res, expected)
    # Check that ser isn't mutated
    tm.assert_series_equal(ser, ser_copy)


@pytest.mark.parametrize("klass", [np.array, list])
def test_na_to_datetime(nulls_fixture, klass):
    result = pd.to_datetime(klass([nulls_fixture]))

    assert result[0] is pd.NaT