test_tools.py

""" test to_datetime """

import sys
import pytz
import pytest
import locale
import calendar
import dateutil
import numpy as np
from dateutil.parser import parse
from datetime import datetime, date, time, timedelta
from distutils.version import LooseVersion

import pandas as pd
from pandas.conftest import is_dateutil_le_261, is_dateutil_gt_261
from pandas._libs import tslib
from pandas._libs.tslibs import parsing
from pandas.core.tools import datetimes as tools

from pandas.errors import OutOfBoundsDatetime
from pandas.compat import lmap, PY3
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import is_datetime64_ns_dtype
from pandas.util import testing as tm
import pandas.util._test_decorators as td
from pandas.util.testing import assert_series_equal
from pandas import (isna, to_datetime, Timestamp, Series, DataFrame,
                    Index, DatetimeIndex, NaT, date_range, compat)


class TestTimeConversionFormats(object):

    @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):
                    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)
        assert_series_equal(result, expected)

        result = to_datetime(s.apply(str), format='%Y%m%d', cache=cache)
        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)
        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)
        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]')
        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)
        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)
        assert_series_equal(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 = '01-{}-2011 00:00:01.978'.format(month_abbr)

        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
        if sys.version_info < (2, 7):
            pytest.skip('on python version < 2.7')

        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)
        assert_series_equal(result, expected)

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

        # GH8989
        # trunctaing 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


class TestToDatetime(object):

    @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_ alawys 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]')
            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.value - nptoday.astype(np.int64)) < 1e10
            assert abs(pdtoday2.value - nptoday.astype(np.int64)) < 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]')
            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.value - nptoday.astype(np.int64)) < 1e10
            assert abs(pdtoday2.value - nptoday.astype(np.int64)) < 1e10

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

    def test_to_datetime_today_now_unicode_bytes(self):
        to_datetime([u'now'])
        to_datetime([u'today'])
        if not PY3:
            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)

        oob_dts = [np.datetime64('1000-01-01'), np.datetime64('5000-01-02'), ]

        for dt in oob_dts:
            pytest.raises(ValueError, pd.to_datetime, dt, errors='raise')
            pytest.raises(ValueError, Timestamp, dt)
            assert pd.to_datetime(dt, errors='coerce', cache=cache) is NaT

    @pytest.mark.parametrize('cache', [True, False])
    def test_to_datetime_array_of_dt64s(self, cache):
        dts = [np.datetime64('2000-01-01'), np.datetime64('2000-01-02'), ]

        # Assuming all datetimes are in bounds, to_datetime() returns
        # an array that is equal to Timestamp() parsing
        tm.assert_numpy_array_equal(
            pd.to_datetime(dts, box=False, cache=cache),
            np.array([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')]

        pytest.raises(ValueError, pd.to_datetime, dts_with_oob,
                      errors='raise')

        tm.assert_numpy_array_equal(
            pd.to_datetime(dts_with_oob, box=False, errors='coerce',
                           cache=cache),
            np.array(
                [
                    Timestamp(dts_with_oob[0]).asm8,
                    Timestamp(dts_with_oob[1]).asm8,
                    tslib.iNaT,
                ],
                dtype='M8'
            )
        )

        # 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_numpy_array_equal(
            pd.to_datetime(dts_with_oob, box=False, errors='ignore',
                           cache=cache),
            np.array(
                [dt.item() for dt in dts_with_oob],
                dtype='O'
            )
        )

    @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')]
        pytest.raises(ValueError, lambda: pd.to_datetime(arr, cache=cache))

    @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])
    def test_to_datetime_tz_psycopg2(self, cache):

        # xref 8260
        try:
            import psycopg2
        except ImportError:
            pytest.skip("no psycopg2 installed")

        # 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+00:00'
        ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None))
        assert is_datetime64_ns_dtype(i)

        # tz coerceion
        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',
        [pytest.param(True,
                      marks=pytest.mark.skipif(True, reason="GH 18111")),
         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("utc", [True, None])
    @pytest.mark.parametrize("format", ['%Y%m%d %H:%M:%S', None])
    @pytest.mark.parametrize("box", [True, False])
    @pytest.mark.parametrize("constructor", [list, tuple, np.array, pd.Index])
    def test_to_datetime_cache(self, utc, format, box, constructor):
        date = '20130101 00:00:00'
        test_dates = [date] * 10**5
        data = constructor(test_dates)
        result = pd.to_datetime(data, utc=utc, format=format, box=box,
                                cache=True)
        expected = pd.to_datetime(data, utc=utc, format=format, box=box,
                                  cache=False)
        if box:
            tm.assert_index_equal(result, expected)
        else:
            tm.assert_numpy_array_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 tm.assert_raises_regex(ValueError, msg):
            pd.to_datetime(date, format=format)


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

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

        values = [11111111, 1, 1.0, tslib.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, tslib.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:
                raise AssertionError("incorrect exception raised")
            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 = [1.434692e+18, 1.432766e+18]
        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', 1.434692e+18, 1.432766e+18]
        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 = [1.434692e+18, 1.432766e+18, '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'), 1.434692e+18, 1.432766e+18]
        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 = [1.434692e+18, 1.432766e+18, 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_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')])
        assert_series_equal(result, expected)

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

        # coerce back to int
        result = to_datetime(df.astype(str), cache=cache)
        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 tm.assert_raises_regex(ValueError, msg):
            to_datetime(df2, cache=cache)
        result = to_datetime(df2, errors='coerce', cache=cache)
        expected = Series([Timestamp('20150204 00:00:00'),
                           NaT])
        assert_series_equal(result, expected)

        # extra columns
        msg = ("extra keys have been passed to the datetime assemblage: "
               r"\[foo\]")
        with tm.assert_raises_regex(ValueError, 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 tm.assert_raises_regex(ValueError, 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 tm.assert_raises_regex(ValueError, 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 tm.assert_raises_regex(ValueError, 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')])
        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')])
        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)


class TestToDatetimeMisc(object):
    @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', ' '])
        pytest.raises(ValueError, lambda: 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)
        assert_series_equal(result, expected)

        td = pd.Series(['May 04', 'Jun 02', ''], index=[1, 2, 3])
        pytest.raises(ValueError,
                      lambda: pd.to_datetime(td, format='%b %y',
                                             errors='raise',
                                             cache=cache))
        pytest.raises(ValueError,
                      lambda: 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))
        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 = lmap(Timestamp, 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
        tm.assert_numpy_array_equal(
            to_datetime([1, '1'], errors='ignore', cache=cache),
            np.array([1, '1'], dtype='O')
        )
        pytest.raises(TypeError, 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

        from pandas.compat import parse_date

        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] = tslib.iNaT
            else:
                expected[i] = parse_date(val)

        result = tslib.array_to_datetime(strings)
        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'
        pytest.raises(ValueError,
                      lambda: to_datetime(malformed, errors='raise',
                                          cache=cache))

        result = to_datetime(malformed, errors='ignore', cache=cache)
        tm.assert_numpy_array_equal(result, malformed)

        pytest.raises(ValueError, 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] = tslib.iNaT
            else:
                expected[i] = to_datetime(x, cache=cache)

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

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

    @pytest.mark.parametrize('cache', [True, False])
    def test_dti_constructor_numpy_timeunits(self, cache):
        # GH 9114
        base = pd.to_datetime(['2000-01-01T00:00', '2000-01-02T00:00', 'NaT'],
                              cache=cache)

        for dtype in ['datetime64[h]', 'datetime64[m]', 'datetime64[s]',
                      'datetime64[ms]', 'datetime64[us]', 'datetime64[ns]']:
            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)


class TestGuessDatetimeFormat(object):

    @td.skip_if_not_us_locale
    @is_dateutil_le_261
    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

    @td.skip_if_not_us_locale
    @is_dateutil_gt_261
    def test_guess_datetime_format_for_array_gt_261(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) is None

        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(object):

    @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('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(object):
    # 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):
        pytest.raises(ValueError, to_datetime, '2015-02-29',
                      errors='raise', cache=cache)
        pytest.raises(ValueError, to_datetime, '2015-02-29',
                      errors='raise', format="%Y-%m-%d", cache=cache)
        pytest.raises(ValueError, to_datetime, '2015-02-32',
                      errors='raise', format="%Y-%m-%d", cache=cache)
        pytest.raises(ValueError, 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(object):

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

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

        cases = {'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),

                 # GH 10537
                 '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),
                 }

        for date_str, expected in compat.iteritems(cases):
            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

        # NaT
        result1, _, _ = parsing.parse_time_string('NaT')
        result2 = to_datetime('NaT')
        result3 = Timestamp('NaT')
        result4 = DatetimeIndex(['NaT'])[0]
        assert result1 is tslib.NaT
        assert result2 is tslib.NaT
        assert result3 is tslib.NaT
        assert result4 is tslib.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

        is_lt_253 = LooseVersion(dateutil.__version__) < LooseVersion('2.5.3')

        # 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 compat.iteritems(cases):
            for dayfirst, yearfirst, expected in values:

                # odd comparisons across version
                # let's just skip
                if dayfirst and yearfirst and is_lt_253:
                    continue

                # 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 compat.iteritems(cases):
            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

    @td.skip_if_has_locale
    def test_parsers_time(self):
        # GH11818
        strings = ["14:15", "1415", "2:15pm", "0215pm", "14:15:00", "141500",
                   "2:15:00pm", "021500pm", time(14, 15)]
        expected = time(14, 15)

        for time_string in strings:
            assert tools.to_time(time_string) == expected

        new_string = "14.15"
        pytest.raises(ValueError, tools.to_time, new_string)
        assert tools.to_time(new_string, format="%H.%M") == expected

        arg = ["14:15", "20:20"]
        expected_arr = [time(14, 15), time(20, 20)]
        assert tools.to_time(arg) == expected_arr
        assert tools.to_time(arg, format="%H:%M") == expected_arr
        assert tools.to_time(arg, infer_time_format=True) == expected_arr
        assert tools.to_time(arg, format="%I:%M%p",
                             errors="coerce") == [None, None]

        res = tools.to_time(arg, format="%I:%M%p", errors="ignore")
        tm.assert_numpy_array_equal(res, np.array(arg, dtype=np.object_))

        with pytest.raises(ValueError):
            tools.to_time(arg, format="%I:%M%p", errors="raise")

        tm.assert_series_equal(tools.to_time(Series(arg, name="test")),
                               Series(expected_arr, name="test"))

        res = tools.to_time(np.array(arg))
        assert isinstance(res, list)
        assert res == expected_arr

    @pytest.mark.parametrize('cache', [True, False])
    def test_parsers_timezone_minute_offsets_roundtrip(self, cache):
        # GH11708
        base = to_datetime("2013-01-01 00:00:00", cache=cache)
        dt_strings = [
            ('2013-01-01 05:45+0545',
             "Asia/Katmandu",
             "Timestamp('2013-01-01 05:45:00+0545', tz='Asia/Katmandu')"),
            ('2013-01-01 05:30+0530',
             "Asia/Kolkata",
             "Timestamp('2013-01-01 05:30:00+0530', tz='Asia/Kolkata')")
        ]

        for dt_string, tz, dt_string_repr in dt_strings:
            dt_time = to_datetime(dt_string, cache=cache)
            assert base == dt_time
            converted_time = dt_time.tz_localize('UTC').tz_convert(tz)
            assert dt_string_repr == repr(converted_time)

    def test_parsers_iso8601(self):
        # GH 12060
        # test only the iso parser - flexibility to different
        # separators and leadings 0s
        # Timestamp construction falls back to dateutil
        cases = {'2011-01-02': datetime(2011, 1, 2),
                 '2011-1-2': datetime(2011, 1, 2),
                 '2011-01': datetime(2011, 1, 1),
                 '2011-1': datetime(2011, 1, 1),
                 '2011 01 02': datetime(2011, 1, 2),
                 '2011.01.02': datetime(2011, 1, 2),
                 '2011/01/02': datetime(2011, 1, 2),
                 '2011\\01\\02': datetime(2011, 1, 2),
                 '2013-01-01 05:30:00': datetime(2013, 1, 1, 5, 30),
                 '2013-1-1 5:30:00': datetime(2013, 1, 1, 5, 30)}
        for date_str, exp in compat.iteritems(cases):
            actual = tslib._test_parse_iso8601(date_str)
            assert actual == exp

        # separators must all match - YYYYMM not valid
        invalid_cases = ['2011-01/02', '2011^11^11',
                         '201401', '201111', '200101',
                         # mixed separated and unseparated
                         '2005-0101', '200501-01',
                         '20010101 12:3456', '20010101 1234:56',
                         # HHMMSS must have two digits in each component
                         # if unseparated
                         '20010101 1', '20010101 123', '20010101 12345',
                         '20010101 12345Z',
                         # wrong separator for HHMMSS
                         '2001-01-01 12-34-56']
        for date_str in invalid_cases:
            with pytest.raises(ValueError):
                tslib._test_parse_iso8601(date_str)
                # If no ValueError raised, let me know which case failed.
                raise Exception(date_str)


class TestArrayToDatetime(object):
    def test_coerce_out_of_bounds_utc(self):
        # GH#19612
        ts = Timestamp('1900-01-01', tz='US/Pacific')
        dt = ts.to_pydatetime() - timedelta(days=365 * 300)  # ~1600AD
        arr = np.array([dt])
        result = tslib.array_to_datetime(arr, utc=True, errors='coerce')
        expected = np.array(['NaT'], dtype='datetime64[ns]')
        tm.assert_numpy_array_equal(result, expected)

    def test_parsing_valid_dates(self):
        arr = np.array(['01-01-2013', '01-02-2013'], dtype=object)
        tm.assert_numpy_array_equal(
            tslib.array_to_datetime(arr),
            np_array_datetime64_compat(
                [
                    '2013-01-01T00:00:00.000000000-0000',
                    '2013-01-02T00:00:00.000000000-0000'
                ],
                dtype='M8[ns]'
            )
        )

        arr = np.array(['Mon Sep 16 2013', 'Tue Sep 17 2013'], dtype=object)
        tm.assert_numpy_array_equal(
            tslib.array_to_datetime(arr),
            np_array_datetime64_compat(
                [
                    '2013-09-16T00:00:00.000000000-0000',
                    '2013-09-17T00:00:00.000000000-0000'
                ],
                dtype='M8[ns]'
            )
        )

    def test_parsing_timezone_offsets(self):
        # All of these datetime strings with offsets are equivalent
        # to the same datetime after the timezone offset is added
        dt_strings = [
            '01-01-2013 08:00:00+08:00',
            '2013-01-01T08:00:00.000000000+0800',
            '2012-12-31T16:00:00.000000000-0800',
            '12-31-2012 23:00:00-01:00'
        ]

        expected_output = tslib.array_to_datetime(np.array(
            ['01-01-2013 00:00:00'], dtype=object))

        for dt_string in dt_strings:
            tm.assert_numpy_array_equal(
                tslib.array_to_datetime(
                    np.array([dt_string], dtype=object)
                ),
                expected_output
            )

    def test_number_looking_strings_not_into_datetime(self):
        # #4601
        # These strings don't look like datetimes so they shouldn't be
        # attempted to be converted
        arr = np.array(['-352.737091', '183.575577'], dtype=object)
        tm.assert_numpy_array_equal(
            tslib.array_to_datetime(arr, errors='ignore'), arr)

        arr = np.array(['1', '2', '3', '4', '5'], dtype=object)
        tm.assert_numpy_array_equal(
            tslib.array_to_datetime(arr, errors='ignore'), arr)

    def test_coercing_dates_outside_of_datetime64_ns_bounds(self):
        invalid_dates = [
            date(1000, 1, 1),
            datetime(1000, 1, 1),
            '1000-01-01',
            'Jan 1, 1000',
            np.datetime64('1000-01-01'),
        ]

        for invalid_date in invalid_dates:
            pytest.raises(ValueError,
                          tslib.array_to_datetime,
                          np.array([invalid_date], dtype='object'),
                          errors='raise', )
            tm.assert_numpy_array_equal(
                tslib.array_to_datetime(
                    np.array([invalid_date], dtype='object'),
                    errors='coerce'),
                np.array([tslib.iNaT], dtype='M8[ns]')
            )

        arr = np.array(['1/1/1000', '1/1/2000'], dtype=object)
        tm.assert_numpy_array_equal(
            tslib.array_to_datetime(arr, errors='coerce'),
            np_array_datetime64_compat(
                [
                    tslib.iNaT,
                    '2000-01-01T00:00:00.000000000-0000'
                ],
                dtype='M8[ns]'
            )
        )

    def test_coerce_of_invalid_datetimes(self):
        arr = np.array(['01-01-2013', 'not_a_date', '1'], dtype=object)

        # Without coercing, the presence of any invalid dates prevents
        # any values from being converted
        tm.assert_numpy_array_equal(
            tslib.array_to_datetime(arr, errors='ignore'), arr)

        # With coercing, the invalid dates becomes iNaT
        tm.assert_numpy_array_equal(
            tslib.array_to_datetime(arr, errors='coerce'),
            np_array_datetime64_compat(
                [
                    '2013-01-01T00:00:00.000000000-0000',
                    tslib.iNaT,
                    tslib.iNaT
                ],
                dtype='M8[ns]'
            )
        )

    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)

        with pytest.raises(OutOfBoundsDatetime):
            # Essentially the same as above, but more directly calling
            # the relevant function
            tslib.array_to_datetime(arr)


def test_normalize_date():
    value = date(2012, 9, 7)

    result = tslib.normalize_date(value)
    assert (result == datetime(2012, 9, 7))

    value = datetime(2012, 9, 7, 12)

    result = tslib.normalize_date(value)
    assert (result == datetime(2012, 9, 7))


@pytest.fixture(params=['D', 's', 'ms', 'us', 'ns'])
def units(request):
    return request.param


@pytest.fixture
def epoch_1960():
    # for origin as 1960-01-01
    return Timestamp('1960-01-01')


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


@pytest.fixture(params=[epoch_1960(),
                        epoch_1960().to_pydatetime(),
                        epoch_1960().to_datetime64(),
                        str(epoch_1960())])
def epochs(request):
    return request.param


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


class TestOrigin(object):

    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'))
        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')])
        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')])
        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))
        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))

    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