manyi/venv/Lib/site-packages/pandas/tests/resample/test_period_index.py

from datetime import datetime, timedelta

import dateutil
import numpy as np
import pytest
import pytz

from pandas._libs.tslibs.ccalendar import DAYS, MONTHS
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.compat import lrange, range, zip

import pandas as pd
from pandas import DataFrame, Series, Timestamp
from pandas.core.indexes.datetimes import date_range
from pandas.core.indexes.period import Period, PeriodIndex, period_range
from pandas.core.resample import _get_period_range_edges
import pandas.util.testing as tm
from pandas.util.testing import (
    assert_almost_equal, assert_frame_equal, assert_series_equal)

import pandas.tseries.offsets as offsets


@pytest.fixture()
def _index_factory():
    return period_range


@pytest.fixture
def _series_name():
    return 'pi'


class TestPeriodIndex(object):

    @pytest.mark.parametrize('freq', ['2D', '1H', '2H'])
    @pytest.mark.parametrize('kind', ['period', None, 'timestamp'])
    def test_asfreq(self, series_and_frame, freq, kind):
        # GH 12884, 15944
        # make sure .asfreq() returns PeriodIndex (except kind='timestamp')

        obj = series_and_frame
        if kind == 'timestamp':
            expected = obj.to_timestamp().resample(freq).asfreq()
        else:
            start = obj.index[0].to_timestamp(how='start')
            end = (obj.index[-1] + obj.index.freq).to_timestamp(how='start')
            new_index = date_range(start=start, end=end, freq=freq,
                                   closed='left')
            expected = obj.to_timestamp().reindex(new_index).to_period(freq)
        result = obj.resample(freq, kind=kind).asfreq()
        assert_almost_equal(result, expected)

    def test_asfreq_fill_value(self, series):
        # test for fill value during resampling, issue 3715

        s = series
        new_index = date_range(s.index[0].to_timestamp(how='start'),
                               (s.index[-1]).to_timestamp(how='start'),
                               freq='1H')
        expected = s.to_timestamp().reindex(new_index, fill_value=4.0)
        result = s.resample('1H', kind='timestamp').asfreq(fill_value=4.0)
        assert_series_equal(result, expected)

        frame = s.to_frame('value')
        new_index = date_range(frame.index[0].to_timestamp(how='start'),
                               (frame.index[-1]).to_timestamp(how='start'),
                               freq='1H')
        expected = frame.to_timestamp().reindex(new_index, fill_value=3.0)
        result = frame.resample('1H', kind='timestamp').asfreq(fill_value=3.0)
        assert_frame_equal(result, expected)

    @pytest.mark.parametrize('freq', ['H', '12H', '2D', 'W'])
    @pytest.mark.parametrize('kind', [None, 'period', 'timestamp'])
    def test_selection(self, index, freq, kind):
        # This is a bug, these should be implemented
        # GH 14008
        rng = np.arange(len(index), dtype=np.int64)
        df = DataFrame({'date': index, 'a': rng},
                       index=pd.MultiIndex.from_arrays([rng, index],
                                                       names=['v', 'd']))
        with pytest.raises(NotImplementedError):
            df.resample(freq, on='date', kind=kind)
        with pytest.raises(NotImplementedError):
            df.resample(freq, level='d', kind=kind)

    @pytest.mark.parametrize('month', MONTHS)
    @pytest.mark.parametrize('meth', ['ffill', 'bfill'])
    @pytest.mark.parametrize('conv', ['start', 'end'])
    @pytest.mark.parametrize('targ', ['D', 'B', 'M'])
    def test_annual_upsample_cases(self, targ, conv, meth, month,
                                   simple_period_range_series):
        ts = simple_period_range_series(
            '1/1/1990', '12/31/1991', freq='A-%s' % month)

        result = getattr(ts.resample(targ, convention=conv), meth)()
        expected = result.to_timestamp(targ, how=conv)
        expected = expected.asfreq(targ, meth).to_period()
        assert_series_equal(result, expected)

    def test_basic_downsample(self, simple_period_range_series):
        ts = simple_period_range_series('1/1/1990', '6/30/1995', freq='M')
        result = ts.resample('a-dec').mean()

        expected = ts.groupby(ts.index.year).mean()
        expected.index = period_range('1/1/1990', '6/30/1995', freq='a-dec')
        assert_series_equal(result, expected)

        # this is ok
        assert_series_equal(ts.resample('a-dec').mean(), result)
        assert_series_equal(ts.resample('a').mean(), result)

    def test_not_subperiod(self, simple_period_range_series):
        # These are incompatible period rules for resampling
        ts = simple_period_range_series('1/1/1990', '6/30/1995', freq='w-wed')
        pytest.raises(ValueError, lambda: ts.resample('a-dec').mean())
        pytest.raises(ValueError, lambda: ts.resample('q-mar').mean())
        pytest.raises(ValueError, lambda: ts.resample('M').mean())
        pytest.raises(ValueError, lambda: ts.resample('w-thu').mean())

    @pytest.mark.parametrize('freq', ['D', '2D'])
    def test_basic_upsample(self, freq, simple_period_range_series):
        ts = simple_period_range_series('1/1/1990', '6/30/1995', freq='M')
        result = ts.resample('a-dec').mean()

        resampled = result.resample(freq, convention='end').ffill()
        expected = result.to_timestamp(freq, how='end')
        expected = expected.asfreq(freq, 'ffill').to_period(freq)
        assert_series_equal(resampled, expected)

    def test_upsample_with_limit(self):
        rng = period_range('1/1/2000', periods=5, freq='A')
        ts = Series(np.random.randn(len(rng)), rng)

        result = ts.resample('M', convention='end').ffill(limit=2)
        expected = ts.asfreq('M').reindex(result.index, method='ffill',
                                          limit=2)
        assert_series_equal(result, expected)

    def test_annual_upsample(self, simple_period_range_series):
        ts = simple_period_range_series('1/1/1990', '12/31/1995', freq='A-DEC')
        df = DataFrame({'a': ts})
        rdf = df.resample('D').ffill()
        exp = df['a'].resample('D').ffill()
        assert_series_equal(rdf['a'], exp)

        rng = period_range('2000', '2003', freq='A-DEC')
        ts = Series([1, 2, 3, 4], index=rng)

        result = ts.resample('M').ffill()
        ex_index = period_range('2000-01', '2003-12', freq='M')

        expected = ts.asfreq('M', how='start').reindex(ex_index,
                                                       method='ffill')
        assert_series_equal(result, expected)

    @pytest.mark.parametrize('month', MONTHS)
    @pytest.mark.parametrize('target', ['D', 'B', 'M'])
    @pytest.mark.parametrize('convention', ['start', 'end'])
    def test_quarterly_upsample(self, month, target, convention,
                                simple_period_range_series):
        freq = 'Q-{month}'.format(month=month)
        ts = simple_period_range_series('1/1/1990', '12/31/1995', freq=freq)
        result = ts.resample(target, convention=convention).ffill()
        expected = result.to_timestamp(target, how=convention)
        expected = expected.asfreq(target, 'ffill').to_period()
        assert_series_equal(result, expected)

    @pytest.mark.parametrize('target', ['D', 'B'])
    @pytest.mark.parametrize('convention', ['start', 'end'])
    def test_monthly_upsample(self, target, convention,
                              simple_period_range_series):
        ts = simple_period_range_series('1/1/1990', '12/31/1995', freq='M')
        result = ts.resample(target, convention=convention).ffill()
        expected = result.to_timestamp(target, how=convention)
        expected = expected.asfreq(target, 'ffill').to_period()
        assert_series_equal(result, expected)

    def test_resample_basic(self):
        # GH3609
        s = Series(range(100), index=date_range(
            '20130101', freq='s', periods=100, name='idx'), dtype='float')
        s[10:30] = np.nan
        index = PeriodIndex([
            Period('2013-01-01 00:00', 'T'),
            Period('2013-01-01 00:01', 'T')], name='idx')
        expected = Series([34.5, 79.5], index=index)
        result = s.to_period().resample('T', kind='period').mean()
        assert_series_equal(result, expected)
        result2 = s.resample('T', kind='period').mean()
        assert_series_equal(result2, expected)

    @pytest.mark.parametrize('freq,expected_vals', [('M', [31, 29, 31, 9]),
                                                    ('2M', [31 + 29, 31 + 9])])
    def test_resample_count(self, freq, expected_vals):
        # GH12774
        series = Series(1, index=pd.period_range(start='2000', periods=100))
        result = series.resample(freq).count()
        expected_index = pd.period_range(start='2000', freq=freq,
                                         periods=len(expected_vals))
        expected = Series(expected_vals, index=expected_index)
        assert_series_equal(result, expected)

    def test_resample_same_freq(self, resample_method):

        # GH12770
        series = Series(range(3), index=pd.period_range(
            start='2000', periods=3, freq='M'))
        expected = series

        result = getattr(series.resample('M'), resample_method)()
        assert_series_equal(result, expected)

    def test_resample_incompat_freq(self):

        with pytest.raises(IncompatibleFrequency):
            Series(range(3), index=pd.period_range(
                start='2000', periods=3, freq='M')).resample('W').mean()

    def test_with_local_timezone_pytz(self):
        # see gh-5430
        local_timezone = pytz.timezone('America/Los_Angeles')

        start = datetime(year=2013, month=11, day=1, hour=0, minute=0,
                         tzinfo=pytz.utc)
        # 1 day later
        end = datetime(year=2013, month=11, day=2, hour=0, minute=0,
                       tzinfo=pytz.utc)

        index = pd.date_range(start, end, freq='H')

        series = Series(1, index=index)
        series = series.tz_convert(local_timezone)
        result = series.resample('D', kind='period').mean()

        # Create the expected series
        # Index is moved back a day with the timezone conversion from UTC to
        # Pacific
        expected_index = (pd.period_range(start=start, end=end, freq='D') -
                          offsets.Day())
        expected = Series(1, index=expected_index)
        assert_series_equal(result, expected)

    def test_resample_with_pytz(self):
        # GH 13238
        s = Series(2, index=pd.date_range('2017-01-01', periods=48, freq="H",
                                          tz="US/Eastern"))
        result = s.resample("D").mean()
        expected = Series(2, index=pd.DatetimeIndex(['2017-01-01',
                                                     '2017-01-02'],
                                                    tz="US/Eastern"))
        assert_series_equal(result, expected)
        # Especially assert that the timezone is LMT for pytz
        assert result.index.tz == pytz.timezone('US/Eastern')

    def test_with_local_timezone_dateutil(self):
        # see gh-5430
        local_timezone = 'dateutil/America/Los_Angeles'

        start = datetime(year=2013, month=11, day=1, hour=0, minute=0,
                         tzinfo=dateutil.tz.tzutc())
        # 1 day later
        end = datetime(year=2013, month=11, day=2, hour=0, minute=0,
                       tzinfo=dateutil.tz.tzutc())

        index = pd.date_range(start, end, freq='H', name='idx')

        series = Series(1, index=index)
        series = series.tz_convert(local_timezone)
        result = series.resample('D', kind='period').mean()

        # Create the expected series
        # Index is moved back a day with the timezone conversion from UTC to
        # Pacific
        expected_index = (pd.period_range(start=start, end=end, freq='D',
                                          name='idx') - offsets.Day())
        expected = Series(1, index=expected_index)
        assert_series_equal(result, expected)

    def test_resample_nonexistent_time_bin_edge(self):
        # GH 19375
        index = date_range('2017-03-12', '2017-03-12 1:45:00', freq='15T')
        s = Series(np.zeros(len(index)), index=index)
        expected = s.tz_localize('US/Pacific')
        result = expected.resample('900S').mean()
        tm.assert_series_equal(result, expected)

        # GH 23742
        index = date_range(start='2017-10-10', end='2017-10-20', freq='1H')
        index = index.tz_localize('UTC').tz_convert('America/Sao_Paulo')
        df = DataFrame(data=list(range(len(index))), index=index)
        result = df.groupby(pd.Grouper(freq='1D')).count()
        expected = date_range(start='2017-10-09', end='2017-10-20', freq='D',
                              tz="America/Sao_Paulo",
                              nonexistent='shift_forward', closed='left')
        tm.assert_index_equal(result.index, expected)

    def test_resample_ambiguous_time_bin_edge(self):
        # GH 10117
        idx = pd.date_range("2014-10-25 22:00:00", "2014-10-26 00:30:00",
                            freq="30T", tz="Europe/London")
        expected = Series(np.zeros(len(idx)), index=idx)
        result = expected.resample('30T').mean()
        tm.assert_series_equal(result, expected)

    def test_fill_method_and_how_upsample(self):
        # GH2073
        s = Series(np.arange(9, dtype='int64'),
                   index=date_range('2010-01-01', periods=9, freq='Q'))
        last = s.resample('M').ffill()
        both = s.resample('M').ffill().resample('M').last().astype('int64')
        assert_series_equal(last, both)

    @pytest.mark.parametrize('day', DAYS)
    @pytest.mark.parametrize('target', ['D', 'B'])
    @pytest.mark.parametrize('convention', ['start', 'end'])
    def test_weekly_upsample(self, day, target, convention,
                             simple_period_range_series):
        freq = 'W-{day}'.format(day=day)
        ts = simple_period_range_series('1/1/1990', '12/31/1995', freq=freq)
        result = ts.resample(target, convention=convention).ffill()
        expected = result.to_timestamp(target, how=convention)
        expected = expected.asfreq(target, 'ffill').to_period()
        assert_series_equal(result, expected)

    def test_resample_to_timestamps(self, simple_period_range_series):
        ts = simple_period_range_series('1/1/1990', '12/31/1995', freq='M')

        result = ts.resample('A-DEC', kind='timestamp').mean()
        expected = ts.to_timestamp(how='start').resample('A-DEC').mean()
        assert_series_equal(result, expected)

    def test_resample_to_quarterly(self, simple_period_range_series):
        for month in MONTHS:
            ts = simple_period_range_series(
                '1990', '1992', freq='A-%s' % month)
            quar_ts = ts.resample('Q-%s' % month).ffill()

            stamps = ts.to_timestamp('D', how='start')
            qdates = period_range(ts.index[0].asfreq('D', 'start'),
                                  ts.index[-1].asfreq('D', 'end'),
                                  freq='Q-%s' % month)

            expected = stamps.reindex(qdates.to_timestamp('D', 's'),
                                      method='ffill')
            expected.index = qdates

            assert_series_equal(quar_ts, expected)

        # conforms, but different month
        ts = simple_period_range_series('1990', '1992', freq='A-JUN')

        for how in ['start', 'end']:
            result = ts.resample('Q-MAR', convention=how).ffill()
            expected = ts.asfreq('Q-MAR', how=how)
            expected = expected.reindex(result.index, method='ffill')

            # .to_timestamp('D')
            # expected = expected.resample('Q-MAR').ffill()

            assert_series_equal(result, expected)

    def test_resample_fill_missing(self):
        rng = PeriodIndex([2000, 2005, 2007, 2009], freq='A')

        s = Series(np.random.randn(4), index=rng)

        stamps = s.to_timestamp()
        filled = s.resample('A').ffill()
        expected = stamps.resample('A').ffill().to_period('A')
        assert_series_equal(filled, expected)

    def test_cant_fill_missing_dups(self):
        rng = PeriodIndex([2000, 2005, 2005, 2007, 2007], freq='A')
        s = Series(np.random.randn(5), index=rng)
        pytest.raises(Exception, lambda: s.resample('A').ffill())

    @pytest.mark.parametrize('freq', ['5min'])
    @pytest.mark.parametrize('kind', ['period', None, 'timestamp'])
    def test_resample_5minute(self, freq, kind):
        rng = period_range('1/1/2000', '1/5/2000', freq='T')
        ts = Series(np.random.randn(len(rng)), index=rng)
        expected = ts.to_timestamp().resample(freq).mean()
        if kind != 'timestamp':
            expected = expected.to_period(freq)
        result = ts.resample(freq, kind=kind).mean()
        assert_series_equal(result, expected)

    def test_upsample_daily_business_daily(self, simple_period_range_series):
        ts = simple_period_range_series('1/1/2000', '2/1/2000', freq='B')

        result = ts.resample('D').asfreq()
        expected = ts.asfreq('D').reindex(period_range('1/3/2000', '2/1/2000'))
        assert_series_equal(result, expected)

        ts = simple_period_range_series('1/1/2000', '2/1/2000')
        result = ts.resample('H', convention='s').asfreq()
        exp_rng = period_range('1/1/2000', '2/1/2000 23:00', freq='H')
        expected = ts.asfreq('H', how='s').reindex(exp_rng)
        assert_series_equal(result, expected)

    def test_resample_irregular_sparse(self):
        dr = date_range(start='1/1/2012', freq='5min', periods=1000)
        s = Series(np.array(100), index=dr)
        # subset the data.
        subset = s[:'2012-01-04 06:55']

        result = subset.resample('10min').apply(len)
        expected = s.resample('10min').apply(len).loc[result.index]
        assert_series_equal(result, expected)

    def test_resample_weekly_all_na(self):
        rng = date_range('1/1/2000', periods=10, freq='W-WED')
        ts = Series(np.random.randn(len(rng)), index=rng)

        result = ts.resample('W-THU').asfreq()

        assert result.isna().all()

        result = ts.resample('W-THU').asfreq().ffill()[:-1]
        expected = ts.asfreq('W-THU').ffill()
        assert_series_equal(result, expected)

    def test_resample_tz_localized(self):
        dr = date_range(start='2012-4-13', end='2012-5-1')
        ts = Series(lrange(len(dr)), dr)

        ts_utc = ts.tz_localize('UTC')
        ts_local = ts_utc.tz_convert('America/Los_Angeles')

        result = ts_local.resample('W').mean()

        ts_local_naive = ts_local.copy()
        ts_local_naive.index = [x.replace(tzinfo=None)
                                for x in ts_local_naive.index.to_pydatetime()]

        exp = ts_local_naive.resample(
            'W').mean().tz_localize('America/Los_Angeles')

        assert_series_equal(result, exp)

        # it works
        result = ts_local.resample('D').mean()

        # #2245
        idx = date_range('2001-09-20 15:59', '2001-09-20 16:00', freq='T',
                         tz='Australia/Sydney')
        s = Series([1, 2], index=idx)

        result = s.resample('D', closed='right', label='right').mean()
        ex_index = date_range('2001-09-21', periods=1, freq='D',
                              tz='Australia/Sydney')
        expected = Series([1.5], index=ex_index)

        assert_series_equal(result, expected)

        # for good measure
        result = s.resample('D', kind='period').mean()
        ex_index = period_range('2001-09-20', periods=1, freq='D')
        expected = Series([1.5], index=ex_index)
        assert_series_equal(result, expected)

        # GH 6397
        # comparing an offset that doesn't propagate tz's
        rng = date_range('1/1/2011', periods=20000, freq='H')
        rng = rng.tz_localize('EST')
        ts = DataFrame(index=rng)
        ts['first'] = np.random.randn(len(rng))
        ts['second'] = np.cumsum(np.random.randn(len(rng)))
        expected = DataFrame(
            {
                'first': ts.resample('A').sum()['first'],
                'second': ts.resample('A').mean()['second']},
            columns=['first', 'second'])
        result = ts.resample(
            'A').agg({'first': np.sum,
                      'second': np.mean}).reindex(columns=['first', 'second'])
        assert_frame_equal(result, expected)

    def test_closed_left_corner(self):
        # #1465
        s = Series(np.random.randn(21),
                   index=date_range(start='1/1/2012 9:30',
                                    freq='1min', periods=21))
        s[0] = np.nan

        result = s.resample('10min', closed='left', label='right').mean()
        exp = s[1:].resample('10min', closed='left', label='right').mean()
        assert_series_equal(result, exp)

        result = s.resample('10min', closed='left', label='left').mean()
        exp = s[1:].resample('10min', closed='left', label='left').mean()

        ex_index = date_range(start='1/1/2012 9:30', freq='10min', periods=3)

        tm.assert_index_equal(result.index, ex_index)
        assert_series_equal(result, exp)

    def test_quarterly_resampling(self):
        rng = period_range('2000Q1', periods=10, freq='Q-DEC')
        ts = Series(np.arange(10), index=rng)

        result = ts.resample('A').mean()
        exp = ts.to_timestamp().resample('A').mean().to_period()
        assert_series_equal(result, exp)

    def test_resample_weekly_bug_1726(self):
        # 8/6/12 is a Monday
        ind = date_range(start="8/6/2012", end="8/26/2012", freq="D")
        n = len(ind)
        data = [[x] * 5 for x in range(n)]
        df = DataFrame(data, columns=['open', 'high', 'low', 'close', 'vol'],
                       index=ind)

        # it works!
        df.resample('W-MON', closed='left', label='left').first()

    def test_resample_with_dst_time_change(self):
        # GH 15549
        index = (
            pd.DatetimeIndex([1457537600000000000, 1458059600000000000])
            .tz_localize("UTC").tz_convert('America/Chicago')
        )
        df = pd.DataFrame([1, 2], index=index)
        result = df.resample('12h', closed='right',
                             label='right').last().ffill()

        expected_index_values = ['2016-03-09 12:00:00-06:00',
                                 '2016-03-10 00:00:00-06:00',
                                 '2016-03-10 12:00:00-06:00',
                                 '2016-03-11 00:00:00-06:00',
                                 '2016-03-11 12:00:00-06:00',
                                 '2016-03-12 00:00:00-06:00',
                                 '2016-03-12 12:00:00-06:00',
                                 '2016-03-13 00:00:00-06:00',
                                 '2016-03-13 13:00:00-05:00',
                                 '2016-03-14 01:00:00-05:00',
                                 '2016-03-14 13:00:00-05:00',
                                 '2016-03-15 01:00:00-05:00',
                                 '2016-03-15 13:00:00-05:00']
        index = pd.to_datetime(expected_index_values, utc=True).tz_convert(
            'America/Chicago')
        expected = pd.DataFrame([1.0, 1.0, 1.0, 1.0, 1.0,
                                 1.0, 1.0, 1.0, 1.0, 1.0,
                                 1.0, 1.0, 2.0], index=index)
        assert_frame_equal(result, expected)

    def test_resample_bms_2752(self):
        # GH2753
        foo = Series(index=pd.bdate_range('20000101', '20000201'))
        res1 = foo.resample("BMS").mean()
        res2 = foo.resample("BMS").mean().resample("B").mean()
        assert res1.index[0] == Timestamp('20000103')
        assert res1.index[0] == res2.index[0]

    # def test_monthly_convention_span(self):
    #     rng = period_range('2000-01', periods=3, freq='M')
    #     ts = Series(np.arange(3), index=rng)

    #     # hacky way to get same thing
    #     exp_index = period_range('2000-01-01', '2000-03-31', freq='D')
    #     expected = ts.asfreq('D', how='end').reindex(exp_index)
    #     expected = expected.fillna(method='bfill')

    #     result = ts.resample('D', convention='span').mean()

    #     assert_series_equal(result, expected)

    def test_default_right_closed_label(self):
        end_freq = ['D', 'Q', 'M', 'D']
        end_types = ['M', 'A', 'Q', 'W']

        for from_freq, to_freq in zip(end_freq, end_types):
            idx = date_range(start='8/15/2012', periods=100, freq=from_freq)
            df = DataFrame(np.random.randn(len(idx), 2), idx)

            resampled = df.resample(to_freq).mean()
            assert_frame_equal(resampled, df.resample(to_freq, closed='right',
                                                      label='right').mean())

    def test_default_left_closed_label(self):
        others = ['MS', 'AS', 'QS', 'D', 'H']
        others_freq = ['D', 'Q', 'M', 'H', 'T']

        for from_freq, to_freq in zip(others_freq, others):
            idx = date_range(start='8/15/2012', periods=100, freq=from_freq)
            df = DataFrame(np.random.randn(len(idx), 2), idx)

            resampled = df.resample(to_freq).mean()
            assert_frame_equal(resampled, df.resample(to_freq, closed='left',
                                                      label='left').mean())

    def test_all_values_single_bin(self):
        # 2070
        index = period_range(start="2012-01-01", end="2012-12-31", freq="M")
        s = Series(np.random.randn(len(index)), index=index)

        result = s.resample("A").mean()
        tm.assert_almost_equal(result[0], s.mean())

    def test_evenly_divisible_with_no_extra_bins(self):
        # 4076
        # when the frequency is evenly divisible, sometimes extra bins

        df = DataFrame(np.random.randn(9, 3),
                       index=date_range('2000-1-1', periods=9))
        result = df.resample('5D').mean()
        expected = pd.concat(
            [df.iloc[0:5].mean(), df.iloc[5:].mean()], axis=1).T
        expected.index = [Timestamp('2000-1-1'), Timestamp('2000-1-6')]
        assert_frame_equal(result, expected)

        index = date_range(start='2001-5-4', periods=28)
        df = DataFrame(
            [{'REST_KEY': 1, 'DLY_TRN_QT': 80, 'DLY_SLS_AMT': 90,
              'COOP_DLY_TRN_QT': 30, 'COOP_DLY_SLS_AMT': 20}] * 28 +
            [{'REST_KEY': 2, 'DLY_TRN_QT': 70, 'DLY_SLS_AMT': 10,
              'COOP_DLY_TRN_QT': 50, 'COOP_DLY_SLS_AMT': 20}] * 28,
            index=index.append(index)).sort_index()

        index = date_range('2001-5-4', periods=4, freq='7D')
        expected = DataFrame(
            [{'REST_KEY': 14, 'DLY_TRN_QT': 14, 'DLY_SLS_AMT': 14,
              'COOP_DLY_TRN_QT': 14, 'COOP_DLY_SLS_AMT': 14}] * 4,
            index=index)
        result = df.resample('7D').count()
        assert_frame_equal(result, expected)

        expected = DataFrame(
            [{'REST_KEY': 21, 'DLY_TRN_QT': 1050, 'DLY_SLS_AMT': 700,
              'COOP_DLY_TRN_QT': 560, 'COOP_DLY_SLS_AMT': 280}] * 4,
            index=index)
        result = df.resample('7D').sum()
        assert_frame_equal(result, expected)

    @pytest.mark.parametrize('kind', ['period', None, 'timestamp'])
    @pytest.mark.parametrize('agg_arg', ['mean', {'value': 'mean'}, ['mean']])
    def test_loffset_returns_datetimeindex(self, frame, kind, agg_arg):
        # make sure passing loffset returns DatetimeIndex in all cases
        # basic method taken from Base.test_resample_loffset_arg_type()
        df = frame
        expected_means = [df.values[i:i + 2].mean()
                          for i in range(0, len(df.values), 2)]
        expected_index = period_range(
            df.index[0], periods=len(df.index) / 2, freq='2D')

        # loffset coerces PeriodIndex to DateTimeIndex
        expected_index = expected_index.to_timestamp()
        expected_index += timedelta(hours=2)
        expected = DataFrame({'value': expected_means}, index=expected_index)

        result_agg = df.resample('2D', loffset='2H', kind=kind).agg(agg_arg)
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            result_how = df.resample('2D', how=agg_arg, loffset='2H',
                                     kind=kind)
        if isinstance(agg_arg, list):
            expected.columns = pd.MultiIndex.from_tuples([('value', 'mean')])
        assert_frame_equal(result_agg, expected)
        assert_frame_equal(result_how, expected)

    @pytest.mark.parametrize('freq, period_mult', [('H', 24), ('12H', 2)])
    @pytest.mark.parametrize('kind', [None, 'period'])
    def test_upsampling_ohlc(self, freq, period_mult, kind):
        # GH 13083
        pi = period_range(start='2000', freq='D', periods=10)
        s = Series(range(len(pi)), index=pi)
        expected = s.to_timestamp().resample(freq).ohlc().to_period(freq)

        # timestamp-based resampling doesn't include all sub-periods
        # of the last original period, so extend accordingly:
        new_index = period_range(start='2000', freq=freq,
                                 periods=period_mult * len(pi))
        expected = expected.reindex(new_index)
        result = s.resample(freq, kind=kind).ohlc()
        assert_frame_equal(result, expected)

    @pytest.mark.parametrize('periods, values',
                             [([pd.NaT, '1970-01-01 00:00:00', pd.NaT,
                                '1970-01-01 00:00:02', '1970-01-01 00:00:03'],
                               [2, 3, 5, 7, 11]),
                              ([pd.NaT, pd.NaT, '1970-01-01 00:00:00', pd.NaT,
                                pd.NaT, pd.NaT, '1970-01-01 00:00:02',
                                '1970-01-01 00:00:03', pd.NaT, pd.NaT],
                               [1, 2, 3, 5, 6, 8, 7, 11, 12, 13])])
    @pytest.mark.parametrize('freq, expected_values',
                             [('1s', [3, np.NaN, 7, 11]),
                              ('2s', [3, int((7 + 11) / 2)]),
                              ('3s', [int((3 + 7) / 2), 11])])
    def test_resample_with_nat(self, periods, values, freq, expected_values):
        # GH 13224
        index = PeriodIndex(periods, freq='S')
        frame = DataFrame(values, index=index)

        expected_index = period_range('1970-01-01 00:00:00',
                                      periods=len(expected_values), freq=freq)
        expected = DataFrame(expected_values, index=expected_index)
        result = frame.resample(freq).mean()
        assert_frame_equal(result, expected)

    def test_resample_with_only_nat(self):
        # GH 13224
        pi = PeriodIndex([pd.NaT] * 3, freq='S')
        frame = DataFrame([2, 3, 5], index=pi)
        expected_index = PeriodIndex(data=[], freq=pi.freq)
        expected = DataFrame([], index=expected_index)
        result = frame.resample('1s').mean()
        assert_frame_equal(result, expected)

    @pytest.mark.parametrize('start,end,start_freq,end_freq,base', [
        ('19910905', '19910909 03:00', 'H', '24H', 10),
        ('19910905', '19910909 12:00', 'H', '24H', 10),
        ('19910905', '19910909 23:00', 'H', '24H', 10),
        ('19910905 10:00', '19910909', 'H', '24H', 10),
        ('19910905 10:00', '19910909 10:00', 'H', '24H', 10),
        ('19910905', '19910909 10:00', 'H', '24H', 10),
        ('19910905 12:00', '19910909', 'H', '24H', 10),
        ('19910905 12:00', '19910909 03:00', 'H', '24H', 10),
        ('19910905 12:00', '19910909 12:00', 'H', '24H', 10),
        ('19910905 12:00', '19910909 12:00', 'H', '24H', 34),
        ('19910905 12:00', '19910909 12:00', 'H', '17H', 10),
        ('19910905 12:00', '19910909 12:00', 'H', '17H', 3),
        ('19910905 12:00', '19910909 1:00', 'H', 'M', 3),
        ('19910905', '19910913 06:00', '2H', '24H', 10),
        ('19910905', '19910905 01:39', 'Min', '5Min', 3),
        ('19910905', '19910905 03:18', '2Min', '5Min', 3),
    ])
    def test_resample_with_non_zero_base(self, start, end, start_freq,
                                         end_freq, base):
        # GH 23882
        s = pd.Series(0, index=pd.period_range(start, end, freq=start_freq))
        s = s + np.arange(len(s))
        result = s.resample(end_freq, base=base).mean()
        result = result.to_timestamp(end_freq)
        # to_timestamp casts 24H -> D
        result = result.asfreq(end_freq) if end_freq == '24H' else result
        expected = s.to_timestamp().resample(end_freq, base=base).mean()
        assert_series_equal(result, expected)

    @pytest.mark.parametrize('first,last,offset,exp_first,exp_last', [
        ('19910905', '19920406', 'D', '19910905', '19920406'),
        ('19910905 00:00', '19920406 06:00', 'D', '19910905', '19920406'),
        ('19910905 06:00', '19920406 06:00', 'H', '19910905 06:00',
         '19920406 06:00'),
        ('19910906', '19920406', 'M', '1991-09', '1992-04'),
        ('19910831', '19920430', 'M', '1991-08', '1992-04'),
        ('1991-08', '1992-04', 'M', '1991-08', '1992-04'),
    ])
    def test_get_period_range_edges(self, first, last, offset,
                                    exp_first, exp_last):
        first = pd.Period(first)
        last = pd.Period(last)

        exp_first = pd.Period(exp_first, freq=offset)
        exp_last = pd.Period(exp_last, freq=offset)

        offset = pd.tseries.frequencies.to_offset(offset)
        result = _get_period_range_edges(first, last, offset)
        expected = (exp_first, exp_last)
        assert result == expected