Python pandas.TimedeltaIndex() Examples

def test_insert_index_timedelta64(self):
        obj = pd.TimedeltaIndex(['1 day', '2 day', '3 day', '4 day'])
        assert obj.dtype == 'timedelta64[ns]'

        # timedelta64 + timedelta64 => timedelta64
        exp = pd.TimedeltaIndex(['1 day', '10 day', '2 day', '3 day', '4 day'])
        self._assert_insert_conversion(obj, pd.Timedelta('10 day'),
                                       exp, 'timedelta64[ns]')

        # ToDo: must coerce to object
        msg = "cannot insert TimedeltaIndex with incompatible label"
        with pytest.raises(TypeError, match=msg):
            obj.insert(1, pd.Timestamp('2012-01-01'))

        # ToDo: must coerce to object
        msg = "cannot insert TimedeltaIndex with incompatible label"
        with pytest.raises(TypeError, match=msg):
            obj.insert(1, 1) 

def test_tdi_round(self):
        td = pd.timedelta_range(start='16801 days', periods=5, freq='30Min')
        elt = td[1]

        expected_rng = TimedeltaIndex([Timedelta('16801 days 00:00:00'),
                                       Timedelta('16801 days 00:00:00'),
                                       Timedelta('16801 days 01:00:00'),
                                       Timedelta('16801 days 02:00:00'),
                                       Timedelta('16801 days 02:00:00')])
        expected_elt = expected_rng[1]

        tm.assert_index_equal(td.round(freq='H'), expected_rng)
        assert elt.round(freq='H') == expected_elt

        msg = pd._libs.tslibs.frequencies.INVALID_FREQ_ERR_MSG
        with pytest.raises(ValueError, match=msg):
            td.round(freq='foo')
        with pytest.raises(ValueError, match=msg):
            elt.round(freq='foo')

        msg = "<MonthEnd> is a non-fixed frequency"
        with pytest.raises(ValueError, match=msg):
            td.round(freq='M')
        with pytest.raises(ValueError, match=msg):
            elt.round(freq='M') 

def test_td64arr_add_offset_array(self, box):
        # GH#18849
        tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00'])
        other = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)])

        expected = TimedeltaIndex([tdi[n] + other[n] for n in range(len(tdi))],
                                  freq='infer')

        tdi = tm.box_expected(tdi, box)
        expected = tm.box_expected(expected, box)

        # The DataFrame operation is transposed and so operates as separate
        #  scalar operations, which do not issue a PerformanceWarning
        warn = PerformanceWarning if box is not pd.DataFrame else None
        with tm.assert_produces_warning(warn):
            res = tdi + other
        tm.assert_equal(res, expected)

        with tm.assert_produces_warning(warn):
            res2 = other + tdi
        tm.assert_equal(res2, expected) 

def test_sort_values(self):

        idx = TimedeltaIndex(['4d', '1d', '2d'])

        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]),
                                    check_dtype=False)

        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]),
                                    check_dtype=False) 

def test_td64arr_add_sub_td64_nat(self, box):
        # GH#23320 special handling for timedelta64("NaT")
        tdi = pd.TimedeltaIndex([NaT, Timedelta('1s')])
        other = np.timedelta64("NaT")
        expected = pd.TimedeltaIndex(["NaT"] * 2)

        obj = tm.box_expected(tdi, box)
        expected = tm.box_expected(expected, box)

        result = obj + other
        tm.assert_equal(result, expected)
        result = other + obj
        tm.assert_equal(result, expected)
        result = obj - other
        tm.assert_equal(result, expected)
        result = other - obj
        tm.assert_equal(result, expected) 

def test_factorize(self):
        idx1 = TimedeltaIndex(['1 day', '1 day', '2 day', '2 day', '3 day',
                               '3 day'])

        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
        exp_idx = TimedeltaIndex(['1 day', '2 day', '3 day'])

        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)

        # freq must be preserved
        idx3 = timedelta_range('1 day', periods=4, freq='s')
        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_difference_sort(self, sort):

        index = pd.TimedeltaIndex(["5 days", "3 days", "2 days", "4 days",
                                   "1 days", "0 days"])

        other = timedelta_range("1 days", "4 days", freq="D")
        idx_diff = index.difference(other, sort)

        expected = TimedeltaIndex(["5 days", "0 days"], freq=None)

        if sort is None:
            expected = expected.sort_values()

        tm.assert_index_equal(idx_diff, expected)
        tm.assert_attr_equal('freq', idx_diff, expected)

        other = timedelta_range("2 days", "5 days", freq="D")
        idx_diff = index.difference(other, sort)
        expected = TimedeltaIndex(["1 days", "0 days"], freq=None)

        if sort is None:
            expected = expected.sort_values()

        tm.assert_index_equal(idx_diff, expected)
        tm.assert_attr_equal('freq', idx_diff, expected) 

def test_nat(self):
        assert pd.TimedeltaIndex._na_value is pd.NaT
        assert pd.TimedeltaIndex([])._na_value is pd.NaT

        idx = pd.TimedeltaIndex(['1 days', '2 days'])
        assert idx._can_hold_na

        tm.assert_numpy_array_equal(idx._isnan, np.array([False, False]))
        assert idx.hasnans is False
        tm.assert_numpy_array_equal(idx._nan_idxs,
                                    np.array([], dtype=np.intp))

        idx = pd.TimedeltaIndex(['1 days', 'NaT'])
        assert idx._can_hold_na

        tm.assert_numpy_array_equal(idx._isnan, np.array([False, True]))
        assert idx.hasnans is True
        tm.assert_numpy_array_equal(idx._nan_idxs,
                                    np.array([1], dtype=np.intp)) 

def test_td64arr_sub_offset_index(self, names, box):
        # GH#18824, GH#19744
        if box is pd.DataFrame and names[1] == 'bar':
            pytest.skip("Name propagation for DataFrame does not behave like "
                        "it does for Index/Series")

        tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00'],
                             name=names[0])
        other = pd.Index([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)],
                         name=names[1])

        expected = TimedeltaIndex([tdi[n] - other[n] for n in range(len(tdi))],
                                  freq='infer', name=names[2])

        tdi = tm.box_expected(tdi, box)
        expected = tm.box_expected(expected, box)

        # The DataFrame operation is transposed and so operates as separate
        #  scalar operations, which do not issue a PerformanceWarning
        warn = PerformanceWarning if box is not pd.DataFrame else None
        with tm.assert_produces_warning(warn):
            res = tdi - other
        tm.assert_equal(res, expected) 

def test_td64arr_sub_offset_array(self, box_with_array):
        # GH#18824
        tdi = TimedeltaIndex(['1 days 00:00:00', '3 days 04:00:00'])
        other = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)])

        expected = TimedeltaIndex([tdi[n] - other[n] for n in range(len(tdi))],
                                  freq='infer')

        tdi = tm.box_expected(tdi, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        # The DataFrame operation is transposed and so operates as separate
        #  scalar operations, which do not issue a PerformanceWarning
        warn = None if box_with_array is pd.DataFrame else PerformanceWarning
        with tm.assert_produces_warning(warn):
            res = tdi - other
        tm.assert_equal(res, expected) 

def test_difference_freq(self, sort):
        # GH14323: Difference of TimedeltaIndex should not preserve frequency

        index = timedelta_range("0 days", "5 days", freq="D")

        other = timedelta_range("1 days", "4 days", freq="D")
        expected = TimedeltaIndex(["0 days", "5 days"], freq=None)
        idx_diff = index.difference(other, sort)
        tm.assert_index_equal(idx_diff, expected)
        tm.assert_attr_equal('freq', idx_diff, expected)

        other = timedelta_range("2 days", "5 days", freq="D")
        idx_diff = index.difference(other, sort)
        expected = TimedeltaIndex(["0 days", "1 days"], freq=None)
        tm.assert_index_equal(idx_diff, expected)
        tm.assert_attr_equal('freq', idx_diff, expected) 

def test_td64arr_add_timestamp(self, box_with_array, tz_naive_fixture):
        # GH#23215

        # TODO: parametrize over scalar datetime types?
        tz = tz_naive_fixture
        other = Timestamp('2011-01-01', tz=tz)

        idx = TimedeltaIndex(['1 day', '2 day'])
        expected = DatetimeIndex(['2011-01-02', '2011-01-03'], tz=tz)

        # FIXME: fails with transpose=True because of tz-aware DataFrame
        #  transpose bug
        idx = tm.box_expected(idx, box_with_array, transpose=False)
        expected = tm.box_expected(expected, box_with_array, transpose=False)

        result = idx + other
        tm.assert_equal(result, expected)

        result = other + idx
        tm.assert_equal(result, expected) 

def test_take(self):
        # GH 10295
        idx1 = timedelta_range('1 day', '31 day', freq='D', name='idx')

        for idx in [idx1]:
            result = idx.take([0])
            assert result == Timedelta('1 day')

            result = idx.take([-1])
            assert result == Timedelta('31 day')

            result = idx.take([0, 1, 2])
            expected = timedelta_range('1 day', '3 day', freq='D',
                                       name='idx')
            tm.assert_index_equal(result, expected)
            assert result.freq == expected.freq

            result = idx.take([0, 2, 4])
            expected = timedelta_range('1 day', '5 day', freq='2D',
                                       name='idx')
            tm.assert_index_equal(result, expected)
            assert result.freq == expected.freq

            result = idx.take([7, 4, 1])
            expected = timedelta_range('8 day', '2 day', freq='-3D',
                                       name='idx')
            tm.assert_index_equal(result, expected)
            assert result.freq == expected.freq

            result = idx.take([3, 2, 5])
            expected = TimedeltaIndex(['4 day', '3 day', '6 day'], name='idx')
            tm.assert_index_equal(result, expected)
            assert result.freq is None

            result = idx.take([-3, 2, 5])
            expected = TimedeltaIndex(['29 day', '3 day', '6 day'], name='idx')
            tm.assert_index_equal(result, expected)
            assert result.freq is None 

def test_take2(self):
        tds = ['1day 02:00:00', '1 day 04:00:00', '1 day 10:00:00']
        idx = timedelta_range(start='1d', end='2d', freq='H', name='idx')
        expected = TimedeltaIndex(tds, freq=None, name='idx')

        taken1 = idx.take([2, 4, 10])
        taken2 = idx[[2, 4, 10]]

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

def test_equals(self):
        # GH 13107
        idx = pd.TimedeltaIndex(['1 days', '2 days', 'NaT'])
        assert idx.equals(idx)
        assert idx.equals(idx.copy())
        assert idx.equals(idx.astype(object))
        assert idx.astype(object).equals(idx)
        assert idx.astype(object).equals(idx.astype(object))
        assert not idx.equals(list(idx))
        assert not idx.equals(pd.Series(idx))

        idx2 = pd.TimedeltaIndex(['2 days', '1 days', 'NaT'])
        assert not idx.equals(idx2)
        assert not idx.equals(idx2.copy())
        assert not idx.equals(idx2.astype(object))
        assert not idx.astype(object).equals(idx2)
        assert not idx.astype(object).equals(idx2.astype(object))
        assert not idx.equals(list(idx2))
        assert not idx.equals(pd.Series(idx2)) 

def test_freq_setter_errors(self):
        # GH 20678
        idx = TimedeltaIndex(['0 days', '2 days', '4 days'])

        # setting with an incompatible freq
        msg = ('Inferred frequency 2D from passed values does not conform to '
               'passed frequency 5D')
        with pytest.raises(ValueError, match=msg):
            idx.freq = '5D'

        # setting with a non-fixed frequency
        msg = r'<2 \* BusinessDays> is a non-fixed frequency'
        with pytest.raises(ValueError, match=msg):
            idx.freq = '2B'

        # setting with non-freq string
        with pytest.raises(ValueError, match='Invalid frequency'):
            idx.freq = 'foo' 

def test_dti_tdi_numeric_ops(self):
        # These are normally union/diff set-like ops
        tdi = TimedeltaIndex(['1 days', pd.NaT, '2 days'], name='foo')
        dti = pd.date_range('20130101', periods=3, name='bar')

        # TODO(wesm): unused?
        # td = Timedelta('1 days')
        # dt = Timestamp('20130101')

        result = tdi - tdi
        expected = TimedeltaIndex(['0 days', pd.NaT, '0 days'], name='foo')
        tm.assert_index_equal(result, expected)

        result = tdi + tdi
        expected = TimedeltaIndex(['2 days', pd.NaT, '4 days'], name='foo')
        tm.assert_index_equal(result, expected)

        result = dti - tdi  # name will be reset
        expected = DatetimeIndex(['20121231', pd.NaT, '20130101'])
        tm.assert_index_equal(result, expected) 

def test_tdi_addsub_integer_array_no_freq(self, box):
        # GH#19959
        tdi = TimedeltaIndex(['1 Day', 'NaT', '3 Hours'])
        other = box([14, -1, 16])
        with pytest.raises(NullFrequencyError):
            tdi + other
        with pytest.raises(NullFrequencyError):
            other + tdi
        with pytest.raises(NullFrequencyError):
            tdi - other
        with pytest.raises(NullFrequencyError):
            other - tdi

    # -------------------------------------------------------------
    # Binary operations TimedeltaIndex and timedelta-like
    # Note: add and sub are tested in tests.test_arithmetic, in-place
    #  tests are kept here because their behavior is Index-specific 

def test_tdi_cmp_str_invalid(self, box_with_array):
        # GH#13624
        xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
        tdi = TimedeltaIndex(['1 day', '2 days'])
        tdarr = tm.box_expected(tdi, box_with_array)

        for left, right in [(tdarr, 'a'), ('a', tdarr)]:
            with pytest.raises(TypeError):
                left > right
            with pytest.raises(TypeError):
                left >= right
            with pytest.raises(TypeError):
                left < right
            with pytest.raises(TypeError):
                left <= right

            result = left == right
            expected = np.array([False, False], dtype=bool)
            expected = tm.box_expected(expected, xbox)
            tm.assert_equal(result, expected)

            result = left != right
            expected = np.array([True, True], dtype=bool)
            expected = tm.box_expected(expected, xbox)
            tm.assert_equal(result, expected) 

def test_float64_ns_rounded(self):
        # GH#23539 without specifying a unit, floats are regarded as nanos,
        #  and fractional portions are truncated
        tdi = TimedeltaIndex([2.3, 9.7])
        expected = TimedeltaIndex([2, 9])
        tm.assert_index_equal(tdi, expected)

        # integral floats are non-lossy
        tdi = TimedeltaIndex([2.0, 9.0])
        expected = TimedeltaIndex([2, 9])
        tm.assert_index_equal(tdi, expected)

        # NaNs get converted to NaT
        tdi = TimedeltaIndex([2.0, np.nan])
        expected = TimedeltaIndex([pd.Timedelta(nanoseconds=2), pd.NaT])
        tm.assert_index_equal(tdi, expected) 

def test_representation_to_series(self):
        idx1 = TimedeltaIndex([], freq='D')
        idx2 = TimedeltaIndex(['1 days'], freq='D')
        idx3 = TimedeltaIndex(['1 days', '2 days'], freq='D')
        idx4 = TimedeltaIndex(['1 days', '2 days', '3 days'], freq='D')
        idx5 = TimedeltaIndex(['1 days 00:00:01', '2 days', '3 days'])

        exp1 = """Series([], dtype: timedelta64[ns])"""

        exp2 = ("0   1 days\n"
                "dtype: timedelta64[ns]")

        exp3 = ("0   1 days\n"
                "1   2 days\n"
                "dtype: timedelta64[ns]")

        exp4 = ("0   1 days\n"
                "1   2 days\n"
                "2   3 days\n"
                "dtype: timedelta64[ns]")

        exp5 = ("0   1 days 00:00:01\n"
                "1   2 days 00:00:00\n"
                "2   3 days 00:00:00\n"
                "dtype: timedelta64[ns]")

        with pd.option_context('display.width', 300):
            for idx, expected in zip([idx1, idx2, idx3, idx4, idx5],
                                     [exp1, exp2, exp3, exp4, exp5]):
                result = repr(pd.Series(idx))
                assert result == expected 

def test_summary(self):
        # GH#9116
        idx1 = TimedeltaIndex([], freq='D')
        idx2 = TimedeltaIndex(['1 days'], freq='D')
        idx3 = TimedeltaIndex(['1 days', '2 days'], freq='D')
        idx4 = TimedeltaIndex(['1 days', '2 days', '3 days'], freq='D')
        idx5 = TimedeltaIndex(['1 days 00:00:01', '2 days', '3 days'])

        exp1 = ("TimedeltaIndex: 0 entries\n"
                "Freq: D")

        exp2 = ("TimedeltaIndex: 1 entries, 1 days to 1 days\n"
                "Freq: D")

        exp3 = ("TimedeltaIndex: 2 entries, 1 days to 2 days\n"
                "Freq: D")

        exp4 = ("TimedeltaIndex: 3 entries, 1 days to 3 days\n"
                "Freq: D")

        exp5 = ("TimedeltaIndex: 3 entries, 1 days 00:00:01 to 3 days "
                "00:00:00")

        for idx, expected in zip([idx1, idx2, idx3, idx4, idx5],
                                 [exp1, exp2, exp3, exp4, exp5]):
            result = idx._summary()
            assert result == expected 

def test_representation(self, method):
        idx1 = TimedeltaIndex([], freq='D')
        idx2 = TimedeltaIndex(['1 days'], freq='D')
        idx3 = TimedeltaIndex(['1 days', '2 days'], freq='D')
        idx4 = TimedeltaIndex(['1 days', '2 days', '3 days'], freq='D')
        idx5 = TimedeltaIndex(['1 days 00:00:01', '2 days', '3 days'])

        exp1 = """TimedeltaIndex([], dtype='timedelta64[ns]', freq='D')"""

        exp2 = ("TimedeltaIndex(['1 days'], dtype='timedelta64[ns]', "
                "freq='D')")

        exp3 = ("TimedeltaIndex(['1 days', '2 days'], "
                "dtype='timedelta64[ns]', freq='D')")

        exp4 = ("TimedeltaIndex(['1 days', '2 days', '3 days'], "
                "dtype='timedelta64[ns]', freq='D')")

        exp5 = ("TimedeltaIndex(['1 days 00:00:01', '2 days 00:00:00', "
                "'3 days 00:00:00'], dtype='timedelta64[ns]', freq=None)")

        with pd.option_context('display.width', 300):
            for idx, expected in zip([idx1, idx2, idx3, idx4, idx5],
                                     [exp1, exp2, exp3, exp4, exp5]):
                result = getattr(idx, method)()
                assert result == expected 

def test_tdi_sub_integer_array(self, box):
        # GH#19959
        rng = timedelta_range('9H', freq='H', periods=3)
        other = box([4, 3, 2])
        expected = TimedeltaIndex(['5H', '7H', '9H'])
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            # GH#22535
            result = rng - other
            tm.assert_index_equal(result, expected)

        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            # GH#22535
            result = other - rng
            tm.assert_index_equal(result, -expected) 

def test_infer_freq(self, freq):
        # GH#11018
        idx = pd.timedelta_range('1', freq=freq, periods=10)
        result = pd.TimedeltaIndex(idx.asi8, freq='infer')
        tm.assert_index_equal(idx, result)
        assert result.freq == freq 

def test_nonunique_contains(self):
        # GH 9512
        for idx in map(TimedeltaIndex, ([0, 1, 0], [0, 0, -1], [0, -1, -1],
                                        ['00:01:00', '00:01:00', '00:02:00'],
                                        ['00:01:00', '00:01:00', '00:00:01'])):
            assert idx[0] in idx 

def test_concatlike_common_coerce_to_pandas_object(self):
        # GH 13626
        # result must be Timestamp/Timedelta, not datetime.datetime/timedelta
        dti = pd.DatetimeIndex(['2011-01-01', '2011-01-02'])
        tdi = pd.TimedeltaIndex(['1 days', '2 days'])

        exp = pd.Index([pd.Timestamp('2011-01-01'),
                        pd.Timestamp('2011-01-02'),
                        pd.Timedelta('1 days'),
                        pd.Timedelta('2 days')])

        res = dti.append(tdi)
        tm.assert_index_equal(res, exp)
        assert isinstance(res[0], pd.Timestamp)
        assert isinstance(res[-1], pd.Timedelta)

        dts = pd.Series(dti)
        tds = pd.Series(tdi)
        res = dts.append(tds)
        tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
        assert isinstance(res.iloc[0], pd.Timestamp)
        assert isinstance(res.iloc[-1], pd.Timedelta)

        res = pd.concat([dts, tds])
        tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))
        assert isinstance(res.iloc[0], pd.Timestamp)
        assert isinstance(res.iloc[-1], pd.Timedelta) 

def test_repeat(self):
        index = pd.timedelta_range('1 days', periods=2, freq='D')
        exp = pd.TimedeltaIndex(['1 days', '1 days', '2 days', '2 days'])
        for res in [index.repeat(2), np.repeat(index, 2)]:
            tm.assert_index_equal(res, exp)
            assert res.freq is None

        index = TimedeltaIndex(['1 days', 'NaT', '3 days'])
        exp = TimedeltaIndex(['1 days', '1 days', '1 days',
                              'NaT', 'NaT', 'NaT',
                              '3 days', '3 days', '3 days'])
        for res in [index.repeat(3), np.repeat(index, 3)]:
            tm.assert_index_equal(res, exp)
            assert res.freq is None 

def test_concatlike_common_period_mixed_dt_to_object(self):
        # GH 13221
        # different datetimelike
        pi1 = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
        tdi = pd.TimedeltaIndex(['1 days', '2 days'])
        exp = pd.Index([pd.Period('2011-01', freq='M'),
                        pd.Period('2011-02', freq='M'),
                        pd.Timedelta('1 days'),
                        pd.Timedelta('2 days')], dtype=object)

        res = pi1.append(tdi)
        tm.assert_index_equal(res, exp)

        ps1 = pd.Series(pi1)
        tds = pd.Series(tdi)
        res = ps1.append(tds)
        tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))

        res = pd.concat([ps1, tds])
        tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))

        # inverse
        exp = pd.Index([pd.Timedelta('1 days'),
                        pd.Timedelta('2 days'),
                        pd.Period('2011-01', freq='M'),
                        pd.Period('2011-02', freq='M')], dtype=object)

        res = tdi.append(pi1)
        tm.assert_index_equal(res, exp)

        ps1 = pd.Series(pi1)
        tds = pd.Series(tdi)
        res = tds.append(ps1)
        tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1]))

        res = pd.concat([tds, ps1])
        tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) 

def test_constructor_dtypes_to_timedelta(self, cast_index, vals):
        if cast_index:
            index = Index(vals, dtype=object)
            assert isinstance(index, Index)
            assert index.dtype == object
        else:
            index = Index(vals)
            assert isinstance(index, TimedeltaIndex)