test_datetime.py

import pytest

import numpy as np
from datetime import date, timedelta, time

import dateutil
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import lrange
from pandas.compat.numpy import np_datetime64_compat
from pandas import (DatetimeIndex, Index, date_range, Series, DataFrame,
                    Timestamp, datetime, offsets)

from pandas.util.testing import assert_series_equal, assert_almost_equal

randn = np.random.randn


class TestDatetimeIndex(object):

    def test_get_loc(self):
        idx = pd.date_range('2000-01-01', periods=3)

        for method in [None, 'pad', 'backfill', 'nearest']:
            assert idx.get_loc(idx[1], method) == 1
            assert idx.get_loc(idx[1].to_pydatetime(), method) == 1
            assert idx.get_loc(str(idx[1]), method) == 1

            if method is not None:
                assert idx.get_loc(idx[1], method,
                                   tolerance=pd.Timedelta('0 days')) == 1

        assert idx.get_loc('2000-01-01', method='nearest') == 0
        assert idx.get_loc('2000-01-01T12', method='nearest') == 1

        assert idx.get_loc('2000-01-01T12', method='nearest',
                           tolerance='1 day') == 1
        assert idx.get_loc('2000-01-01T12', method='nearest',
                           tolerance=pd.Timedelta('1D')) == 1
        assert idx.get_loc('2000-01-01T12', method='nearest',
                           tolerance=np.timedelta64(1, 'D')) == 1
        assert idx.get_loc('2000-01-01T12', method='nearest',
                           tolerance=timedelta(1)) == 1
        with tm.assert_raises_regex(ValueError, 'must be convertible'):
            idx.get_loc('2000-01-01T12', method='nearest', tolerance='foo')
        with pytest.raises(KeyError):
            idx.get_loc('2000-01-01T03', method='nearest', tolerance='2 hours')

        assert idx.get_loc('2000', method='nearest') == slice(0, 3)
        assert idx.get_loc('2000-01', method='nearest') == slice(0, 3)

        assert idx.get_loc('1999', method='nearest') == 0
        assert idx.get_loc('2001', method='nearest') == 2

        with pytest.raises(KeyError):
            idx.get_loc('1999', method='pad')
        with pytest.raises(KeyError):
            idx.get_loc('2001', method='backfill')

        with pytest.raises(KeyError):
            idx.get_loc('foobar')
        with pytest.raises(TypeError):
            idx.get_loc(slice(2))

        idx = pd.to_datetime(['2000-01-01', '2000-01-04'])
        assert idx.get_loc('2000-01-02', method='nearest') == 0
        assert idx.get_loc('2000-01-03', method='nearest') == 1
        assert idx.get_loc('2000-01', method='nearest') == slice(0, 2)

        # time indexing
        idx = pd.date_range('2000-01-01', periods=24, freq='H')
        tm.assert_numpy_array_equal(idx.get_loc(time(12)),
                                    np.array([12]), check_dtype=False)
        tm.assert_numpy_array_equal(idx.get_loc(time(12, 30)),
                                    np.array([]), check_dtype=False)
        with pytest.raises(NotImplementedError):
            idx.get_loc(time(12, 30), method='pad')

    def test_get_indexer(self):
        idx = pd.date_range('2000-01-01', periods=3)
        exp = np.array([0, 1, 2], dtype=np.intp)
        tm.assert_numpy_array_equal(idx.get_indexer(idx), exp)

        target = idx[0] + pd.to_timedelta(['-1 hour', '12 hours',
                                           '1 day 1 hour'])
        tm.assert_numpy_array_equal(idx.get_indexer(target, 'pad'),
                                    np.array([-1, 0, 1], dtype=np.intp))
        tm.assert_numpy_array_equal(idx.get_indexer(target, 'backfill'),
                                    np.array([0, 1, 2], dtype=np.intp))
        tm.assert_numpy_array_equal(idx.get_indexer(target, 'nearest'),
                                    np.array([0, 1, 1], dtype=np.intp))
        tm.assert_numpy_array_equal(
            idx.get_indexer(target, 'nearest',
                            tolerance=pd.Timedelta('1 hour')),
            np.array([0, -1, 1], dtype=np.intp))
        with pytest.raises(ValueError):
            idx.get_indexer(idx[[0]], method='nearest', tolerance='foo')

    def test_reasonable_keyerror(self):
        # GH #1062
        index = DatetimeIndex(['1/3/2000'])
        try:
            index.get_loc('1/1/2000')
        except KeyError as e:
            assert '2000' in str(e)

    def test_roundtrip_pickle_with_tz(self):

        # GH 8367
        # round-trip of timezone
        index = date_range('20130101', periods=3, tz='US/Eastern', name='foo')
        unpickled = tm.round_trip_pickle(index)
        tm.assert_index_equal(index, unpickled)

    def test_reindex_preserves_tz_if_target_is_empty_list_or_array(self):
        # GH7774
        index = date_range('20130101', periods=3, tz='US/Eastern')
        assert str(index.reindex([])[0].tz) == 'US/Eastern'
        assert str(index.reindex(np.array([]))[0].tz) == 'US/Eastern'

    def test_time_loc(self):  # GH8667
        from datetime import time
        from pandas._libs.index import _SIZE_CUTOFF

        ns = _SIZE_CUTOFF + np.array([-100, 100], dtype=np.int64)
        key = time(15, 11, 30)
        start = key.hour * 3600 + key.minute * 60 + key.second
        step = 24 * 3600

        for n in ns:
            idx = pd.date_range('2014-11-26', periods=n, freq='S')
            ts = pd.Series(np.random.randn(n), index=idx)
            i = np.arange(start, n, step)

            tm.assert_numpy_array_equal(ts.index.get_loc(key), i,
                                        check_dtype=False)
            tm.assert_series_equal(ts[key], ts.iloc[i])

            left, right = ts.copy(), ts.copy()
            left[key] *= -10
            right.iloc[i] *= -10
            tm.assert_series_equal(left, right)

    def test_time_overflow_for_32bit_machines(self):
        # GH8943.  On some machines NumPy defaults to np.int32 (for example,
        # 32-bit Linux machines).  In the function _generate_regular_range
        # found in tseries/index.py, `periods` gets multiplied by `strides`
        # (which has value 1e9) and since the max value for np.int32 is ~2e9,
        # and since those machines won't promote np.int32 to np.int64, we get
        # overflow.
        periods = np.int_(1000)

        idx1 = pd.date_range(start='2000', periods=periods, freq='S')
        assert len(idx1) == periods

        idx2 = pd.date_range(end='2000', periods=periods, freq='S')
        assert len(idx2) == periods

    def test_nat(self):
        assert DatetimeIndex([np.nan])[0] is pd.NaT

    def test_ufunc_coercions(self):
        idx = date_range('2011-01-01', periods=3, freq='2D', name='x')

        delta = np.timedelta64(1, 'D')
        for result in [idx + delta, np.add(idx, delta)]:
            assert isinstance(result, DatetimeIndex)
            exp = date_range('2011-01-02', periods=3, freq='2D', name='x')
            tm.assert_index_equal(result, exp)
            assert result.freq == '2D'

        for result in [idx - delta, np.subtract(idx, delta)]:
            assert isinstance(result, DatetimeIndex)
            exp = date_range('2010-12-31', periods=3, freq='2D', name='x')
            tm.assert_index_equal(result, exp)
            assert result.freq == '2D'

        delta = np.array([np.timedelta64(1, 'D'), np.timedelta64(2, 'D'),
                          np.timedelta64(3, 'D')])
        for result in [idx + delta, np.add(idx, delta)]:
            assert isinstance(result, DatetimeIndex)
            exp = DatetimeIndex(['2011-01-02', '2011-01-05', '2011-01-08'],
                                freq='3D', name='x')
            tm.assert_index_equal(result, exp)
            assert result.freq == '3D'

        for result in [idx - delta, np.subtract(idx, delta)]:
            assert isinstance(result, DatetimeIndex)
            exp = DatetimeIndex(['2010-12-31', '2011-01-01', '2011-01-02'],
                                freq='D', name='x')
            tm.assert_index_equal(result, exp)
            assert result.freq == 'D'

    def test_week_of_month_frequency(self):
        # GH 5348: "ValueError: Could not evaluate WOM-1SUN" shouldn't raise
        d1 = date(2002, 9, 1)
        d2 = date(2013, 10, 27)
        d3 = date(2012, 9, 30)
        idx1 = DatetimeIndex([d1, d2])
        idx2 = DatetimeIndex([d3])
        result_append = idx1.append(idx2)
        expected = DatetimeIndex([d1, d2, d3])
        tm.assert_index_equal(result_append, expected)
        result_union = idx1.union(idx2)
        expected = DatetimeIndex([d1, d3, d2])
        tm.assert_index_equal(result_union, expected)

        # GH 5115
        result = date_range("2013-1-1", periods=4, freq='WOM-1SAT')
        dates = ['2013-01-05', '2013-02-02', '2013-03-02', '2013-04-06']
        expected = DatetimeIndex(dates, freq='WOM-1SAT')
        tm.assert_index_equal(result, expected)

    def test_hash_error(self):
        index = date_range('20010101', periods=10)
        with tm.assert_raises_regex(TypeError, "unhashable type: %r" %
                                    type(index).__name__):
            hash(index)

    def test_stringified_slice_with_tz(self):
        # GH2658
        import datetime
        start = datetime.datetime.now()
        idx = DatetimeIndex(start=start, freq="1d", periods=10)
        df = DataFrame(lrange(10), index=idx)
        df["2013-01-14 23:44:34.437768-05:00":]  # no exception here

    def test_append_join_nondatetimeindex(self):
        rng = date_range('1/1/2000', periods=10)
        idx = Index(['a', 'b', 'c', 'd'])

        result = rng.append(idx)
        assert isinstance(result[0], Timestamp)

        # it works
        rng.join(idx, how='outer')

    def test_to_period_nofreq(self):
        idx = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-04'])
        pytest.raises(ValueError, idx.to_period)

        idx = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03'],
                            freq='infer')
        assert idx.freqstr == 'D'
        expected = pd.PeriodIndex(['2000-01-01', '2000-01-02',
                                   '2000-01-03'], freq='D')
        tm.assert_index_equal(idx.to_period(), expected)

        # GH 7606
        idx = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03'])
        assert idx.freqstr is None
        tm.assert_index_equal(idx.to_period(), expected)

    def test_comparisons_coverage(self):
        rng = date_range('1/1/2000', periods=10)

        # raise TypeError for now
        pytest.raises(TypeError, rng.__lt__, rng[3].value)

        result = rng == list(rng)
        exp = rng == rng
        tm.assert_numpy_array_equal(result, exp)

    def test_comparisons_nat(self):

        fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
        fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])

        didx1 = pd.DatetimeIndex(['2014-01-01', pd.NaT, '2014-03-01', pd.NaT,
                                  '2014-05-01', '2014-07-01'])
        didx2 = pd.DatetimeIndex(['2014-02-01', '2014-03-01', pd.NaT, pd.NaT,
                                  '2014-06-01', '2014-07-01'])
        darr = np.array([np_datetime64_compat('2014-02-01 00:00Z'),
                         np_datetime64_compat('2014-03-01 00:00Z'),
                         np_datetime64_compat('nat'), np.datetime64('nat'),
                         np_datetime64_compat('2014-06-01 00:00Z'),
                         np_datetime64_compat('2014-07-01 00:00Z')])

        cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]

        # Check pd.NaT is handles as the same as np.nan
        with tm.assert_produces_warning(None):
            for idx1, idx2 in cases:

                result = idx1 < idx2
                expected = np.array([True, False, False, False, True, False])
                tm.assert_numpy_array_equal(result, expected)

                result = idx2 > idx1
                expected = np.array([True, False, False, False, True, False])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 <= idx2
                expected = np.array([True, False, False, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx2 >= idx1
                expected = np.array([True, False, False, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 == idx2
                expected = np.array([False, False, False, False, False, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 != idx2
                expected = np.array([True, True, True, True, True, False])
                tm.assert_numpy_array_equal(result, expected)

        with tm.assert_produces_warning(None):
            for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]:
                result = idx1 < val
                expected = np.array([False, False, False, False, False, False])
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 > val
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 <= val
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 >= val
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 == val
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 != val
                expected = np.array([True, True, True, True, True, True])
                tm.assert_numpy_array_equal(result, expected)

        # Check pd.NaT is handles as the same as np.nan
        with tm.assert_produces_warning(None):
            for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
                result = idx1 < val
                expected = np.array([True, False, False, False, False, False])
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 > val
                expected = np.array([False, False, False, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 <= val
                expected = np.array([True, False, True, False, False, False])
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 >= val
                expected = np.array([False, False, True, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 == val
                expected = np.array([False, False, True, False, False, False])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 != val
                expected = np.array([True, True, False, True, True, True])
                tm.assert_numpy_array_equal(result, expected)

    def test_map(self):
        rng = date_range('1/1/2000', periods=10)

        f = lambda x: x.strftime('%Y%m%d')
        result = rng.map(f)
        exp = Index([f(x) for x in rng], dtype='<U8')
        tm.assert_index_equal(result, exp)

    def test_iteration_preserves_tz(self):
        # see gh-8890
        index = date_range("2012-01-01", periods=3, freq='H', tz='US/Eastern')

        for i, ts in enumerate(index):
            result = ts
            expected = index[i]
            assert result == expected

        index = date_range("2012-01-01", periods=3, freq='H',
                           tz=dateutil.tz.tzoffset(None, -28800))

        for i, ts in enumerate(index):
            result = ts
            expected = index[i]
            assert result._repr_base == expected._repr_base
            assert result == expected

        # 9100
        index = pd.DatetimeIndex(['2014-12-01 03:32:39.987000-08:00',
                                  '2014-12-01 04:12:34.987000-08:00'])
        for i, ts in enumerate(index):
            result = ts
            expected = index[i]
            assert result._repr_base == expected._repr_base
            assert result == expected

    def test_misc_coverage(self):
        rng = date_range('1/1/2000', periods=5)
        result = rng.groupby(rng.day)
        assert isinstance(list(result.values())[0][0], Timestamp)

        idx = DatetimeIndex(['2000-01-03', '2000-01-01', '2000-01-02'])
        assert not idx.equals(list(idx))

        non_datetime = Index(list('abc'))
        assert not idx.equals(list(non_datetime))

    def test_string_index_series_name_converted(self):
        # #1644
        df = DataFrame(np.random.randn(10, 4),
                       index=date_range('1/1/2000', periods=10))

        result = df.loc['1/3/2000']
        assert result.name == df.index[2]

        result = df.T['1/3/2000']
        assert result.name == df.index[2]

    def test_overflow_offset(self):
        # xref https://github.com/statsmodels/statsmodels/issues/3374
        # ends up multiplying really large numbers which overflow

        t = Timestamp('2017-01-13 00:00:00', freq='D')
        offset = 20169940 * pd.offsets.Day(1)

        def f():
            t + offset
        pytest.raises(OverflowError, f)

        def f():
            offset + t
        pytest.raises(OverflowError, f)

        def f():
            t - offset
        pytest.raises(OverflowError, f)

    def test_get_duplicates(self):
        idx = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-02',
                             '2000-01-03', '2000-01-03', '2000-01-04'])

        result = idx.get_duplicates()
        ex = DatetimeIndex(['2000-01-02', '2000-01-03'])
        tm.assert_index_equal(result, ex)

    def test_argmin_argmax(self):
        idx = DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-02'])
        assert idx.argmin() == 1
        assert idx.argmax() == 0

    def test_sort_values(self):
        idx = DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-02'])

        ordered = idx.sort_values()
        assert ordered.is_monotonic

        ordered = idx.sort_values(ascending=False)
        assert ordered[::-1].is_monotonic

        ordered, dexer = idx.sort_values(return_indexer=True)
        assert ordered.is_monotonic
        tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0], dtype=np.intp))

        ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
        assert ordered[::-1].is_monotonic
        tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1], dtype=np.intp))

    def test_take(self):
        dates = [datetime(2010, 1, 1, 14), datetime(2010, 1, 1, 15),
                 datetime(2010, 1, 1, 17), datetime(2010, 1, 1, 21)]

        for tz in [None, 'US/Eastern', 'Asia/Tokyo']:
            idx = DatetimeIndex(start='2010-01-01 09:00',
                                end='2010-02-01 09:00', freq='H', tz=tz,
                                name='idx')
            expected = DatetimeIndex(dates, freq=None, name='idx', tz=tz)

            taken1 = idx.take([5, 6, 8, 12])
            taken2 = idx[[5, 6, 8, 12]]

            for taken in [taken1, taken2]:
                tm.assert_index_equal(taken, expected)
                assert isinstance(taken, DatetimeIndex)
                assert taken.freq is None
                assert taken.tz == expected.tz
                assert taken.name == expected.name

    def test_take_fill_value(self):
        # GH 12631
        idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'],
                               name='xxx')
        result = idx.take(np.array([1, 0, -1]))
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                    name='xxx')
        tm.assert_index_equal(result, expected)

        # fill_value
        result = idx.take(np.array([1, 0, -1]), fill_value=True)
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'],
                                    name='xxx')
        tm.assert_index_equal(result, expected)

        # allow_fill=False
        result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                          fill_value=True)
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                    name='xxx')
        tm.assert_index_equal(result, expected)

        msg = ('When allow_fill=True and fill_value is not None, '
               'all indices must be >= -1')
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -2]), fill_value=True)
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -5]), fill_value=True)

        with pytest.raises(IndexError):
            idx.take(np.array([1, -5]))

    def test_take_fill_value_with_timezone(self):
        idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'],
                               name='xxx', tz='US/Eastern')
        result = idx.take(np.array([1, 0, -1]))
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                    name='xxx', tz='US/Eastern')
        tm.assert_index_equal(result, expected)

        # fill_value
        result = idx.take(np.array([1, 0, -1]), fill_value=True)
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'],
                                    name='xxx', tz='US/Eastern')
        tm.assert_index_equal(result, expected)

        # allow_fill=False
        result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                          fill_value=True)
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                    name='xxx', tz='US/Eastern')
        tm.assert_index_equal(result, expected)

        msg = ('When allow_fill=True and fill_value is not None, '
               'all indices must be >= -1')
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -2]), fill_value=True)
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -5]), fill_value=True)

        with pytest.raises(IndexError):
            idx.take(np.array([1, -5]))

    def test_map_bug_1677(self):
        index = DatetimeIndex(['2012-04-25 09:30:00.393000'])
        f = index.asof

        result = index.map(f)
        expected = Index([f(index[0])])
        tm.assert_index_equal(result, expected)

    def test_groupby_function_tuple_1677(self):
        df = DataFrame(np.random.rand(100),
                       index=date_range("1/1/2000", periods=100))
        monthly_group = df.groupby(lambda x: (x.year, x.month))

        result = monthly_group.mean()
        assert isinstance(result.index[0], tuple)

    def test_append_numpy_bug_1681(self):
        # another datetime64 bug
        dr = date_range('2011/1/1', '2012/1/1', freq='W-FRI')
        a = DataFrame()
        c = DataFrame({'A': 'foo', 'B': dr}, index=dr)

        result = a.append(c)
        assert (result['B'] == dr).all()

    def test_isin(self):
        index = tm.makeDateIndex(4)
        result = index.isin(index)
        assert result.all()

        result = index.isin(list(index))
        assert result.all()

        assert_almost_equal(index.isin([index[2], 5]),
                            np.array([False, False, True, False]))

    def test_time(self):
        rng = pd.date_range('1/1/2000', freq='12min', periods=10)
        result = pd.Index(rng).time
        expected = [t.time() for t in rng]
        assert (result == expected).all()

    def test_date(self):
        rng = pd.date_range('1/1/2000', freq='12H', periods=10)
        result = pd.Index(rng).date
        expected = [t.date() for t in rng]
        assert (result == expected).all()

    def test_does_not_convert_mixed_integer(self):
        df = tm.makeCustomDataframe(10, 10,
                                    data_gen_f=lambda *args, **kwargs: randn(),
                                    r_idx_type='i', c_idx_type='dt')
        cols = df.columns.join(df.index, how='outer')
        joined = cols.join(df.columns)
        assert cols.dtype == np.dtype('O')
        assert cols.dtype == joined.dtype
        tm.assert_numpy_array_equal(cols.values, joined.values)

    def test_slice_keeps_name(self):
        # GH4226
        st = pd.Timestamp('2013-07-01 00:00:00', tz='America/Los_Angeles')
        et = pd.Timestamp('2013-07-02 00:00:00', tz='America/Los_Angeles')
        dr = pd.date_range(st, et, freq='H', name='timebucket')
        assert dr[1:].name == dr.name

    def test_join_self(self):
        index = date_range('1/1/2000', periods=10)
        kinds = 'outer', 'inner', 'left', 'right'
        for kind in kinds:
            joined = index.join(index, how=kind)
            assert index is joined

    def assert_index_parameters(self, index):
        assert index.freq == '40960N'
        assert index.inferred_freq == '40960N'

    def test_ns_index(self):
        nsamples = 400
        ns = int(1e9 / 24414)
        dtstart = np.datetime64('2012-09-20T00:00:00')

        dt = dtstart + np.arange(nsamples) * np.timedelta64(ns, 'ns')
        freq = ns * offsets.Nano()
        index = pd.DatetimeIndex(dt, freq=freq, name='time')
        self.assert_index_parameters(index)

        new_index = pd.DatetimeIndex(start=index[0], end=index[-1],
                                     freq=index.freq)
        self.assert_index_parameters(new_index)

    def test_join_with_period_index(self):
        df = tm.makeCustomDataframe(
            10, 10, data_gen_f=lambda *args: np.random.randint(2),
            c_idx_type='p', r_idx_type='dt')
        s = df.iloc[:5, 0]
        joins = 'left', 'right', 'inner', 'outer'

        for join in joins:
            with tm.assert_raises_regex(ValueError,
                                        'can only call with other '
                                        'PeriodIndex-ed objects'):
                df.columns.join(s.index, how=join)

    def test_factorize(self):
        idx1 = DatetimeIndex(['2014-01', '2014-01', '2014-02', '2014-02',
                              '2014-03', '2014-03'])

        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
        exp_idx = DatetimeIndex(['2014-01', '2014-02', '2014-03'])

        arr, idx = idx1.factorize()
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, exp_idx)

        arr, idx = idx1.factorize(sort=True)
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, exp_idx)

        # tz must be preserved
        idx1 = idx1.tz_localize('Asia/Tokyo')
        exp_idx = exp_idx.tz_localize('Asia/Tokyo')

        arr, idx = idx1.factorize()
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, exp_idx)

        idx2 = pd.DatetimeIndex(['2014-03', '2014-03', '2014-02', '2014-01',
                                 '2014-03', '2014-01'])

        exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.intp)
        exp_idx = DatetimeIndex(['2014-01', '2014-02', '2014-03'])
        arr, idx = idx2.factorize(sort=True)
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, exp_idx)

        exp_arr = np.array([0, 0, 1, 2, 0, 2], dtype=np.intp)
        exp_idx = DatetimeIndex(['2014-03', '2014-02', '2014-01'])
        arr, idx = idx2.factorize()
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, exp_idx)

        # freq must be preserved
        idx3 = date_range('2000-01', periods=4, freq='M', tz='Asia/Tokyo')
        exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
        arr, idx = idx3.factorize()
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, idx3)

    def test_factorize_tz(self):
        # GH 13750
        for tz in [None, 'UTC', 'US/Eastern', 'Asia/Tokyo']:
            base = pd.date_range('2016-11-05', freq='H', periods=100, tz=tz)
            idx = base.repeat(5)

            exp_arr = np.arange(100, dtype=np.intp).repeat(5)

            for obj in [idx, pd.Series(idx)]:
                arr, res = obj.factorize()
                tm.assert_numpy_array_equal(arr, exp_arr)
                tm.assert_index_equal(res, base)

    def test_factorize_dst(self):
        # GH 13750
        idx = pd.date_range('2016-11-06', freq='H', periods=12,
                            tz='US/Eastern')

        for obj in [idx, pd.Series(idx)]:
            arr, res = obj.factorize()
            tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
            tm.assert_index_equal(res, idx)

        idx = pd.date_range('2016-06-13', freq='H', periods=12,
                            tz='US/Eastern')

        for obj in [idx, pd.Series(idx)]:
            arr, res = obj.factorize()
            tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
            tm.assert_index_equal(res, idx)

    def test_slice_with_negative_step(self):
        ts = Series(np.arange(20),
                    date_range('2014-01-01', periods=20, freq='MS'))
        SLC = pd.IndexSlice

        def assert_slices_equivalent(l_slc, i_slc):
            assert_series_equal(ts[l_slc], ts.iloc[i_slc])
            assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc])
            assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc])

        assert_slices_equivalent(SLC[Timestamp('2014-10-01')::-1], SLC[9::-1])
        assert_slices_equivalent(SLC['2014-10-01'::-1], SLC[9::-1])

        assert_slices_equivalent(SLC[:Timestamp('2014-10-01'):-1], SLC[:8:-1])
        assert_slices_equivalent(SLC[:'2014-10-01':-1], SLC[:8:-1])

        assert_slices_equivalent(SLC['2015-02-01':'2014-10-01':-1],
                                 SLC[13:8:-1])
        assert_slices_equivalent(SLC[Timestamp('2015-02-01'):Timestamp(
            '2014-10-01'):-1], SLC[13:8:-1])
        assert_slices_equivalent(SLC['2015-02-01':Timestamp('2014-10-01'):-1],
                                 SLC[13:8:-1])
        assert_slices_equivalent(SLC[Timestamp('2015-02-01'):'2014-10-01':-1],
                                 SLC[13:8:-1])

        assert_slices_equivalent(SLC['2014-10-01':'2015-02-01':-1], SLC[:0])

    def test_slice_with_zero_step_raises(self):
        ts = Series(np.arange(20),
                    date_range('2014-01-01', periods=20, freq='MS'))
        tm.assert_raises_regex(ValueError, 'slice step cannot be zero',
                               lambda: ts[::0])
        tm.assert_raises_regex(ValueError, 'slice step cannot be zero',
                               lambda: ts.loc[::0])
        tm.assert_raises_regex(ValueError, 'slice step cannot be zero',
                               lambda: ts.loc[::0])

    def test_slice_bounds_empty(self):
        # GH 14354
        empty_idx = DatetimeIndex(freq='1H', periods=0, end='2015')

        right = empty_idx._maybe_cast_slice_bound('2015-01-02', 'right', 'loc')
        exp = Timestamp('2015-01-02 23:59:59.999999999')
        assert right == exp

        left = empty_idx._maybe_cast_slice_bound('2015-01-02', 'left', 'loc')
        exp = Timestamp('2015-01-02 00:00:00')
        assert left == exp

    def test_slice_duplicate_monotonic(self):
        # https://github.com/pandas-dev/pandas/issues/16515
        idx = pd.DatetimeIndex(['2017', '2017'])
        result = idx._maybe_cast_slice_bound('2017-01-01', 'left', 'loc')
        expected = Timestamp('2017-01-01')
        assert result == expected