/pandas/tseries/tests/test_timeseries.py

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# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta
import sys
import operator

import nose

import numpy as np
randn = np.random.randn

from pandas import (Index, Series, TimeSeries, DataFrame,
                    isnull, date_range, Timestamp, Period, DatetimeIndex,
                    Int64Index, to_datetime, bdate_range, Float64Index)

import pandas.core.datetools as datetools
import pandas.tseries.offsets as offsets
import pandas.tseries.tools as tools
import pandas.tseries.frequencies as fmod
import pandas as pd

from pandas.util.testing import assert_series_equal, assert_almost_equal
import pandas.util.testing as tm

from pandas.tslib import NaT, iNaT
import pandas.lib as lib
import pandas.tslib as tslib

import pandas.index as _index

from pandas.compat import range, long, StringIO, lrange, lmap, zip, product
import pandas.core.datetools as dt
from numpy.random import rand
from numpy.testing import assert_array_equal
from pandas.util.testing import assert_frame_equal
import pandas.compat as compat
import pandas.core.common as com
from pandas import concat
from pandas import _np_version_under1p7

from numpy.testing.decorators import slow


def _skip_if_has_locale():
    import locale
    lang, _ = locale.getlocale()
    if lang is not None:
        raise nose.SkipTest("Specific locale is set {0}".format(lang))

def _skip_if_windows_python_3():
    if sys.version_info > (3,) and sys.platform == 'win32':
        raise nose.SkipTest("not used on python 3/win32")

def _skip_if_not_windows_python_3():
    if sys.version_info < (3,) or sys.platform != 'win32':
        raise nose.SkipTest("only run on python 3/win32")


class TestTimeSeriesDuplicates(tm.TestCase):
    _multiprocess_can_split_ = True

    def setUp(self):
        dates = [datetime(2000, 1, 2), datetime(2000, 1, 2),
                 datetime(2000, 1, 2), datetime(2000, 1, 3),
                 datetime(2000, 1, 3), datetime(2000, 1, 3),
                 datetime(2000, 1, 4), datetime(2000, 1, 4),
                 datetime(2000, 1, 4), datetime(2000, 1, 5)]

        self.dups = Series(np.random.randn(len(dates)), index=dates)

    def test_constructor(self):
        tm.assert_isinstance(self.dups, TimeSeries)
        tm.assert_isinstance(self.dups.index, DatetimeIndex)

    def test_is_unique_monotonic(self):
        self.assertFalse(self.dups.index.is_unique)

    def test_index_unique(self):
        uniques = self.dups.index.unique()
        expected = DatetimeIndex([datetime(2000, 1, 2), datetime(2000, 1, 3),
                                  datetime(2000, 1, 4), datetime(2000, 1, 5)])
        self.assertEqual(uniques.dtype, 'M8[ns]')  # sanity
        self.assertTrue(uniques.equals(expected))
        self.assertEqual(self.dups.index.nunique(), 4)

        # #2563
        self.assertTrue(isinstance(uniques, DatetimeIndex))

        dups_local = self.dups.index.tz_localize('US/Eastern')
        dups_local.name = 'foo'
        result = dups_local.unique()
        expected = DatetimeIndex(expected, tz='US/Eastern')
        self.assertTrue(result.tz is not None)
        self.assertEqual(result.name, 'foo')
        self.assertTrue(result.equals(expected))

        # NaT, note this is excluded
        arr = [ 1370745748 + t for t in range(20) ] + [iNaT]
        idx = DatetimeIndex(arr * 3)
        self.assertTrue(idx.unique().equals(DatetimeIndex(arr)))
        self.assertEqual(idx.nunique(), 20)
        self.assertEqual(idx.nunique(dropna=False), 21)

        arr = [ Timestamp('2013-06-09 02:42:28') + timedelta(seconds=t) for t in range(20) ] + [NaT]
        idx = DatetimeIndex(arr * 3)
        self.assertTrue(idx.unique().equals(DatetimeIndex(arr)))
        self.assertEqual(idx.nunique(), 20)
        self.assertEqual(idx.nunique(dropna=False), 21)


    def test_index_dupes_contains(self):
        d = datetime(2011, 12, 5, 20, 30)
        ix = DatetimeIndex([d, d])
        self.assertTrue(d in ix)

    def test_duplicate_dates_indexing(self):
        ts = self.dups

        uniques = ts.index.unique()
        for date in uniques:
            result = ts[date]

            mask = ts.index == date
            total = (ts.index == date).sum()
            expected = ts[mask]
            if total > 1:
                assert_series_equal(result, expected)
            else:
                assert_almost_equal(result, expected[0])

            cp = ts.copy()
            cp[date] = 0
            expected = Series(np.where(mask, 0, ts), index=ts.index)
            assert_series_equal(cp, expected)

        self.assertRaises(KeyError, ts.__getitem__, datetime(2000, 1, 6))

        # new index
        ts[datetime(2000,1,6)] = 0
        self.assertEqual(ts[datetime(2000,1,6)], 0)

    def test_range_slice(self):
        idx = DatetimeIndex(['1/1/2000', '1/2/2000', '1/2/2000', '1/3/2000',
                             '1/4/2000'])

        ts = Series(np.random.randn(len(idx)), index=idx)

        result = ts['1/2/2000':]
        expected = ts[1:]
        assert_series_equal(result, expected)

        result = ts['1/2/2000':'1/3/2000']
        expected = ts[1:4]
        assert_series_equal(result, expected)

    def test_groupby_average_dup_values(self):
        result = self.dups.groupby(level=0).mean()
        expected = self.dups.groupby(self.dups.index).mean()
        assert_series_equal(result, expected)

    def test_indexing_over_size_cutoff(self):
        import datetime
        # #1821

        old_cutoff = _index._SIZE_CUTOFF
        try:
            _index._SIZE_CUTOFF = 1000

            # create large list of non periodic datetime
            dates = []
            sec = datetime.timedelta(seconds=1)
            half_sec = datetime.timedelta(microseconds=500000)
            d = datetime.datetime(2011, 12, 5, 20, 30)
            n = 1100
            for i in range(n):
                dates.append(d)
                dates.append(d + sec)
                dates.append(d + sec + half_sec)
                dates.append(d + sec + sec + half_sec)
                d += 3 * sec

            # duplicate some values in the list
            duplicate_positions = np.random.randint(0, len(dates) - 1, 20)
            for p in duplicate_positions:
                dates[p + 1] = dates[p]

            df = DataFrame(np.random.randn(len(dates), 4),
                           index=dates,
                           columns=list('ABCD'))

            pos = n * 3
            timestamp = df.index[pos]
            self.assertIn(timestamp, df.index)

            # it works!
            df.ix[timestamp]
            self.assertTrue(len(df.ix[[timestamp]]) > 0)
        finally:
            _index._SIZE_CUTOFF = old_cutoff

    def test_indexing_unordered(self):

        # GH 2437
        rng = date_range(start='2011-01-01', end='2011-01-15')
        ts  = Series(randn(len(rng)), index=rng)
        ts2 = concat([ts[0:4],ts[-4:],ts[4:-4]])

        for t in ts.index:
            s = str(t)
            expected = ts[t]
            result = ts2[t]
            self.assertTrue(expected == result)

        # GH 3448 (ranges)
        def compare(slobj):
            result = ts2[slobj].copy()
            result = result.sort_index()
            expected = ts[slobj]
            assert_series_equal(result,expected)

        compare(slice('2011-01-01','2011-01-15'))
        compare(slice('2010-12-30','2011-01-15'))
        compare(slice('2011-01-01','2011-01-16'))

        # partial ranges
        compare(slice('2011-01-01','2011-01-6'))
        compare(slice('2011-01-06','2011-01-8'))
        compare(slice('2011-01-06','2011-01-12'))

        # single values
        result = ts2['2011'].sort_index()
        expected = ts['2011']
        assert_series_equal(result,expected)

        # diff freq
        rng = date_range(datetime(2005, 1, 1), periods=20, freq='M')
        ts = Series(np.arange(len(rng)), index=rng)
        ts = ts.take(np.random.permutation(20))

        result = ts['2005']
        for t in result.index:
            self.assertTrue(t.year == 2005)

    def test_indexing(self):

        idx = date_range("2001-1-1", periods=20, freq='M')
        ts = Series(np.random.rand(len(idx)),index=idx)

        # getting

        # GH 3070, make sure semantics work on Series/Frame
        expected = ts['2001']

        df = DataFrame(dict(A = ts))
        result = df['2001']['A']
        assert_series_equal(expected,result)

        # setting
        ts['2001'] = 1
        expected = ts['2001']

        df.loc['2001','A'] = 1

        result = df['2001']['A']
        assert_series_equal(expected,result)

        # GH3546 (not including times on the last day)
        idx = date_range(start='2013-05-31 00:00', end='2013-05-31 23:00', freq='H')
        ts  = Series(lrange(len(idx)), index=idx)
        expected = ts['2013-05']
        assert_series_equal(expected,ts)

        idx = date_range(start='2013-05-31 00:00', end='2013-05-31 23:59', freq='S')
        ts  = Series(lrange(len(idx)), index=idx)
        expected = ts['2013-05']
        assert_series_equal(expected,ts)

        idx = [ Timestamp('2013-05-31 00:00'), Timestamp(datetime(2013,5,31,23,59,59,999999))]
        ts  = Series(lrange(len(idx)), index=idx)
        expected = ts['2013']
        assert_series_equal(expected,ts)

        # GH 3925, indexing with a seconds resolution string / datetime object
        df = DataFrame(randn(5,5),columns=['open','high','low','close','volume'],index=date_range('2012-01-02 18:01:00',periods=5,tz='US/Central',freq='s'))
        expected = df.loc[[df.index[2]]]
        result = df['2012-01-02 18:01:02']
        assert_frame_equal(result,expected)

        # this is a single date, so will raise
        self.assertRaises(KeyError, df.__getitem__, df.index[2],)

    def test_recreate_from_data(self):
        if _np_version_under1p7:
            freqs = ['M', 'Q', 'A', 'D', 'B', 'T', 'S', 'L', 'U', 'H']
        else:
            freqs = ['M', 'Q', 'A', 'D', 'B', 'T', 'S', 'L', 'U', 'H', 'N', 'C']

        for f in freqs:
            org = DatetimeIndex(start='2001/02/01 09:00', freq=f, periods=1)
            idx = DatetimeIndex(org, freq=f)
            self.assertTrue(idx.equals(org))

            org = DatetimeIndex(start='2001/02/01 09:00', freq=f, tz='US/Pacific', periods=1)
            idx = DatetimeIndex(org, freq=f, tz='US/Pacific')
            self.assertTrue(idx.equals(org))


def assert_range_equal(left, right):
    assert(left.equals(right))
    assert(left.freq == right.freq)
    assert(left.tz == right.tz)


class TestTimeSeries(tm.TestCase):
    _multiprocess_can_split_ = True

    def test_is_(self):
        dti = DatetimeIndex(start='1/1/2005', end='12/1/2005', freq='M')
        self.assertTrue(dti.is_(dti))
        self.assertTrue(dti.is_(dti.view()))
        self.assertFalse(dti.is_(dti.copy()))

    def test_dti_slicing(self):
        dti = DatetimeIndex(start='1/1/2005', end='12/1/2005', freq='M')
        dti2 = dti[[1, 3, 5]]

        v1 = dti2[0]
        v2 = dti2[1]
        v3 = dti2[2]

        self.assertEqual(v1, Timestamp('2/28/2005'))
        self.assertEqual(v2, Timestamp('4/30/2005'))
        self.assertEqual(v3, Timestamp('6/30/2005'))

        # don't carry freq through irregular slicing
        self.assertIsNone(dti2.freq)

    def test_pass_datetimeindex_to_index(self):
        # Bugs in #1396

        rng = date_range('1/1/2000', '3/1/2000')
        idx = Index(rng, dtype=object)

        expected = Index(rng.to_pydatetime(), dtype=object)

        self.assert_numpy_array_equal(idx.values, expected.values)

    def test_contiguous_boolean_preserve_freq(self):
        rng = date_range('1/1/2000', '3/1/2000', freq='B')

        mask = np.zeros(len(rng), dtype=bool)
        mask[10:20] = True

        masked = rng[mask]
        expected = rng[10:20]
        self.assertIsNotNone(expected.freq)
        assert_range_equal(masked, expected)

        mask[22] = True
        masked = rng[mask]
        self.assertIsNone(masked.freq)

    def test_getitem_median_slice_bug(self):
        index = date_range('20090415', '20090519', freq='2B')
        s = Series(np.random.randn(13), index=index)

        indexer = [slice(6, 7, None)]
        result = s[indexer]
        expected = s[indexer[0]]
        assert_series_equal(result, expected)

    def test_series_box_timestamp(self):
        rng = date_range('20090415', '20090519', freq='B')
        s = Series(rng)

        tm.assert_isinstance(s[5], Timestamp)

        rng = date_range('20090415', '20090519', freq='B')
        s = Series(rng, index=rng)
        tm.assert_isinstance(s[5], Timestamp)

        tm.assert_isinstance(s.iget_value(5), Timestamp)

    def test_date_range_ambiguous_arguments(self):
        # #2538
        start = datetime(2011, 1, 1, 5, 3, 40)
        end = datetime(2011, 1, 1, 8, 9, 40)

        self.assertRaises(ValueError, date_range, start, end,
                          freq='s', periods=10)

    def test_timestamp_to_datetime(self):
        tm._skip_if_no_pytz()
        rng = date_range('20090415', '20090519',
                         tz='US/Eastern')

        stamp = rng[0]
        dtval = stamp.to_pydatetime()
        self.assertEqual(stamp, dtval)
        self.assertEqual(stamp.tzinfo, dtval.tzinfo)

    def test_timestamp_to_datetime_dateutil(self):
        tm._skip_if_no_pytz()
        rng = date_range('20090415', '20090519',
                         tz='dateutil/US/Eastern')

        stamp = rng[0]
        dtval = stamp.to_pydatetime()
        self.assertEqual(stamp, dtval)
        self.assertEqual(stamp.tzinfo, dtval.tzinfo)

    def test_timestamp_to_datetime_explicit_pytz(self):
        tm._skip_if_no_pytz()
        import pytz
        rng = date_range('20090415', '20090519',
                         tz=pytz.timezone('US/Eastern'))

        stamp = rng[0]
        dtval = stamp.to_pydatetime()
        self.assertEquals(stamp, dtval)
        self.assertEquals(stamp.tzinfo, dtval.tzinfo)

    def test_timestamp_to_datetime_explicit_dateutil(self):
        _skip_if_windows_python_3()
        tm._skip_if_no_dateutil()
        import dateutil
        rng = date_range('20090415', '20090519',
                         tz=dateutil.tz.gettz('US/Eastern'))

        stamp = rng[0]
        dtval = stamp.to_pydatetime()
        self.assertEquals(stamp, dtval)
        self.assertEquals(stamp.tzinfo, dtval.tzinfo)

    def test_index_convert_to_datetime_array(self):
        tm._skip_if_no_pytz()

        def _check_rng(rng):
            converted = rng.to_pydatetime()
            tm.assert_isinstance(converted, np.ndarray)
            for x, stamp in zip(converted, rng):
                tm.assert_isinstance(x, datetime)
                self.assertEqual(x, stamp.to_pydatetime())
                self.assertEqual(x.tzinfo, stamp.tzinfo)

        rng = date_range('20090415', '20090519')
        rng_eastern = date_range('20090415', '20090519', tz='US/Eastern')
        rng_utc = date_range('20090415', '20090519', tz='utc')

        _check_rng(rng)
        _check_rng(rng_eastern)
        _check_rng(rng_utc)

    def test_index_convert_to_datetime_array_explicit_pytz(self):
        tm._skip_if_no_pytz()
        import pytz

        def _check_rng(rng):
            converted = rng.to_pydatetime()
            tm.assert_isinstance(converted, np.ndarray)
            for x, stamp in zip(converted, rng):
                tm.assert_isinstance(x, datetime)
                self.assertEquals(x, stamp.to_pydatetime())
                self.assertEquals(x.tzinfo, stamp.tzinfo)

        rng = date_range('20090415', '20090519')
        rng_eastern = date_range('20090415', '20090519', tz=pytz.timezone('US/Eastern'))
        rng_utc = date_range('20090415', '20090519', tz=pytz.utc)

        _check_rng(rng)
        _check_rng(rng_eastern)
        _check_rng(rng_utc)

    def test_index_convert_to_datetime_array_dateutil(self):
        tm._skip_if_no_dateutil()
        import dateutil

        def _check_rng(rng):
            converted = rng.to_pydatetime()
            tm.assert_isinstance(converted, np.ndarray)
            for x, stamp in zip(converted, rng):
                tm.assert_isinstance(x, datetime)
                self.assertEquals(x, stamp.to_pydatetime())
                self.assertEquals(x.tzinfo, stamp.tzinfo)

        rng = date_range('20090415', '20090519')
        rng_eastern = date_range('20090415', '20090519', tz='dateutil/US/Eastern')
        rng_utc = date_range('20090415', '20090519', tz=dateutil.tz.tzutc())

        _check_rng(rng)
        _check_rng(rng_eastern)
        _check_rng(rng_utc)

    def test_ctor_str_intraday(self):
        rng = DatetimeIndex(['1-1-2000 00:00:01'])
        self.assertEqual(rng[0].second, 1)

    def test_series_ctor_plus_datetimeindex(self):
        rng = date_range('20090415', '20090519', freq='B')
        data = dict((k, 1) for k in rng)

        result = Series(data, index=rng)
        self.assertIs(result.index, rng)

    def test_series_pad_backfill_limit(self):
        index = np.arange(10)
        s = Series(np.random.randn(10), index=index)

        result = s[:2].reindex(index, method='pad', limit=5)

        expected = s[:2].reindex(index).fillna(method='pad')
        expected[-3:] = np.nan
        assert_series_equal(result, expected)

        result = s[-2:].reindex(index, method='backfill', limit=5)

        expected = s[-2:].reindex(index).fillna(method='backfill')
        expected[:3] = np.nan
        assert_series_equal(result, expected)

    def test_series_fillna_limit(self):
        index = np.arange(10)
        s = Series(np.random.randn(10), index=index)

        result = s[:2].reindex(index)
        result = result.fillna(method='pad', limit=5)

        expected = s[:2].reindex(index).fillna(method='pad')
        expected[-3:] = np.nan
        assert_series_equal(result, expected)

        result = s[-2:].reindex(index)
        result = result.fillna(method='bfill', limit=5)

        expected = s[-2:].reindex(index).fillna(method='backfill')
        expected[:3] = np.nan
        assert_series_equal(result, expected)

    def test_frame_pad_backfill_limit(self):
        index = np.arange(10)
        df = DataFrame(np.random.randn(10, 4), index=index)

        result = df[:2].reindex(index, method='pad', limit=5)

        expected = df[:2].reindex(index).fillna(method='pad')
        expected.values[-3:] = np.nan
        tm.assert_frame_equal(result, expected)

        result = df[-2:].reindex(index, method='backfill', limit=5)

        expected = df[-2:].reindex(index).fillna(method='backfill')
        expected.values[:3] = np.nan
        tm.assert_frame_equal(result, expected)

    def test_frame_fillna_limit(self):
        index = np.arange(10)
        df = DataFrame(np.random.randn(10, 4), index=index)

        result = df[:2].reindex(index)
        result = result.fillna(method='pad', limit=5)

        expected = df[:2].reindex(index).fillna(method='pad')
        expected.values[-3:] = np.nan
        tm.assert_frame_equal(result, expected)

        result = df[-2:].reindex(index)
        result = result.fillna(method='backfill', limit=5)

        expected = df[-2:].reindex(index).fillna(method='backfill')
        expected.values[:3] = np.nan
        tm.assert_frame_equal(result, expected)

    def test_frame_setitem_timestamp(self):
        # 2155
        columns = DatetimeIndex(start='1/1/2012', end='2/1/2012',
                                freq=datetools.bday)
        index = lrange(10)
        data = DataFrame(columns=columns, index=index)
        t = datetime(2012, 11, 1)
        ts = Timestamp(t)
        data[ts] = np.nan  # works

    def test_sparse_series_fillna_limit(self):
        index = np.arange(10)
        s = Series(np.random.randn(10), index=index)

        ss = s[:2].reindex(index).to_sparse()
        result = ss.fillna(method='pad', limit=5)
        expected = ss.fillna(method='pad', limit=5)
        expected = expected.to_dense()
        expected[-3:] = np.nan
        expected = expected.to_sparse()
        assert_series_equal(result, expected)

        ss = s[-2:].reindex(index).to_sparse()
        result = ss.fillna(method='backfill', limit=5)
        expected = ss.fillna(method='backfill')
        expected = expected.to_dense()
        expected[:3] = np.nan
        expected = expected.to_sparse()
        assert_series_equal(result, expected)

    def test_sparse_series_pad_backfill_limit(self):
        index = np.arange(10)
        s = Series(np.random.randn(10), index=index)
        s = s.to_sparse()

        result = s[:2].reindex(index, method='pad', limit=5)
        expected = s[:2].reindex(index).fillna(method='pad')
        expected = expected.to_dense()
        expected[-3:] = np.nan
        expected = expected.to_sparse()
        assert_series_equal(result, expected)

        result = s[-2:].reindex(index, method='backfill', limit=5)
        expected = s[-2:].reindex(index).fillna(method='backfill')
        expected = expected.to_dense()
        expected[:3] = np.nan
        expected = expected.to_sparse()
        assert_series_equal(result, expected)

    def test_sparse_frame_pad_backfill_limit(self):
        index = np.arange(10)
        df = DataFrame(np.random.randn(10, 4), index=index)
        sdf = df.to_sparse()

        result = sdf[:2].reindex(index, method='pad', limit=5)

        expected = sdf[:2].reindex(index).fillna(method='pad')
        expected = expected.to_dense()
        expected.values[-3:] = np.nan
        expected = expected.to_sparse()
        tm.assert_frame_equal(result, expected)

        result = sdf[-2:].reindex(index, method='backfill', limit=5)

        expected = sdf[-2:].reindex(index).fillna(method='backfill')
        expected = expected.to_dense()
        expected.values[:3] = np.nan
        expected = expected.to_sparse()
        tm.assert_frame_equal(result, expected)

    def test_sparse_frame_fillna_limit(self):
        index = np.arange(10)
        df = DataFrame(np.random.randn(10, 4), index=index)
        sdf = df.to_sparse()

        result = sdf[:2].reindex(index)
        result = result.fillna(method='pad', limit=5)

        expected = sdf[:2].reindex(index).fillna(method='pad')
        expected = expected.to_dense()
        expected.values[-3:] = np.nan
        expected = expected.to_sparse()
        tm.assert_frame_equal(result, expected)

        result = sdf[-2:].reindex(index)
        result = result.fillna(method='backfill', limit=5)

        expected = sdf[-2:].reindex(index).fillna(method='backfill')
        expected = expected.to_dense()
        expected.values[:3] = np.nan
        expected = expected.to_sparse()
        tm.assert_frame_equal(result, expected)

    def test_pad_require_monotonicity(self):
        rng = date_range('1/1/2000', '3/1/2000', freq='B')

        rng2 = rng[::2][::-1]

        self.assertRaises(ValueError, rng2.get_indexer, rng,
                          method='pad')

    def test_frame_ctor_datetime64_column(self):
        rng = date_range('1/1/2000 00:00:00', '1/1/2000 1:59:50',
                         freq='10s')
        dates = np.asarray(rng)

        df = DataFrame({'A': np.random.randn(len(rng)), 'B': dates})
        self.assertTrue(np.issubdtype(df['B'].dtype, np.dtype('M8[ns]')))

    def test_frame_add_datetime64_column(self):
        rng = date_range('1/1/2000 00:00:00', '1/1/2000 1:59:50',
                         freq='10s')
        df = DataFrame(index=np.arange(len(rng)))

        df['A'] = rng
        self.assertTrue(np.issubdtype(df['A'].dtype, np.dtype('M8[ns]')))

    def test_frame_datetime64_pre1900_repr(self):
        df = DataFrame({'year': date_range('1/1/1700', periods=50,
                                           freq='A-DEC')})
        # it works!
        repr(df)

    def test_frame_add_datetime64_col_other_units(self):
        n = 100

        units = ['h', 'm', 's', 'ms', 'D', 'M', 'Y']

        ns_dtype = np.dtype('M8[ns]')

        for unit in units:
            dtype = np.dtype('M8[%s]' % unit)
            vals = np.arange(n, dtype=np.int64).view(dtype)

            df = DataFrame({'ints': np.arange(n)}, index=np.arange(n))
            df[unit] = vals

            ex_vals = to_datetime(vals.astype('O'))

            self.assertEqual(df[unit].dtype, ns_dtype)
            self.assertTrue((df[unit].values == ex_vals).all())

        # Test insertion into existing datetime64 column
        df = DataFrame({'ints': np.arange(n)}, index=np.arange(n))
        df['dates'] = np.arange(n, dtype=np.int64).view(ns_dtype)

        for unit in units:
            dtype = np.dtype('M8[%s]' % unit)
            vals = np.arange(n, dtype=np.int64).view(dtype)

            tmp = df.copy()

            tmp['dates'] = vals
            ex_vals = to_datetime(vals.astype('O'))

            self.assertTrue((tmp['dates'].values == ex_vals).all())

    def test_to_datetime_unit(self):

        epoch = 1370745748
        s = Series([ epoch + t for t in range(20) ])
        result = to_datetime(s,unit='s')
        expected = Series([ Timestamp('2013-06-09 02:42:28') + timedelta(seconds=t) for t in range(20) ])
        assert_series_equal(result,expected)

        s = Series([ epoch + t for t in range(20) ]).astype(float)
        result = to_datetime(s,unit='s')
        expected = Series([ Timestamp('2013-06-09 02:42:28') + timedelta(seconds=t) for t in range(20) ])
        assert_series_equal(result,expected)

        s = Series([ epoch + t for t in range(20) ] + [iNaT])
        result = to_datetime(s,unit='s')
        expected = Series([ Timestamp('2013-06-09 02:42:28') + timedelta(seconds=t) for t in range(20) ] + [NaT])
        assert_series_equal(result,expected)

        s = Series([ epoch + t for t in range(20) ] + [iNaT]).astype(float)
        result = to_datetime(s,unit='s')
        expected = Series([ Timestamp('2013-06-09 02:42:28') + timedelta(seconds=t) for t in range(20) ] + [NaT])
        assert_series_equal(result,expected)

        s = concat([Series([ epoch + t for t in range(20) ]).astype(float),Series([np.nan])],ignore_index=True)
        result = to_datetime(s,unit='s')
        expected = Series([ Timestamp('2013-06-09 02:42:28') + timedelta(seconds=t) for t in range(20) ] + [NaT])
        assert_series_equal(result,expected)

    def test_series_ctor_datetime64(self):
        rng = date_range('1/1/2000 00:00:00', '1/1/2000 1:59:50',
                         freq='10s')
        dates = np.asarray(rng)

        series = Series(dates)
        self.assertTrue(np.issubdtype(series.dtype, np.dtype('M8[ns]')))

    def test_index_cast_datetime64_other_units(self):
        arr = np.arange(0, 100, 10, dtype=np.int64).view('M8[D]')

        idx = Index(arr)

        self.assertTrue((idx.values == tslib.cast_to_nanoseconds(arr)).all())

    def test_index_astype_datetime64(self):
        # valid only under 1.7!
        if not _np_version_under1p7:
            raise nose.SkipTest("test only valid in numpy < 1.7")

        idx = Index([datetime(2012, 1, 1)], dtype=object)
        casted = idx.astype(np.dtype('M8[D]'))

        casted = idx.astype(np.dtype('M8[D]'))
        expected = DatetimeIndex(idx.values)
        tm.assert_isinstance(casted, DatetimeIndex)
        self.assertTrue(casted.equals(expected))

    def test_reindex_series_add_nat(self):
        rng = date_range('1/1/2000 00:00:00', periods=10, freq='10s')
        series = Series(rng)

        result = series.reindex(lrange(15))
        self.assertTrue(np.issubdtype(result.dtype, np.dtype('M8[ns]')))

        mask = result.isnull()
        self.assertTrue(mask[-5:].all())
        self.assertFalse(mask[:-5].any())

    def test_reindex_frame_add_nat(self):
        rng = date_range('1/1/2000 00:00:00', periods=10, freq='10s')
        df = DataFrame({'A': np.random.randn(len(rng)), 'B': rng})

        result = df.reindex(lrange(15))
        self.assertTrue(np.issubdtype(result['B'].dtype, np.dtype('M8[ns]')))

        mask = com.isnull(result)['B']
        self.assertTrue(mask[-5:].all())
        self.assertFalse(mask[:-5].any())

    def test_series_repr_nat(self):
        series = Series([0, 1000, 2000, iNaT], dtype='M8[ns]')

        result = repr(series)
        expected = ('0          1970-01-01 00:00:00\n'
                    '1   1970-01-01 00:00:00.000001\n'
                    '2   1970-01-01 00:00:00.000002\n'
                    '3                          NaT\n'
                    'dtype: datetime64[ns]')
        self.assertEqual(result, expected)

    def test_fillna_nat(self):
        series = Series([0, 1, 2, iNaT], dtype='M8[ns]')

        filled = series.fillna(method='pad')
        filled2 = series.fillna(value=series.values[2])

        expected = series.copy()
        expected.values[3] = expected.values[2]

        assert_series_equal(filled, expected)
        assert_series_equal(filled2, expected)

        df = DataFrame({'A': series})
        filled = df.fillna(method='pad')
        filled2 = df.fillna(value=series.values[2])
        expected = DataFrame({'A': expected})
        assert_frame_equal(filled, expected)
        assert_frame_equal(filled2, expected)

        series = Series([iNaT, 0, 1, 2], dtype='M8[ns]')

        filled = series.fillna(method='bfill')
        filled2 = series.fillna(value=series[1])

        expected = series.copy()
        expected[0] = expected[1]

        assert_series_equal(filled, expected)
        assert_series_equal(filled2, expected)

        df = DataFrame({'A': series})
        filled = df.fillna(method='bfill')
        filled2 = df.fillna(value=series[1])
        expected = DataFrame({'A': expected})
        assert_frame_equal(filled, expected)
        assert_frame_equal(filled2, expected)

    def test_string_na_nat_conversion(self):
        # 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 com.isnull(val):
                expected[i] = iNaT
            else:
                expected[i] = parse_date(val)

        result = tslib.array_to_datetime(strings)
        assert_almost_equal(result, expected)

        result2 = to_datetime(strings)
        tm.assert_isinstance(result2, DatetimeIndex)
        assert_almost_equal(result, result2)

        malformed = np.array(['1/100/2000', np.nan], dtype=object)
        result = to_datetime(malformed)
        assert_almost_equal(result, malformed)

        self.assertRaises(ValueError, to_datetime, malformed,
                          errors='raise')

        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'), np.nan,
                          to_datetime('1/3/2000'), np.nan,
                          to_datetime('1/5/2000')], index=idx, name='foo')

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

        expected = Series(np.empty(5, dtype='M8[ns]'), index=idx)
        for i in range(5):
            x = series[i]
            if isnull(x):
                expected[i] = iNaT
            else:
                expected[i] = to_datetime(x)

        assert_series_equal(result, expected)
        self.assertEqual(result.name, 'foo')

        assert_series_equal(dresult, expected)
        self.assertEqual(dresult.name, 'foo')

    def test_to_datetime_iso8601(self):
        result = to_datetime(["2012-01-01 00:00:00"])
        exp = Timestamp("2012-01-01 00:00:00")
        self.assertEqual(result[0], exp)

        result = to_datetime(['20121001'])  # bad iso 8601
        exp = Timestamp('2012-10-01')
        self.assertEqual(result[0], exp)

    def test_to_datetime_default(self):
        rs = to_datetime('2001')
        xp = datetime(2001, 1, 1)
        self.assertTrue(rs, xp)

        #### dayfirst is essentially broken
        #### to_datetime('01-13-2012', dayfirst=True)
        #### self.assertRaises(ValueError, to_datetime('01-13-2012', dayfirst=True))

    def test_to_datetime_on_datetime64_series(self):
        # #2699
        s = Series(date_range('1/1/2000', periods=10))

        result = to_datetime(s)
        self.assertEqual(result[0], s[0])

    def test_to_datetime_with_apply(self):
        # this is only locale tested with US/None locales
        _skip_if_has_locale()

        # 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')
        result = td.apply(pd.to_datetime, format='%b %y')
        assert_series_equal(result, expected)

        td = pd.Series(['May 04', 'Jun 02', ''], index=[1,2,3])
        self.assertRaises(ValueError, lambda : pd.to_datetime(td,format='%b %y'))
        self.assertRaises(ValueError, lambda : td.apply(pd.to_datetime, format='%b %y'))
        expected = pd.to_datetime(td, format='%b %y', coerce=True)

        result = td.apply(lambda x: pd.to_datetime(x, format='%b %y', coerce=True))
        assert_series_equal(result, expected)

    def test_nat_vector_field_access(self):
        idx = DatetimeIndex(['1/1/2000', None, None, '1/4/2000'])

        fields = ['year', 'quarter', 'month', 'day', 'hour',
                  'minute', 'second', 'microsecond', 'nanosecond',
                  'week', 'dayofyear']
        for field in fields:
            result = getattr(idx, field)
            expected = [getattr(x, field) if x is not NaT else -1
                        for x in idx]
            self.assert_numpy_array_equal(result, expected)

    def test_nat_scalar_field_access(self):
        fields = ['year', 'quarter', 'month', 'day', 'hour',
                  'minute', 'second', 'microsecond', 'nanosecond',
                  'week', 'dayofyear']
        for field in fields:
            result = getattr(NaT, field)
            self.assertEqual(result, -1)

        self.assertEqual(NaT.weekday(), -1)

    def test_to_datetime_types(self):

        # empty string
        result = to_datetime('')
        self.assertIs(result, NaT)

        result = to_datetime(['', ''])
        self.assertTrue(isnull(result).all())

        # ints
        result = Timestamp(0)
        expected = to_datetime(0)
        self.assertEqual(result, expected)

        # GH 3888 (strings)
        expected = to_datetime(['2012'])[0]
        result = to_datetime('2012')
        self.assertEqual(result, expected)

        ### array = ['2012','20120101','20120101 12:01:01']
        array = ['20120101','20120101 12:01:01']
        expected = list(to_datetime(array))
        result = lmap(Timestamp,array)
        tm.assert_almost_equal(result,expected)

        ### currently fails ###
        ### result = Timestamp('2012')
        ### expected = to_datetime('2012')
        ### self.assertEqual(result, expected)

    def test_to_datetime_unprocessable_input(self):
        # GH 4928
        self.assert_numpy_array_equal(
            to_datetime([1, '1']),
            np.array([1, '1'], dtype='O')
        )
        self.assertRaises(TypeError, to_datetime, [1, '1'], errors='raise')

    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])
        self.assertEqual(index[0], scalar.astype('O'))

        value = Timestamp(scalar)
        self.assertEqual(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)

        self.assertTrue(rng.equals(result))

    def test_to_datetime_dt64s(self):
        in_bound_dts = [
            np.datetime64('2000-01-01'),
            np.datetime64('2000-01-02'),
        ]

        for dt in in_bound_dts:
            self.assertEqual(
                pd.to_datetime(dt),
                Timestamp(dt)
            )

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

        for dt in oob_dts:
            self.assertRaises(ValueError, pd.to_datetime, dt, errors='raise')
            self.assertRaises(ValueError, tslib.Timestamp, dt)
            self.assertIs(pd.to_datetime(dt, coerce=True), NaT)

    def test_to_datetime_array_of_dt64s(self):
        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
        self.assert_numpy_array_equal(
            pd.to_datetime(dts, box=False),
            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')]

        self.assertRaises(
            ValueError,
            pd.to_datetime,
            dts_with_oob,
            coerce=False,
            errors='raise'
        )

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

        # With coerce=False and 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
        self.assert_numpy_array_equal(
            pd.to_datetime(dts_with_oob, box=False, coerce=False),
            np.array(
                    [dt.item() for dt in dts_with_oob],
                    dtype='O'
            )
        )

    def test_index_to_datetime(self):
        idx = Index(['1/1/2000', '1/2/2000', '1/3/2000'])

        result = idx.to_datetime()
        expected = DatetimeIndex(datetools.to_datetime(idx.values))
        self.assertTrue(result.equals(expected))

        today = datetime.today()
        idx = Index([today], dtype=object)
        result = idx.to_datetime()
        expected = DatetimeIndex([today])
        self.assertTrue(result.equals(expected))

    def test_to_datetime_freq(self):
        xp = bdate_range('2000-1-1', periods=10, tz='UTC')
        rs = xp.to_datetime()
        self.assertEqual(xp.freq, rs.freq)
        self.assertEqual(xp.tzinfo, rs.tzinfo)

    def test_range_misspecified(self):
        # GH #1095

        self.assertRaises(ValueError, date_range, '1/1/2000')
        self.assertRaises(ValueError, date_range, end='1/1/2000')
        self.assertRaises(ValueError, date_range, periods=10)

        self.assertRaises(ValueError, date_range, '1/1/2000', freq='H')
        self.assertRaises(ValueError, date_range, end='1/1/2000', freq='H')
        self.assertRaises(ValueError, date_range, periods=10, freq='H')

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

    def test_reindex_with_datetimes(self):
        rng = date_range('1/1/2000', periods=20)
        ts = Series(np.random.randn(20), index=rng)

        result = ts.reindex(list(ts.index[5:10]))
        expected = ts[5:10]
        tm.assert_series_equal(result, expected)

        result = ts[list(ts.index[5:10])]
        tm.assert_series_equal(result, expected)

    def test_promote_datetime_date(self):
        rng = date_range('1/1/2000', periods=20)
        ts = Series(np.random.randn(20), index=rng)

        ts_slice = ts[5:]
        ts2 = ts_slice.copy()
        ts2.index = [x.date() for x in ts2.index]

        result = ts + ts2
        result2 = ts2 + ts
        expected = ts + ts[5:]
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

        # test asfreq
        result = ts2.asfreq('4H', method='ffill')
        expected = ts[5:].asfreq('4H', method='ffill')
        assert_series_equal(result, expected)

        result = rng.get_indexer(ts2.index)
        expected = rng.get_indexer(ts_slice.index)
        self.assert_numpy_array_equal(result, expected)

    def test_asfreq_normalize(self):
        rng = date_range('1/1/2000 09:30', periods=20)
        norm = date_range('1/1/2000', periods=20)
        vals = np.random.randn(20)
        ts = Series(vals, index=rng)

        result = ts.asfreq('D', normalize=True)
        norm = date_range('1/1/2000', periods=20)
        expected = Series(vals, index=norm)

        assert_series_equal(result, expected)

        vals = np.random.randn(20, 3)
        ts = DataFrame(vals, index=rng)

        result = ts.asfreq('D', normalize=True)
        expected = DataFrame(vals, index=norm)

        assert_frame_equal(result, expected)

    def test_date_range_gen_error(self):
        rng = date_range('1/1/2000 00:00', '1/1/2000 00:18', freq='5min')
        self.assertEqual(len(rng), 4)

    def test_first_subset(self):
        ts = _simple_ts('1/1/2000', '1/1/2010', freq='12h')
        result = ts.first('10d')
        self.assertEqual(len(result), 20)

        ts = _simple_ts('1/1/2000', '1/1/2010')
        result = ts.first('10d')
        self.assertEqual(len(result), 10)

        result = ts.first('3M')
        expected = ts[:'3/31/2000']
        assert_series_equal(result, expected)

        result = ts.first('21D')
        expected = ts[:21]
        assert_series_equal(result, expected)

        result = ts[:0].first('3M')
        assert_series_equal(result, ts[:0])

    def test_last_subset(self):
        ts = _simple_ts('1/1/2000', '1/1/2010', freq='12h')
        result = ts.last('10d')
        self.assertEqual(len(result), 20)

        ts = _simple_ts('1/1/2000', '1/1/2010')
        result = ts.last('10d')
        self.assertEqual(len(result), 10)

        result = ts.last('21D')
        expected = ts['12/12/2009':]
        assert_series_equal(result, expected)

        result = ts.last('21D')
        expected = ts[-21:]
        assert_series_equal(result, expected)

        result = ts[:0].last('3M')
        assert_series_equal(result, ts[:0])

    def test_add_offset(self):
        rng = date_range('1/1/2000', '2/1/2000')

        result = rng + offsets.Hour(2)
        expected = date_range('1/1/2000 02:00', '2/1/2000 02:00')
        self.assertTrue(result.equals(expected))

    def test_format_pre_1900_dates(self):
        rng = date_range('1/1/1850', '1/1/1950', freq='A-DEC')
        rng.format()
        ts = Series(1, index=rng)
        repr(ts)

    def test_repeat(self):
        rng = date_range('1/1/2000', '1/1/2001')

        result = rng.repeat(5)
        self.assertIsNone(result.freq)
        self.assertEqual(len(result), 5 * len(rng))

    def test_at_time(self):
        rng = date_range('1/1/2000', '1/5/2000', freq='5min')
        ts = Series(np.random.randn(len(rng)), index=rng)
        rs = ts.at_time(rng[1])
        self.assertTrue((rs.index.hour == rng[1].hour).all())
        self.assertTrue((rs.index.minute == rng[1].minute).all())
        self.assertTrue((rs.index.second == rng[1].second).all())

        result = ts.at_time('9:30')
        expected = ts.at_time(time(9, 30))
        assert_series_equal(result, expected)

        df = DataFrame(np.random.randn(len(rng), 3), index=rng)

        result = ts[time(9, 30)]
        result_df = df.ix[time(9, 30)]
        expected = ts[(rng.hour == 9) & (rng.minute == 30)]
        exp_df = df[(rng.hour == 9) & (rng.minute == 30)]

        # expected.index = date_range('1/1/2000', '1/4/2000')

        assert_series_equal(result, expected)
        tm.assert_frame_equal(result_df, exp_df)

        chunk = df.ix['1/4/2000':]
        result = chunk.ix[time(9, 30)]
        expected = result_df[-1:]
        tm.assert_frame_equal(result, expected)

        # midnight, everything
        rng = date_range('1/1/2000', '1/31/2000')
        ts = Series(np.random.randn(len(rng)), index=rng)

        result = ts.at_time(time(0, 0))
        assert_series_equal(result, ts)

        # time doesn't exist
        rng = date_range('1/1/2012', freq='23Min', periods=384)
        ts = Series(np.random.randn(len(rng)), rng)
        rs = ts.at_time('16:00')
        self.assertEqual(len(rs), 0)

    def test_at_time_frame(self):
        rng = date_range('1/1/2000', '1/5/2000', freq='5min')
        ts = DataFrame(np.random.randn(len(rng), 2), index=rng)
        rs = ts.at_time(rng[1])
        self.assertTrue((rs.index.hour == rng[1].hour).all())
        self.assertTrue((rs.index.minute == rng[1].minute).all())
        self.assertTrue((rs.index.second == rng[1].second).all())

        result = ts.at_time('9:30')
        expected = ts.at_time(time(9, 30))
        assert_frame_equal(result, expected)

        result = ts.ix[time(9, 30)]
        expected = ts.ix[(rng.hour == 9) & (rng.minute == 30)]

        assert_frame_equal(result, expected)

        # midnight, everything
        rng = date_range('1/1/2000', '1/31/2000')
        ts = DataFrame(np.random.randn(len(rng), 3), index=rng)

        result = ts.at_time(time(0, 0))
        assert_frame_equal(result, ts)

        # time doesn't exist
        rng = date_range('1/1/2012', freq='23Min', periods=384)
        ts = DataFrame(np.random.randn(len(rng), 2), rng)
        rs = ts.at_time('16:00')
        self.assertEqual(len(rs), 0)

    def test_between_time(self):
        rng = date_range('1/1/2000', '1/5/2000', freq='5min')
        ts = Series(np.random.randn(len(rng)), index=rng)
        stime = time(0, 0)
        etime = time(1, 0)

        close_open = product([True, False], [True, False])
        for inc_start, inc_end in close_open:
            filtered = ts.between_time(stime, etime, inc_start, inc_end)
            exp_len = 13 * 4 + 1
            if not inc_start:
                exp_len -= 5
            if not inc_end:
                exp_len -= 4

            self.assertEqual(len(filtered), exp_len)
            for rs in filtered.index:
                t = rs.time()
                if inc_start:
                    self.assertTrue(t >= stime)
                else:
                    self.assertTrue(t > stime)

                if inc_end:
                    self.assertTrue(t <= etime)
                else:
                    self.assertTrue(t < etime)

        result = ts.between_time('00:00', '01:00')
        expected = ts.between_time(stime, etime)
        assert_series_equal(result, expected)

        # across midnight
        rng = date_range('1/1/2000', '1/5/2000', freq='5min')
        ts = Series(np.random.randn(len(rng)), index=rng)
        stime = time(22, 0)
        etime = time(9, 0)

        close_open = product([True, False], [True, False])
        for inc_start, inc_end in close_open:
            filtered = ts.between_time(stime, etime, inc_start, inc_end)
            exp_len = (12 * 11 + 1) * 4 + 1
            if not inc_start:
                exp_len -= 4
            if not inc_end:
                exp_len -= 4

            self.assertEqual(len(filtered), exp_len)
            for rs in filtered.index:
                t = rs.time()
                if inc_start:
                    self.assertTrue((t >= stime) or (t <= etime))
                else:
                    self.assertTrue((t > stime) or (t <= etime))

                if inc_end:
                    self.assertTrue((t <= etime) or (t >= stime))
                else:
                    self.assertTrue((t < etime) or (t >= stime))

    def test_between_time_frame(self):
        rng = date_range('1/1/2000', '1/5/2000', freq='5min')
        ts = DataFrame(np.random.randn(len(rng), 2), index=rng)
        stime = time(0, 0)
        etime = time(1, 0)

        close_open = product([True, False], [True, False])
        for inc_start, inc_end in close_open:
            filtered = ts.between_time(stime, etime, inc_start, inc_end)
            exp_len = 13 * 4 + 1
            if not inc_start:
                exp_len -= 5
            if not inc_end:
                exp_len -= 4

            self.assertEqual(len(filtered), exp_len)
            for rs in filtered.index:
                t = rs.time()
                if inc_start:
                    self.assertTrue(t >= stime)
                else:
                    self.assertTrue(t > stime)

                if inc_end:
                    self.assertTrue(t <= etime)
                else:
                    self.assertTrue(t < etime)

        result = ts.between_time('00:00', '01:00')
        expected = ts.between_time(stime, etime)
        assert_frame_equal(result, expected)

        # across midnight
        rng = date_range('1/1/2000', '1/5/2000', freq='5min')
        ts = DataFrame(np.random.randn(len(rng), 2), index=rng)
        stime = time(22, 0)
        etime = time(9, 0)

        close_open = product([True, False], [True, False])
        for inc_start, inc_end in close_open:
            filtered = ts.between_time(stime, etime, inc_start, inc_end)
            exp_len = (12 * 11 + 1) * 4 + 1
            if not inc_start:
                exp_len -= 4
            if not inc_end:
                exp_len -= 4

            self.assertEqual(len(filtered), exp_len)
            for rs in filtered.index:
                t = rs.time()
                if inc_start:
                    self.assertTrue((t >= stime) or (t <= etime))
                else:
                    self.assertTrue((t > stime) or (t <= etime))

                if inc_end:
      …