content: # pylint: disable=E1101
  
  from warnings import catch_warnings
  from datetime import datetime, timedelta
  from functools import partial
  from textwrap import dedent
  
  import pytz
  import pytest
  import dateutil
  import numpy as np
  
  import pandas as pd
  import pandas.tseries.offsets as offsets
  import pandas.util.testing as tm
  import pandas.util._test_decorators as td
  from pandas import (Series, DataFrame, Panel, Index, isna,
                      notna, Timestamp)
  
  from pandas.core.dtypes.generic import ABCSeries, ABCDataFrame
  from pandas.compat import range, lrange, zip, product, OrderedDict
  from pandas.core.base import SpecificationError, AbstractMethodError
  from pandas.errors import UnsupportedFunctionCall
  from pandas.core.groupby import DataError
  from pandas._libs.tslibs.resolution import DAYS
  from pandas.tseries.frequencies import MONTHS
  from pandas.tseries.frequencies import to_offset
  from pandas.core.indexes.datetimes import date_range
  from pandas.tseries.offsets import Minute, BDay
  from pandas.core.indexes.period import period_range, PeriodIndex, Period
  from pandas.core.resample import (DatetimeIndex, TimeGrouper,
                                    DatetimeIndexResampler)
  from pandas.core.indexes.timedeltas import timedelta_range, TimedeltaIndex
  from pandas.util.testing import (assert_series_equal, assert_almost_equal,
                                   assert_frame_equal, assert_index_equal)
  from pandas._libs.period import IncompatibleFrequency
  
  bday = BDay()
  
  # The various methods we support
  downsample_methods = ['min', 'max', 'first', 'last', 'sum', 'mean', 'sem',
                        'median', 'prod', 'var', 'ohlc']
  upsample_methods = ['count', 'size']
  series_methods = ['nunique']
  resample_methods = downsample_methods + upsample_methods + series_methods
  
  
  def _simple_ts(start, end, freq='D'):
      rng = date_range(start, end, freq=freq)
      return Series(np.random.randn(len(rng)), index=rng)
  
  
  def _simple_pts(start, end, freq='D'):
      rng = period_range(start, end, freq=freq)
      return Series(np.random.randn(len(rng)), index=rng)
  
  
  class TestResampleAPI(object):
  
      def setup_method(self, method):
          dti = DatetimeIndex(start=datetime(2005, 1, 1),
                              end=datetime(2005, 1, 10), freq='Min')
  
          self.series = Series(np.random.rand(len(dti)), dti)
          self.frame = DataFrame(
              {'A': self.series, 'B': self.series, 'C': np.arange(len(dti))})
  
      def test_str(self):
  
          r = self.series.resample('H')
          assert ('DatetimeIndexResampler [freq=<Hour>, axis=0, closed=left, '
                  'label=left, convention=start, base=0]' in str(r))
  
      def test_api(self):
  
          r = self.series.resample('H')
          result = r.mean()
          assert isinstance(result, Series)
          assert len(result) == 217
  
          r = self.series.to_frame().resample('H')
          result = r.mean()
          assert isinstance(result, DataFrame)
          assert len(result) == 217
  
      def test_api_changes_v018(self):
  
          # change from .resample(....., how=...)
          # to .resample(......).how()
  
          r = self.series.resample('H')
          assert isinstance(r, DatetimeIndexResampler)
  
          for how in ['sum', 'mean', 'prod', 'min', 'max', 'var', 'std']:
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = self.series.resample('H', how=how)
                  expected = getattr(self.series.resample('H'), how)()
                  tm.assert_series_equal(result, expected)
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              result = self.series.resample('H', how='ohlc')
              expected = self.series.resample('H').ohlc()
              tm.assert_frame_equal(result, expected)
  
          # compat for pandas-like methods
          for how in ['sort_values', 'isna']:
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  getattr(r, how)()
  
          # invalids as these can be setting operations
          r = self.series.resample('H')
          pytest.raises(ValueError, lambda: r.iloc[0])
          pytest.raises(ValueError, lambda: r.iat[0])
          pytest.raises(ValueError, lambda: r.loc[0])
          pytest.raises(ValueError, lambda: r.loc[
              Timestamp('2013-01-01 00:00:00', offset='H')])
          pytest.raises(ValueError, lambda: r.at[
              Timestamp('2013-01-01 00:00:00', offset='H')])
  
          def f():
              r[0] = 5
  
          pytest.raises(ValueError, f)
  
          # str/repr
          r = self.series.resample('H')
          with tm.assert_produces_warning(None):
              str(r)
          with tm.assert_produces_warning(None):
              repr(r)
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              tm.assert_numpy_array_equal(np.array(r), np.array(r.mean()))
  
          # masquerade as Series/DataFrame as needed for API compat
          assert isinstance(self.series.resample('H'), ABCSeries)
          assert not isinstance(self.frame.resample('H'), ABCSeries)
          assert not isinstance(self.series.resample('H'), ABCDataFrame)
          assert isinstance(self.frame.resample('H'), ABCDataFrame)
  
          # bin numeric ops
          for op in ['__add__', '__mul__', '__truediv__', '__div__', '__sub__']:
  
              if getattr(self.series, op, None) is None:
                  continue
              r = self.series.resample('H')
  
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  assert isinstance(getattr(r, op)(2), Series)
  
          # unary numeric ops
          for op in ['__pos__', '__neg__', '__abs__', '__inv__']:
  
              if getattr(self.series, op, None) is None:
                  continue
              r = self.series.resample('H')
  
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  assert isinstance(getattr(r, op)(), Series)
  
          # comparison ops
          for op in ['__lt__', '__le__', '__gt__', '__ge__', '__eq__', '__ne__']:
              r = self.series.resample('H')
  
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  assert isinstance(getattr(r, op)(2), Series)
  
          # IPython introspection shouldn't trigger warning GH 13618
          for op in ['_repr_json', '_repr_latex',
                     '_ipython_canary_method_should_not_exist_']:
              r = self.series.resample('H')
              with tm.assert_produces_warning(None):
                  getattr(r, op, None)
  
          # getitem compat
          df = self.series.to_frame('foo')
  
          # same as prior versions for DataFrame
          pytest.raises(KeyError, lambda: df.resample('H')[0])
  
          # compat for Series
          # but we cannot be sure that we need a warning here
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              result = self.series.resample('H')[0]
              expected = self.series.resample('H').mean()[0]
              assert result == expected
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              result = self.series.resample('H')['2005-01-09 23:00:00']
              expected = self.series.resample('H').mean()['2005-01-09 23:00:00']
              assert result == expected
  
      def test_groupby_resample_api(self):
  
          # GH 12448
          # .groupby(...).resample(...) hitting warnings
          # when appropriate
          df = DataFrame({'date': pd.date_range(start='2016-01-01',
                                                periods=4,
                                                freq='W'),
                          'group': [1, 1, 2, 2],
                          'val': [5, 6, 7, 8]}).set_index('date')
  
          # replication step
          i = pd.date_range('2016-01-03', periods=8).tolist() + \
              pd.date_range('2016-01-17', periods=8).tolist()
          index = pd.MultiIndex.from_arrays([[1] * 8 + [2] * 8, i],
                                            names=['group', 'date'])
          expected = DataFrame({'val': [5] * 7 + [6] + [7] * 7 + [8]},
                               index=index)
          result = df.groupby('group').apply(
              lambda x: x.resample('1D').ffill())[['val']]
          assert_frame_equal(result, expected)
  
      def test_groupby_resample_on_api(self):
  
          # GH 15021
          # .groupby(...).resample(on=...) results in an unexpected
          # keyword warning.
          df = DataFrame({'key': ['A', 'B'] * 5,
                          'dates': pd.date_range('2016-01-01', periods=10),
                          'values': np.random.randn(10)})
  
          expected = df.set_index('dates').groupby('key').resample('D').mean()
  
          result = df.groupby('key').resample('D', on='dates').mean()
          assert_frame_equal(result, expected)
  
      @td.skip_if_no_mpl
      def test_plot_api(self):
          # .resample(....).plot(...)
          # hitting warnings
          # GH 12448
          s = Series(np.random.randn(60),
                     index=date_range('2016-01-01', periods=60, freq='1min'))
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              result = s.resample('15min').plot()
              tm.assert_is_valid_plot_return_object(result)
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              result = s.resample('15min', how='sum').plot()
              tm.assert_is_valid_plot_return_object(result)
  
      def test_getitem(self):
  
          r = self.frame.resample('H')
          tm.assert_index_equal(r._selected_obj.columns, self.frame.columns)
  
          r = self.frame.resample('H')['B']
          assert r._selected_obj.name == self.frame.columns[1]
  
          # technically this is allowed
          r = self.frame.resample('H')['A', 'B']
          tm.assert_index_equal(r._selected_obj.columns,
                                self.frame.columns[[0, 1]])
  
          r = self.frame.resample('H')['A', 'B']
          tm.assert_index_equal(r._selected_obj.columns,
                                self.frame.columns[[0, 1]])
  
      def test_select_bad_cols(self):
  
          g = self.frame.resample('H')
          pytest.raises(KeyError, g.__getitem__, ['D'])
  
          pytest.raises(KeyError, g.__getitem__, ['A', 'D'])
          with tm.assert_raises_regex(KeyError, '^[^A]+$'):
              # A should not be referenced as a bad column...
              # will have to rethink regex if you change message!
              g[['A', 'D']]
  
      def test_attribute_access(self):
  
          r = self.frame.resample('H')
          tm.assert_series_equal(r.A.sum(), r['A'].sum())
  
          # getting
          pytest.raises(AttributeError, lambda: r.F)
  
          # setting
          def f():
              r.F = 'bah'
  
          pytest.raises(ValueError, f)
  
      def test_api_compat_before_use(self):
  
          # make sure that we are setting the binner
          # on these attributes
          for attr in ['groups', 'ngroups', 'indices']:
              rng = pd.date_range('1/1/2012', periods=100, freq='S')
              ts = Series(np.arange(len(rng)), index=rng)
              rs = ts.resample('30s')
  
              # before use
              getattr(rs, attr)
  
              # after grouper is initialized is ok
              rs.mean()
              getattr(rs, attr)
  
      def tests_skip_nuisance(self):
  
          df = self.frame
          df['D'] = 'foo'
          r = df.resample('H')
          result = r[['A', 'B']].sum()
          expected = pd.concat([r.A.sum(), r.B.sum()], axis=1)
          assert_frame_equal(result, expected)
  
          expected = r[['A', 'B', 'C']].sum()
          result = r.sum()
          assert_frame_equal(result, expected)
  
      def test_downsample_but_actually_upsampling(self):
  
          # this is reindex / asfreq
          rng = pd.date_range('1/1/2012', periods=100, freq='S')
          ts = Series(np.arange(len(rng), dtype='int64'), index=rng)
          result = ts.resample('20s').asfreq()
          expected = Series([0, 20, 40, 60, 80],
                            index=pd.date_range('2012-01-01 00:00:00',
                                                freq='20s',
                                                periods=5))
          assert_series_equal(result, expected)
  
      def test_combined_up_downsampling_of_irregular(self):
  
          # since we are reallydoing an operation like this
          # ts2.resample('2s').mean().ffill()
          # preserve these semantics
  
          rng = pd.date_range('1/1/2012', periods=100, freq='S')
          ts = Series(np.arange(len(rng)), index=rng)
          ts2 = ts.iloc[[0, 1, 2, 3, 5, 7, 11, 15, 16, 25, 30]]
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              result = ts2.resample('2s', how='mean', fill_method='ffill')
          expected = ts2.resample('2s').mean().ffill()
          assert_series_equal(result, expected)
  
      def test_transform(self):
  
          r = self.series.resample('20min')
          expected = self.series.groupby(
              pd.Grouper(freq='20min')).transform('mean')
          result = r.transform('mean')
          assert_series_equal(result, expected)
  
      def test_fillna(self):
  
          # need to upsample here
          rng = pd.date_range('1/1/2012', periods=10, freq='2S')
          ts = Series(np.arange(len(rng), dtype='int64'), index=rng)
          r = ts.resample('s')
  
          expected = r.ffill()
          result = r.fillna(method='ffill')
          assert_series_equal(result, expected)
  
          expected = r.bfill()
          result = r.fillna(method='bfill')
          assert_series_equal(result, expected)
  
          with pytest.raises(ValueError):
              r.fillna(0)
  
      def test_apply_without_aggregation(self):
  
          # both resample and groupby should work w/o aggregation
          r = self.series.resample('20min')
          g = self.series.groupby(pd.Grouper(freq='20min'))
  
          for t in [g, r]:
              result = t.apply(lambda x: x)
              assert_series_equal(result, self.series)
  
      def test_agg_consistency(self):
  
          # make sure that we are consistent across
          # similar aggregations with and w/o selection list
          df = DataFrame(np.random.randn(1000, 3),
                         index=pd.date_range('1/1/2012', freq='S', periods=1000),
                         columns=['A', 'B', 'C'])
  
          r = df.resample('3T')
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              expected = r[['A', 'B', 'C']].agg({'r1': 'mean', 'r2': 'sum'})
              result = r.agg({'r1': 'mean', 'r2': 'sum'})
          assert_frame_equal(result, expected)
  
      # TODO: once GH 14008 is fixed, move these tests into
      # `Base` test class
      def test_agg(self):
          # test with all three Resampler apis and TimeGrouper
  
          np.random.seed(1234)
          index = date_range(datetime(2005, 1, 1),
                             datetime(2005, 1, 10), freq='D')
          index.name = 'date'
          df = DataFrame(np.random.rand(10, 2), columns=list('AB'), index=index)
          df_col = df.reset_index()
          df_mult = df_col.copy()
          df_mult.index = pd.MultiIndex.from_arrays([range(10), df.index],
                                                    names=['index', 'date'])
          r = df.resample('2D')
          cases = [
              r,
              df_col.resample('2D', on='date'),
              df_mult.resample('2D', level='date'),
              df.groupby(pd.Grouper(freq='2D'))
          ]
  
          a_mean = r['A'].mean()
          a_std = r['A'].std()
          a_sum = r['A'].sum()
          b_mean = r['B'].mean()
          b_std = r['B'].std()
          b_sum = r['B'].sum()
  
          expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
          expected.columns = pd.MultiIndex.from_product([['A', 'B'],
                                                         ['mean', 'std']])
          for t in cases:
              result = t.aggregate([np.mean, np.std])
              assert_frame_equal(result, expected)
  
          expected = pd.concat([a_mean, b_std], axis=1)
          for t in cases:
              result = t.aggregate({'A': np.mean,
                                    'B': np.std})
              assert_frame_equal(result, expected, check_like=True)
  
          expected = pd.concat([a_mean, a_std], axis=1)
          expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
                                                        ('A', 'std')])
          for t in cases:
              result = t.aggregate({'A': ['mean', 'std']})
              assert_frame_equal(result, expected)
  
          expected = pd.concat([a_mean, a_sum], axis=1)
          expected.columns = ['mean', 'sum']
          for t in cases:
              result = t['A'].aggregate(['mean', 'sum'])
          assert_frame_equal(result, expected)
  
          expected = pd.concat([a_mean, a_sum], axis=1)
          expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
                                                        ('A', 'sum')])
          for t in cases:
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = t.aggregate({'A': {'mean': 'mean', 'sum': 'sum'}})
              assert_frame_equal(result, expected, check_like=True)
  
          expected = pd.concat([a_mean, a_sum, b_mean, b_sum], axis=1)
          expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
                                                        ('A', 'sum'),
                                                        ('B', 'mean2'),
                                                        ('B', 'sum2')])
          for t in cases:
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = t.aggregate({'A': {'mean': 'mean', 'sum': 'sum'},
                                        'B': {'mean2': 'mean', 'sum2': 'sum'}})
              assert_frame_equal(result, expected, check_like=True)
  
          expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
          expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
                                                        ('A', 'std'),
                                                        ('B', 'mean'),
                                                        ('B', 'std')])
          for t in cases:
              result = t.aggregate({'A': ['mean', 'std'],
                                    'B': ['mean', 'std']})
              assert_frame_equal(result, expected, check_like=True)
  
          expected = pd.concat([a_mean, a_sum, b_mean, b_sum], axis=1)
          expected.columns = pd.MultiIndex.from_tuples([('r1', 'A', 'mean'),
                                                        ('r1', 'A', 'sum'),
                                                        ('r2', 'B', 'mean'),
                                                        ('r2', 'B', 'sum')])
  
      def test_agg_misc(self):
          # test with all three Resampler apis and TimeGrouper
  
          np.random.seed(1234)
          index = date_range(datetime(2005, 1, 1),
                             datetime(2005, 1, 10), freq='D')
          index.name = 'date'
          df = DataFrame(np.random.rand(10, 2), columns=list('AB'), index=index)
          df_col = df.reset_index()
          df_mult = df_col.copy()
          df_mult.index = pd.MultiIndex.from_arrays([range(10), df.index],
                                                    names=['index', 'date'])
  
          r = df.resample('2D')
          cases = [
              r,
              df_col.resample('2D', on='date'),
              df_mult.resample('2D', level='date'),
              df.groupby(pd.Grouper(freq='2D'))
          ]
  
          # passed lambda
          for t in cases:
              result = t.agg({'A': np.sum,
                              'B': lambda x: np.std(x, ddof=1)})
              rcustom = t['B'].apply(lambda x: np.std(x, ddof=1))
              expected = pd.concat([r['A'].sum(), rcustom], axis=1)
              assert_frame_equal(result, expected, check_like=True)
  
          # agg with renamers
          expected = pd.concat([t['A'].sum(),
                                t['B'].sum(),
                                t['A'].mean(),
                                t['B'].mean()],
                               axis=1)
          expected.columns = pd.MultiIndex.from_tuples([('result1', 'A'),
                                                        ('result1', 'B'),
                                                        ('result2', 'A'),
                                                        ('result2', 'B')])
  
          for t in cases:
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = t[['A', 'B']].agg(OrderedDict([('result1', np.sum),
                                                          ('result2', np.mean)]))
              assert_frame_equal(result, expected, check_like=True)
  
          # agg with different hows
          expected = pd.concat([t['A'].sum(),
                                t['A'].std(),
                                t['B'].mean(),
                                t['B'].std()],
                               axis=1)
          expected.columns = pd.MultiIndex.from_tuples([('A', 'sum'),
                                                        ('A', 'std'),
                                                        ('B', 'mean'),
                                                        ('B', 'std')])
          for t in cases:
              result = t.agg(OrderedDict([('A', ['sum', 'std']),
                                          ('B', ['mean', 'std'])]))
              assert_frame_equal(result, expected, check_like=True)
  
          # equivalent of using a selection list / or not
          for t in cases:
              result = t[['A', 'B']].agg({'A': ['sum', 'std'],
                                          'B': ['mean', 'std']})
              assert_frame_equal(result, expected, check_like=True)
  
          # series like aggs
          for t in cases:
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = t['A'].agg({'A': ['sum', 'std']})
              expected = pd.concat([t['A'].sum(),
                                    t['A'].std()],
                                   axis=1)
              expected.columns = pd.MultiIndex.from_tuples([('A', 'sum'),
                                                            ('A', 'std')])
              assert_frame_equal(result, expected, check_like=True)
  
              expected = pd.concat([t['A'].agg(['sum', 'std']),
                                    t['A'].agg(['mean', 'std'])],
                                   axis=1)
              expected.columns = pd.MultiIndex.from_tuples([('A', 'sum'),
                                                            ('A', 'std'),
                                                            ('B', 'mean'),
                                                            ('B', 'std')])
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = t['A'].agg({'A': ['sum', 'std'],
                                       'B': ['mean', 'std']})
              assert_frame_equal(result, expected, check_like=True)
  
          # errors
          # invalid names in the agg specification
          for t in cases:
              def f():
                  with tm.assert_produces_warning(FutureWarning,
                                                  check_stacklevel=False):
                      t[['A']].agg({'A': ['sum', 'std'],
                                    'B': ['mean', 'std']})
  
              pytest.raises(SpecificationError, f)
  
      def test_agg_nested_dicts(self):
  
          np.random.seed(1234)
          index = date_range(datetime(2005, 1, 1),
                             datetime(2005, 1, 10), freq='D')
          index.name = 'date'
          df = DataFrame(np.random.rand(10, 2), columns=list('AB'), index=index)
          df_col = df.reset_index()
          df_mult = df_col.copy()
          df_mult.index = pd.MultiIndex.from_arrays([range(10), df.index],
                                                    names=['index', 'date'])
          r = df.resample('2D')
          cases = [
              r,
              df_col.resample('2D', on='date'),
              df_mult.resample('2D', level='date'),
              df.groupby(pd.Grouper(freq='2D'))
          ]
  
          for t in cases:
              def f():
                  t.aggregate({'r1': {'A': ['mean', 'sum']},
                               'r2': {'B': ['mean', 'sum']}})
                  pytest.raises(ValueError, f)
  
          for t in cases:
              expected = pd.concat([t['A'].mean(), t['A'].std(), t['B'].mean(),
                                    t['B'].std()], axis=1)
              expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
                  'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
  
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = t[['A', 'B']].agg({'A': {'ra': ['mean', 'std']},
                                              'B': {'rb': ['mean', 'std']}})
              assert_frame_equal(result, expected, check_like=True)
  
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = t.agg({'A': {'ra': ['mean', 'std']},
                                  'B': {'rb': ['mean', 'std']}})
              assert_frame_equal(result, expected, check_like=True)
  
      def test_selection_api_validation(self):
          # GH 13500
          index = date_range(datetime(2005, 1, 1),
                             datetime(2005, 1, 10), freq='D')
  
          rng = np.arange(len(index), dtype=np.int64)
          df = DataFrame({'date': index, 'a': rng},
                         index=pd.MultiIndex.from_arrays([rng, index],
                                                         names=['v', 'd']))
          df_exp = DataFrame({'a': rng}, index=index)
  
          # non DatetimeIndex
          with pytest.raises(TypeError):
              df.resample('2D', level='v')
  
          with pytest.raises(ValueError):
              df.resample('2D', on='date', level='d')
  
          with pytest.raises(TypeError):
              df.resample('2D', on=['a', 'date'])
  
          with pytest.raises(KeyError):
              df.resample('2D', level=['a', 'date'])
  
          # upsampling not allowed
          with pytest.raises(ValueError):
              df.resample('2D', level='d').asfreq()
  
          with pytest.raises(ValueError):
              df.resample('2D', on='date').asfreq()
  
          exp = df_exp.resample('2D').sum()
          exp.index.name = 'date'
          assert_frame_equal(exp, df.resample('2D', on='date').sum())
  
          exp.index.name = 'd'
          assert_frame_equal(exp, df.resample('2D', level='d').sum())
  
  
  class Base(object):
      """
      base class for resampling testing, calling
      .create_series() generates a series of each index type
      """
  
      def create_index(self, *args, **kwargs):
          """ return the _index_factory created using the args, kwargs """
          factory = self._index_factory()
          return factory(*args, **kwargs)
  
      @pytest.fixture
      def _index_start(self):
          return datetime(2005, 1, 1)
  
      @pytest.fixture
      def _index_end(self):
          return datetime(2005, 1, 10)
  
      @pytest.fixture
      def _index_freq(self):
          return 'D'
  
      @pytest.fixture
      def index(self, _index_start, _index_end, _index_freq):
          return self.create_index(_index_start, _index_end, freq=_index_freq)
  
      @pytest.fixture
      def _series_name(self):
          raise AbstractMethodError(self)
  
      @pytest.fixture
      def _static_values(self, index):
          return np.arange(len(index))
  
      @pytest.fixture
      def series(self, index, _series_name, _static_values):
          return Series(_static_values, index=index, name=_series_name)
  
      @pytest.fixture
      def frame(self, index, _static_values):
          return DataFrame({'value': _static_values}, index=index)
  
      @pytest.fixture(params=[Series, DataFrame])
      def series_and_frame(self, request, index, _series_name, _static_values):
          if request.param == Series:
              return Series(_static_values, index=index, name=_series_name)
          if request.param == DataFrame:
              return DataFrame({'value': _static_values}, index=index)
  
      @pytest.mark.parametrize('freq', ['2D', '1H'])
      def test_asfreq(self, series_and_frame, freq):
          obj = series_and_frame
  
          result = obj.resample(freq).asfreq()
          if freq == '2D':
              new_index = obj.index.take(np.arange(0, len(obj.index), 2))
              new_index.freq = to_offset('2D')
          else:
              new_index = self.create_index(obj.index[0], obj.index[-1],
                                            freq=freq)
          expected = obj.reindex(new_index)
          assert_almost_equal(result, expected)
  
      def test_asfreq_fill_value(self):
          # test for fill value during resampling, issue 3715
  
          s = self.create_series()
  
          result = s.resample('1H').asfreq()
          new_index = self.create_index(s.index[0], s.index[-1], freq='1H')
          expected = s.reindex(new_index)
          assert_series_equal(result, expected)
  
          frame = s.to_frame('value')
          frame.iloc[1] = None
          result = frame.resample('1H').asfreq(fill_value=4.0)
          new_index = self.create_index(frame.index[0],
                                        frame.index[-1], freq='1H')
          expected = frame.reindex(new_index, fill_value=4.0)
          assert_frame_equal(result, expected)
  
      def test_resample_interpolate(self):
          # # 12925
          df = self.create_series().to_frame('value')
          assert_frame_equal(
              df.resample('1T').asfreq().interpolate(),
              df.resample('1T').interpolate())
  
      def test_raises_on_non_datetimelike_index(self):
          # this is a non datetimelike index
          xp = DataFrame()
          pytest.raises(TypeError, lambda: xp.resample('A').mean())
  
      def test_resample_empty_series(self):
          # GH12771 & GH12868
  
          s = self.create_series()[:0]
  
          for freq in ['M', 'D', 'H']:
              # need to test for ohlc from GH13083
              methods = [method for method in resample_methods
                         if method != 'ohlc']
              for method in methods:
                  result = getattr(s.resample(freq), method)()
  
                  expected = s.copy()
                  expected.index = s.index._shallow_copy(freq=freq)
                  assert_index_equal(result.index, expected.index)
                  assert result.index.freq == expected.index.freq
                  assert_series_equal(result, expected, check_dtype=False)
  
      def test_resample_empty_dataframe(self):
          # GH13212
          index = self.create_series().index[:0]
          f = DataFrame(index=index)
  
          for freq in ['M', 'D', 'H']:
              # count retains dimensions too
              methods = downsample_methods + upsample_methods
              for method in methods:
                  result = getattr(f.resample(freq), method)()
                  if method != 'size':
                      expected = f.copy()
                  else:
                      # GH14962
                      expected = Series([])
  
                  expected.index = f.index._shallow_copy(freq=freq)
                  assert_index_equal(result.index, expected.index)
                  assert result.index.freq == expected.index.freq
                  assert_almost_equal(result, expected, check_dtype=False)
  
              # test size for GH13212 (currently stays as df)
  
      def test_resample_empty_dtypes(self):
  
          # Empty series were sometimes causing a segfault (for the functions
          # with Cython bounds-checking disabled) or an IndexError.  We just run
          # them to ensure they no longer do.  (GH #10228)
          for index in tm.all_timeseries_index_generator(0):
              for dtype in (np.float, np.int, np.object, 'datetime64[ns]'):
                  for how in downsample_methods + upsample_methods:
                      empty_series = Series([], index, dtype)
                      try:
                          getattr(empty_series.resample('d'), how)()
                      except DataError:
                          # Ignore these since some combinations are invalid
                          # (ex: doing mean with dtype of np.object)
                          pass
  
      def test_resample_loffset_arg_type(self):
          # GH 13218, 15002
          df = self.create_series().to_frame('value')
          expected_means = [df.values[i:i + 2].mean()
                            for i in range(0, len(df.values), 2)]
          expected_index = self.create_index(df.index[0],
                                             periods=len(df.index) / 2,
                                             freq='2D')
  
          # loffset coerces PeriodIndex to DateTimeIndex
          if isinstance(expected_index, PeriodIndex):
              expected_index = expected_index.to_timestamp()
  
          expected_index += timedelta(hours=2)
          expected = DataFrame({'value': expected_means}, index=expected_index)
  
          for arg in ['mean', {'value': 'mean'}, ['mean']]:
  
              result_agg = df.resample('2D', loffset='2H').agg(arg)
  
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result_how = df.resample('2D', how=arg, loffset='2H')
  
              if isinstance(arg, list):
                  expected.columns = pd.MultiIndex.from_tuples([('value',
                                                                 'mean')])
  
              # GH 13022, 7687 - TODO: fix resample w/ TimedeltaIndex
              if isinstance(expected.index, TimedeltaIndex):
                  with pytest.raises(AssertionError):
                      assert_frame_equal(result_agg, expected)
                      assert_frame_equal(result_how, expected)
              else:
                  assert_frame_equal(result_agg, expected)
                  assert_frame_equal(result_how, expected)
  
      def test_apply_to_empty_series(self):
          # GH 14313
          series = self.create_series()[:0]
  
          for freq in ['M', 'D', 'H']:
              result = series.resample(freq).apply(lambda x: 1)
              expected = series.resample(freq).apply(np.sum)
  
              assert_series_equal(result, expected, check_dtype=False)
  
  
  class TestDatetimeIndex(Base):
      _index_factory = lambda x: date_range
  
      @pytest.fixture
      def _series_name(self):
          return 'dti'
  
      def setup_method(self, method):
          dti = DatetimeIndex(start=datetime(2005, 1, 1),
                              end=datetime(2005, 1, 10), freq='Min')
  
          self.series = Series(np.random.rand(len(dti)), dti)
  
      def create_series(self):
          i = date_range(datetime(2005, 1, 1),
                         datetime(2005, 1, 10), freq='D')
  
          return Series(np.arange(len(i)), index=i, name='dti')
  
      def test_custom_grouper(self):
  
          dti = DatetimeIndex(freq='Min', start=datetime(2005, 1, 1),
                              end=datetime(2005, 1, 10))
  
          s = Series(np.array([1] * len(dti)), index=dti, dtype='int64')
  
          b = TimeGrouper(Minute(5))
          g = s.groupby(b)
  
          # check all cython functions work
          funcs = ['add', 'mean', 'prod', 'ohlc', 'min', 'max', 'var']
          for f in funcs:
              g._cython_agg_general(f)
  
          b = TimeGrouper(Minute(5), closed='right', label='right')
          g = s.groupby(b)
          # check all cython functions work
          funcs = ['add', 'mean', 'prod', 'ohlc', 'min', 'max', 'var']
          for f in funcs:
              g._cython_agg_general(f)
  
          assert g.ngroups == 2593
          assert notna(g.mean()).all()
  
          # construct expected val
          arr = [1] + [5] * 2592
          idx = dti[0:-1:5]
          idx = idx.append(dti[-1:])
          expect = Series(arr, index=idx)
  
          # GH2763 - return in put dtype if we can
          result = g.agg(np.sum)
          assert_series_equal(result, expect)
  
          df = DataFrame(np.random.rand(len(dti), 10),
                         index=dti, dtype='float64')
          r = df.groupby(b).agg(np.sum)
  
          assert len(r.columns) == 10
          assert len(r.index) == 2593
  
      def test_resample_basic(self):
          rng = date_range('1/1/2000 00:00:00', '1/1/2000 00:13:00', freq='min',
                           name='index')
          s = Series(np.random.randn(14), index=rng)
          result = s.resample('5min', closed='right', label='right').mean()
  
          exp_idx = date_range('1/1/2000', periods=4, freq='5min', name='index')
          expected = Series([s[0], s[1:6].mean(), s[6:11].mean(), s[11:].mean()],
                            index=exp_idx)
          assert_series_equal(result, expected)
          assert result.index.name == 'index'
  
          result = s.resample('5min', closed='left', label='right').mean()
  
          exp_idx = date_range('1/1/2000 00:05', periods=3, freq='5min',
                               name='index')
          expected = Series([s[:5].mean(), s[5:10].mean(),
                             s[10:].mean()], index=exp_idx)
          assert_series_equal(result, expected)
  
          s = self.series
          result = s.resample('5Min').last()
          grouper = TimeGrouper(Minute(5), closed='left', label='left')
          expect = s.groupby(grouper).agg(lambda x: x[-1])
          assert_series_equal(result, expect)
  
      def test_resample_how(self):
          rng = date_range('1/1/2000 00:00:00', '1/1/2000 00:13:00', freq='min',
                           name='index')
          s = Series(np.random.randn(14), index=rng)
          grouplist = np.ones_like(s)
          grouplist[0] = 0
          grouplist[1:6] = 1
          grouplist[6:11] = 2
          grouplist[11:] = 3
          args = downsample_methods
  
          def _ohlc(group):
              if isna(group).all():
                  return np.repeat(np.nan, 4)
              return [group[0], group.max(), group.min(), group[-1]]
  
          inds = date_range('1/1/2000', periods=4, freq='5min', name='index')
  
          for arg in args:
              if arg == 'ohlc':
                  func = _ohlc
              else:
                  func = arg
              try:
                  result = getattr(s.resample(
                      '5min', closed='right', label='right'), arg)()
  
                  expected = s.groupby(grouplist).agg(func)
                  assert result.index.name == 'index'
                  if arg == 'ohlc':
                      expected = DataFrame(expected.values.tolist())
                      expected.columns = ['open', 'high', 'low', 'close']
                      expected.index = Index(inds, name='index')
                      assert_frame_equal(result, expected)
                  else:
                      expected.index = inds
                      assert_series_equal(result, expected)
              except BaseException as exc:
  
                  exc.args += ('how=%s' % arg,)
                  raise
  
      def test_numpy_compat(self):
          # see gh-12811
          s = Series([1, 2, 3, 4, 5], index=date_range(
              '20130101', periods=5, freq='s'))
          r = s.resample('2s')
  
          msg = "numpy operations are not valid with resample"
  
          for func in ('min', 'max', 'sum', 'prod',
                       'mean', 'var', 'std'):
              tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                                     getattr(r, func),
                                     func, 1, 2, 3)
              tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                                     getattr(r, func), axis=1)
  
      def test_resample_how_callables(self):
          # GH 7929
          data = np.arange(5, dtype=np.int64)
          ind = pd.DatetimeIndex(start='2014-01-01', periods=len(data), freq='d')
          df = DataFrame({"A": data, "B": data}, index=ind)
  
          def fn(x, a=1):
              return str(type(x))
  
          class fn_class:
  
              def __call__(self, x):
                  return str(type(x))
  
          df_standard = df.resample("M").apply(fn)
          df_lambda = df.resample("M").apply(lambda x: str(type(x)))
          df_partial = df.resample("M").apply(partial(fn))
          df_partial2 = df.resample("M").apply(partial(fn, a=2))
          df_class = df.resample("M").apply(fn_class())
  
          assert_frame_equal(df_standard, df_lambda)
          assert_frame_equal(df_standard, df_partial)
          assert_frame_equal(df_standard, df_partial2)
          assert_frame_equal(df_standard, df_class)
  
      def test_resample_with_timedeltas(self):
  
          expected = DataFrame({'A': np.arange(1480)})
          expected = expected.groupby(expected.index // 30).sum()
          expected.index = pd.timedelta_range('0 days', freq='30T', periods=50)
  
          df = DataFrame({'A': np.arange(1480)}, index=pd.to_timedelta(
              np.arange(1480), unit='T'))
          result = df.resample('30T').sum()
  
          assert_frame_equal(result, expected)
  
          s = df['A']
          result = s.resample('30T').sum()
          assert_series_equal(result, expected['A'])
  
      def test_resample_single_period_timedelta(self):
  
          s = Series(list(range(5)), index=pd.timedelta_range(
              '1 day', freq='s', periods=5))
          result = s.resample('2s').sum()
          expected = Series([1, 5, 4], index=pd.timedelta_range(
              '1 day', freq='2s', periods=3))
          assert_series_equal(result, expected)
  
      def test_resample_timedelta_idempotency(self):
  
          # GH 12072
          index = pd.timedelta_range('0', periods=9, freq='10L')
          series = Series(range(9), index=index)
          result = series.resample('10L').mean()
          expected = series
          assert_series_equal(result, expected)
  
      def test_resample_rounding(self):
          # GH 8371
          # odd results when rounding is needed
  
          data = """date,time,value
  11-08-2014,00:00:01.093,1
  11-08-2014,00:00:02.159,1
  11-08-2014,00:00:02.667,1
  11-08-2014,00:00:03.175,1
  11-08-2014,00:00:07.058,1
  11-08-2014,00:00:07.362,1
  11-08-2014,00:00:08.324,1
  11-08-2014,00:00:08.830,1
  11-08-2014,00:00:08.982,1
  11-08-2014,00:00:09.815,1
  11-08-2014,00:00:10.540,1
  11-08-2014,00:00:11.061,1
  11-08-2014,00:00:11.617,1
  11-08-2014,00:00:13.607,1
  11-08-2014,00:00:14.535,1
  11-08-2014,00:00:15.525,1
  11-08-2014,00:00:17.960,1
  11-08-2014,00:00:20.674,1
  11-08-2014,00:00:21.191,1"""
  
          from pandas.compat import StringIO
          df = pd.read_csv(StringIO(data), parse_dates={'timestamp': [
              'date', 'time']}, index_col='timestamp')
          df.index.name = None
          result = df.resample('6s').sum()
          expected = DataFrame({'value': [
              4, 9, 4, 2
          ]}, index=date_range('2014-11-08', freq='6s', periods=4))
          assert_frame_equal(result, expected)
  
          result = df.resample('7s').sum()
          expected = DataFrame({'value': [
              4, 10, 4, 1
          ]}, index=date_range('2014-11-08', freq='7s', periods=4))
          assert_frame_equal(result, expected)
  
          result = df.resample('11s').sum()
          expected = DataFrame({'value': [
              11, 8
          ]}, index=date_range('2014-11-08', freq='11s', periods=2))
          assert_frame_equal(result, expected)
  
          result = df.resample('13s').sum()
          expected = DataFrame({'value': [
              13, 6
          ]}, index=date_range('2014-11-08', freq='13s', periods=2))
          assert_frame_equal(result, expected)
  
          result = df.resample('17s').sum()
          expected = DataFrame({'value': [
              16, 3
          ]}, index=date_range('2014-11-08', freq='17s', periods=2))
          assert_frame_equal(result, expected)
  
      def test_resample_basic_from_daily(self):
          # from daily
          dti = DatetimeIndex(start=datetime(2005, 1, 1),
                              end=datetime(2005, 1, 10), freq='D', name='index')
  
          s = Series(np.random.rand(len(dti)), dti)
  
          # to weekly
          result = s.resample('w-sun').last()
  
          assert len(result) == 3
          assert (result.index.dayofweek == [6, 6, 6]).all()
          assert result.iloc[0] == s['1/2/2005']
          assert result.iloc[1] == s['1/9/2005']
          assert result.iloc[2] == s.iloc[-1]
  
          result = s.resample('W-MON').last()
          assert len(result) == 2
          assert (result.index.dayofweek == [0, 0]).all()
          assert result.iloc[0] == s['1/3/2005']
          assert result.iloc[1] == s['1/10/2005']
  
          result = s.resample('W-TUE').last()
          assert len(result) == 2
          assert (result.index.dayofweek == [1, 1]).all()
          assert result.iloc[0] == s['1/4/2005']
          assert result.iloc[1] == s['1/10/2005']
  
          result = s.resample('W-WED').last()
          assert len(result) == 2
          assert (result.index.dayofweek == [2, 2]).all()
          assert result.iloc[0] == s['1/5/2005']
          assert result.iloc[1] == s['1/10/2005']
  
          result = s.resample('W-THU').last()
          assert len(result) == 2
          assert (result.index.dayofweek == [3, 3]).all()
          assert result.iloc[0] == s['1/6/2005']
          assert result.iloc[1] == s['1/10/2005']
  
          result = s.resample('W-FRI').last()
          assert len(result) == 2
          assert (result.index.dayofweek == [4, 4]).all()
          assert result.iloc[0] == s['1/7/2005']
          assert result.iloc[1] == s['1/10/2005']
  
          # to biz day
          result = s.resample('B').last()
          assert len(result) == 7
          assert (result.index.dayofweek == [4, 0, 1, 2, 3, 4, 0]).all()
  
          assert result.iloc[0] == s['1/2/2005']
          assert result.iloc[1] == s['1/3/2005']
          assert result.iloc[5] == s['1/9/2005']
          assert result.index.name == 'index'
  
      def test_resample_upsampling_picked_but_not_correct(self):
  
          # Test for issue #3020
          dates = date_range('01-Jan-2014', '05-Jan-2014', freq='D')
          series = Series(1, index=dates)
  
          result = series.resample('D').mean()
          assert result.index[0] == dates[0]
  
          # GH 5955
          # incorrect deciding to upsample when the axis frequency matches the
          # resample frequency
  
          import datetime
          s = Series(np.arange(1., 6), index=[datetime.datetime(
              1975, 1, i, 12, 0) for i in range(1, 6)])
          expected = Series(np.arange(1., 6), index=date_range(
              '19750101', periods=5, freq='D'))
  
          result = s.resample('D').count()
          assert_series_equal(result, Series(1, index=expected.index))
  
          result1 = s.resample('D').sum()
          result2 = s.resample('D').mean()
          assert_series_equal(result1, expected)
          assert_series_equal(result2, expected)
  
      def test_resample_frame_basic(self):
          df = tm.makeTimeDataFrame()
  
          b = TimeGrouper('M')
          g = df.groupby(b)
  
          # check all cython functions work
          funcs = ['add', 'mean', 'prod', 'min', 'max', 'var']
          for f in funcs:
              g._cython_agg_general(f)
  
          result = df.resample('A').mean()
          assert_series_equal(result['A'], df['A'].resample('A').mean())
  
          result = df.resample('M').mean()
          assert_series_equal(result['A'], df['A'].resample('M').mean())
  
          df.resample('M', kind='period').mean()
          df.resample('W-WED', kind='period').mean()
  
      def test_resample_loffset(self):
          rng = date_range('1/1/2000 00:00:00', '1/1/2000 00:13:00', freq='min')
          s = Series(np.random.randn(14), index=rng)
  
          result = s.resample('5min', closed='right', label='right',
                              loffset=timedelta(minutes=1)).mean()
          idx = date_range('1/1/2000', periods=4, freq='5min')
          expected = Series([s[0], s[1:6].mean(), s[6:11].mean(), s[11:].mean()],
                            index=idx + timedelta(minutes=1))
          assert_series_equal(result, expected)
  
          expected = s.resample(
              '5min', closed='right', label='right',
              loffset='1min').mean()
          assert_series_equal(result, expected)
  
          expected = s.resample(
              '5min', closed='right', label='right',
              loffset=Minute(1)).mean()
          assert_series_equal(result, expected)
  
          assert result.index.freq == Minute(5)
  
          # from daily
          dti = DatetimeIndex(start=datetime(2005, 1, 1),
                              end=datetime(2005, 1, 10), freq='D')
          ser = Series(np.random.rand(len(dti)), dti)
  
          # to weekly
          result = ser.resample('w-sun').last()
          expected = ser.resample('w-sun', loffset=-bday).last()
          assert result.index[0] - bday == expected.index[0]
  
      def test_resample_loffset_count(self):
          # GH 12725
          start_time = '1/1/2000 00:00:00'
          rng = date_range(start_time, periods=100, freq='S')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          result = ts.resample('10S', loffset='1s').count()
  
          expected_index = (
              date_range(start_time, periods=10, freq='10S') +
              timedelta(seconds=1)
          )
          expected = Series(10, index=expected_index)
  
          assert_series_equal(result, expected)
  
          # Same issue should apply to .size() since it goes through
          #   same code path
          result = ts.resample('10S', loffset='1s').size()
  
          assert_series_equal(result, expected)
  
      def test_resample_upsample(self):
          # from daily
          dti = DatetimeIndex(start=datetime(2005, 1, 1),
                              end=datetime(2005, 1, 10), freq='D', name='index')
  
          s = Series(np.random.rand(len(dti)), dti)
  
          # to minutely, by padding
          result = s.resample('Min').pad()
          assert len(result) == 12961
          assert result[0] == s[0]
          assert result[-1] == s[-1]
  
          assert result.index.name == 'index'
  
      def test_resample_how_method(self):
          # GH9915
          s = Series([11, 22],
                     index=[Timestamp('2015-03-31 21:48:52.672000'),
                            Timestamp('2015-03-31 21:49:52.739000')])
          expected = Series([11, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, 22],
                            index=[Timestamp('2015-03-31 21:48:50'),
                                   Timestamp('2015-03-31 21:49:00'),
                                   Timestamp('2015-03-31 21:49:10'),
                                   Timestamp('2015-03-31 21:49:20'),
                                   Timestamp('2015-03-31 21:49:30'),
                                   Timestamp('2015-03-31 21:49:40'),
                                   Timestamp('2015-03-31 21:49:50')])
          assert_series_equal(s.resample("10S").mean(), expected)
  
      def test_resample_extra_index_point(self):
          # GH 9756
          index = DatetimeIndex(start='20150101', end='20150331', freq='BM')
          expected = DataFrame({'A': Series([21, 41, 63], index=index)})
  
          index = DatetimeIndex(start='20150101', end='20150331', freq='B')
          df = DataFrame(
              {'A': Series(range(len(index)), index=index)}, dtype='int64')
          result = df.resample('BM').last()
          assert_frame_equal(result, expected)
  
      def test_upsample_with_limit(self):
          rng = date_range('1/1/2000', periods=3, freq='5t')
          ts = Series(np.random.randn(len(rng)), rng)
  
          result = ts.resample('t').ffill(limit=2)
          expected = ts.reindex(result.index, method='ffill', limit=2)
          assert_series_equal(result, expected)
  
      def test_nearest_upsample_with_limit(self):
          rng = date_range('1/1/2000', periods=3, freq='5t')
          ts = Series(np.random.randn(len(rng)), rng)
  
          result = ts.resample('t').nearest(limit=2)
          expected = ts.reindex(result.index, method='nearest', limit=2)
          assert_series_equal(result, expected)
  
      def test_resample_ohlc(self):
          s = self.series
  
          grouper = TimeGrouper(Minute(5))
          expect = s.groupby(grouper).agg(lambda x: x[-1])
          result = s.resample('5Min').ohlc()
  
          assert len(result) == len(expect)
          assert len(result.columns) == 4
  
          xs = result.iloc[-2]
          assert xs['open'] == s[-6]
          assert xs['high'] == s[-6:-1].max()
          assert xs['low'] == s[-6:-1].min()
          assert xs['close'] == s[-2]
  
          xs = result.iloc[0]
          assert xs['open'] == s[0]
          assert xs['high'] == s[:5].max()
          assert xs['low'] == s[:5].min()
          assert xs['close'] == s[4]
  
      def test_resample_ohlc_result(self):
  
          # GH 12332
          index = pd.date_range('1-1-2000', '2-15-2000', freq='h')
          index = index.union(pd.date_range('4-15-2000', '5-15-2000', freq='h'))
          s = Series(range(len(index)), index=index)
  
          a = s.loc[:'4-15-2000'].resample('30T').ohlc()
          assert isinstance(a, DataFrame)
  
          b = s.loc[:'4-14-2000'].resample('30T').ohlc()
          assert isinstance(b, DataFrame)
  
          # GH12348
          # raising on odd period
          rng = date_range('2013-12-30', '2014-01-07')
          index = rng.drop([Timestamp('2014-01-01'),
                            Timestamp('2013-12-31'),
                            Timestamp('2014-01-04'),
                            Timestamp('2014-01-05')])
          df = DataFrame(data=np.arange(len(index)), index=index)
          result = df.resample('B').mean()
          expected = df.reindex(index=date_range(rng[0], rng[-1], freq='B'))
          assert_frame_equal(result, expected)
  
      def test_resample_ohlc_dataframe(self):
          df = (
              DataFrame({
                  'PRICE': {
                      Timestamp('2011-01-06 10:59:05', tz=None): 24990,
                      Timestamp('2011-01-06 12:43:33', tz=None): 25499,
                      Timestamp('2011-01-06 12:54:09', tz=None): 25499},
                  'VOLUME': {
                      Timestamp('2011-01-06 10:59:05', tz=None): 1500000000,
                      Timestamp('2011-01-06 12:43:33', tz=None): 5000000000,
                      Timestamp('2011-01-06 12:54:09', tz=None): 100000000}})
          ).reindex(['VOLUME', 'PRICE'], axis=1)
          res = df.resample('H').ohlc()
          exp = pd.concat([df['VOLUME'].resample('H').ohlc(),
                           df['PRICE'].resample('H').ohlc()],
                          axis=1,
                          keys=['VOLUME', 'PRICE'])
          assert_frame_equal(exp, res)
  
          df.columns = [['a', 'b'], ['c', 'd']]
          res = df.resample('H').ohlc()
          exp.columns = pd.MultiIndex.from_tuples([
              ('a', 'c', 'open'), ('a', 'c', 'high'), ('a', 'c', 'low'),
              ('a', 'c', 'close'), ('b', 'd', 'open'), ('b', 'd', 'high'),
              ('b', 'd', 'low'), ('b', 'd', 'close')])
          assert_frame_equal(exp, res)
  
          # dupe columns fail atm
          # df.columns = ['PRICE', 'PRICE']
  
      def test_resample_dup_index(self):
  
          # GH 4812
          # dup columns with resample raising
          df = DataFrame(np.random.randn(4, 12), index=[2000, 2000, 2000, 2000],
                         columns=[Period(year=2000, month=i + 1, freq='M')
                                  for i in range(12)])
          df.iloc[3, :] = np.nan
          result = df.resample('Q', axis=1).mean()
          expected = df.groupby(lambda x: int((x.month - 1) / 3), axis=1).mean()
          expected.columns = [
              Period(year=2000, quarter=i + 1, freq='Q') for i in range(4)]
          assert_frame_equal(result, expected)
  
      def test_resample_reresample(self):
          dti = DatetimeIndex(start=datetime(2005, 1, 1),
                              end=datetime(2005, 1, 10), freq='D')
          s = Series(np.random.rand(len(dti)), dti)
          bs = s.resample('B', closed='right', label='right').mean()
          result = bs.resample('8H').mean()
          assert len(result) == 22
          assert isinstance(result.index.freq, offsets.DateOffset)
          assert result.index.freq == offsets.Hour(8)
  
      def test_resample_timestamp_to_period(self):
          ts = _simple_ts('1/1/1990', '1/1/2000')
  
          result = ts.resample('A-DEC', kind='period').mean()
          expected = ts.resample('A-DEC').mean()
          expected.index = period_range('1990', '2000', freq='a-dec')
          assert_series_equal(result, expected)
  
          result = ts.resample('A-JUN', kind='period').mean()
          expected = ts.resample('A-JUN').mean()
          expected.index = period_range('1990', '2000', freq='a-jun')
          assert_series_equal(result, expected)
  
          result = ts.resample('M', kind='period').mean()
          expected = ts.resample('M').mean()
          expected.index = period_range('1990-01', '2000-01', freq='M')
          assert_series_equal(result, expected)
  
          result = ts.resample('M', kind='period').mean()
          expected = ts.resample('M').mean()
          expected.index = period_range('1990-01', '2000-01', freq='M')
          assert_series_equal(result, expected)
  
      def test_ohlc_5min(self):
          def _ohlc(group):
              if isna(group).all():
                  return np.repeat(np.nan, 4)
              return [group[0], group.max(), group.min(), group[-1]]
  
          rng = date_range('1/1/2000 00:00:00', '1/1/2000 5:59:50', freq='10s')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          resampled = ts.resample('5min', closed='right',
                                  label='right').ohlc()
  
          assert (resampled.loc['1/1/2000 00:00'] == ts[0]).all()
  
          exp = _ohlc(ts[1:31])
          assert (resampled.loc['1/1/2000 00:05'] == exp).all()
  
          exp = _ohlc(ts['1/1/2000 5:55:01':])
          assert (resampled.loc['1/1/2000 6:00:00'] == exp).all()
  
      def test_downsample_non_unique(self):
          rng = date_range('1/1/2000', '2/29/2000')
          rng2 = rng.repeat(5).values
          ts = Series(np.random.randn(len(rng2)), index=rng2)
  
          result = ts.resample('M').mean()
  
          expected = ts.groupby(lambda x: x.month).mean()
          assert len(result) == 2
          assert_almost_equal(result[0], expected[1])
          assert_almost_equal(result[1], expected[2])
  
      def test_asfreq_non_unique(self):
          # GH #1077
          rng = date_range('1/1/2000', '2/29/2000')
          rng2 = rng.repeat(2).values
          ts = Series(np.random.randn(len(rng2)), index=rng2)
  
          pytest.raises(Exception, ts.asfreq, 'B')
  
      def test_resample_axis1(self):
          rng = date_range('1/1/2000', '2/29/2000')
          df = DataFrame(np.random.randn(3, len(rng)), columns=rng,
                         index=['a', 'b', 'c'])
  
          result = df.resample('M', axis=1).mean()
          expected = df.T.resample('M').mean().T
          tm.assert_frame_equal(result, expected)
  
      def test_resample_panel(self):
          rng = date_range('1/1/2000', '6/30/2000')
          n = len(rng)
  
          with catch_warnings(record=True):
              panel = Panel(np.random.randn(3, n, 5),
                            items=['one', 'two', 'three'],
                            major_axis=rng,
                            minor_axis=['a', 'b', 'c', 'd', 'e'])
  
              result = panel.resample('M', axis=1).mean()
  
              def p_apply(panel, f):
                  result = {}
                  for item in panel.items:
                      result[item] = f(panel[item])
                  return Panel(result, items=panel.items)
  
              expected = p_apply(panel, lambda x: x.resample('M').mean())
              tm.assert_panel_equal(result, expected)
  
              panel2 = panel.swapaxes(1, 2)
              result = panel2.resample('M', axis=2).mean()
              expected = p_apply(panel2,
                                 lambda x: x.resample('M', axis=1).mean())
              tm.assert_panel_equal(result, expected)
  
      def test_resample_panel_numpy(self):
          rng = date_range('1/1/2000', '6/30/2000')
          n = len(rng)
  
          with catch_warnings(record=True):
              panel = Panel(np.random.randn(3, n, 5),
                            items=['one', 'two', 'three'],
                            major_axis=rng,
                            minor_axis=['a', 'b', 'c', 'd', 'e'])
  
              result = panel.resample('M', axis=1).apply(lambda x: x.mean(1))
              expected = panel.resample('M', axis=1).mean()
              tm.assert_panel_equal(result, expected)
  
              panel = panel.swapaxes(1, 2)
              result = panel.resample('M', axis=2).apply(lambda x: x.mean(2))
              expected = panel.resample('M', axis=2).mean()
              tm.assert_panel_equal(result, expected)
  
      def test_resample_anchored_ticks(self):
          # If a fixed delta (5 minute, 4 hour) evenly divides a day, we should
          # "anchor" the origin at midnight so we get regular intervals rather
          # than starting from the first timestamp which might start in the
          # middle of a desired interval
  
          rng = date_range('1/1/2000 04:00:00', periods=86400, freq='s')
          ts = Series(np.random.randn(len(rng)), index=rng)
          ts[:2] = np.nan  # so results are the same
  
          freqs = ['t', '5t', '15t', '30t', '4h', '12h']
          for freq in freqs:
              result = ts[2:].resample(freq, closed='left', label='left').mean()
              expected = ts.resample(freq, closed='left', label='left').mean()
              assert_series_equal(result, expected)
  
      def test_resample_single_group(self):
          mysum = lambda x: x.sum()
  
          rng = date_range('2000-1-1', '2000-2-10', freq='D')
          ts = Series(np.random.randn(len(rng)), index=rng)
          assert_series_equal(ts.resample('M').sum(),
                              ts.resample('M').apply(mysum))
  
          rng = date_range('2000-1-1', '2000-1-10', freq='D')
          ts = Series(np.random.randn(len(rng)), index=rng)
          assert_series_equal(ts.resample('M').sum(),
                              ts.resample('M').apply(mysum))
  
          # GH 3849
          s = Series([30.1, 31.6], index=[Timestamp('20070915 15:30:00'),
                                          Timestamp('20070915 15:40:00')])
          expected = Series([0.75], index=[Timestamp('20070915')])
          result = s.resample('D').apply(lambda x: np.std(x))
          assert_series_equal(result, expected)
  
      def test_resample_base(self):
          rng = date_range('1/1/2000 00:00:00', '1/1/2000 02:00', freq='s')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          resampled = ts.resample('5min', base=2).mean()
          exp_rng = date_range('12/31/1999 23:57:00', '1/1/2000 01:57',
                               freq='5min')
          tm.assert_index_equal(resampled.index, exp_rng)
  
      def test_resample_base_with_timedeltaindex(self):
  
          # GH 10530
          rng = timedelta_range(start='0s', periods=25, freq='s')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          with_base = ts.resample('2s', base=5).mean()
          without_base = ts.resample('2s').mean()
  
          exp_without_base = timedelta_range(start='0s', end='25s', freq='2s')
          exp_with_base = timedelta_range(start='5s', end='29s', freq='2s')
  
          tm.assert_index_equal(without_base.index, exp_without_base)
          tm.assert_index_equal(with_base.index, exp_with_base)
  
      def test_resample_categorical_data_with_timedeltaindex(self):
          # GH #12169
          df = DataFrame({'Group_obj': 'A'},
                         index=pd.to_timedelta(list(range(20)), unit='s'))
          df['Group'] = df['Group_obj'].astype('category')
          result = df.resample('10s').agg(lambda x: (x.value_counts().index[0]))
          expected = DataFrame({'Group_obj': ['A', 'A'],
                                'Group': ['A', 'A']},
                               index=pd.to_timedelta([0, 10], unit='s'))
          expected = expected.reindex(['Group_obj', 'Group'], axis=1)
          tm.assert_frame_equal(result, expected)
  
      def test_resample_daily_anchored(self):
          rng = date_range('1/1/2000 0:00:00', periods=10000, freq='T')
          ts = Series(np.random.randn(len(rng)), index=rng)
          ts[:2] = np.nan  # so results are the same
  
          result = ts[2:].resample('D', closed='left', label='left').mean()
          expected = ts.resample('D', closed='left', label='left').mean()
          assert_series_equal(result, expected)
  
      def test_resample_to_period_monthly_buglet(self):
          # GH #1259
  
          rng = date_range('1/1/2000', '12/31/2000')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          result = ts.resample('M', kind='period').mean()
          exp_index = period_range('Jan-2000', 'Dec-2000', freq='M')
          tm.assert_index_equal(result.index, exp_index)
  
      def test_period_with_agg(self):
  
          # aggregate a period resampler with a lambda
          s2 = Series(np.random.randint(0, 5, 50),
                      index=pd.period_range('2012-01-01', freq='H', periods=50),
                      dtype='float64')
  
          expected = s2.to_timestamp().resample('D').mean().to_period()
          result = s2.resample('D').agg(lambda x: x.mean())
          assert_series_equal(result, expected)
  
      def test_resample_segfault(self):
          # GH 8573
          # segfaulting in older versions
          all_wins_and_wagers = [
              (1, datetime(2013, 10, 1, 16, 20), 1, 0),
              (2, datetime(2013, 10, 1, 16, 10), 1, 0),
              (2, datetime(2013, 10, 1, 18, 15), 1, 0),
              (2, datetime(2013, 10, 1, 16, 10, 31), 1, 0)]
  
          df = DataFrame.from_records(all_wins_and_wagers,
                                      columns=("ID", "timestamp", "A", "B")
                                      ).set_index("timestamp")
          result = df.groupby("ID").resample("5min").sum()
          expected = df.groupby("ID").apply(lambda x: x.resample("5min").sum())
          assert_frame_equal(result, expected)
  
      def test_resample_dtype_preservation(self):
  
          # GH 12202
          # validation tests for dtype preservation
  
          df = DataFrame({'date': pd.date_range(start='2016-01-01',
                                                periods=4, freq='W'),
                          'group': [1, 1, 2, 2],
                          'val': Series([5, 6, 7, 8],
                                        dtype='int32')}
                         ).set_index('date')
  
          result = df.resample('1D').ffill()
          assert result.val.dtype == np.int32
  
          result = df.groupby('group').resample('1D').ffill()
          assert result.val.dtype == np.int32
  
      def test_resample_dtype_coerceion(self):
  
          pytest.importorskip('scipy.interpolate')
  
          # GH 16361
          df = {"a": [1, 3, 1, 4]}
          df = DataFrame(df, index=pd.date_range("2017-01-01", "2017-01-04"))
  
          expected = (df.astype("float64")
                      .resample("H")
                      .mean()
                      ["a"]
                      .interpolate("cubic")
                      )
  
          result = df.resample("H")["a"].mean().interpolate("cubic")
          tm.assert_series_equal(result, expected)
  
          result = df.resample("H").mean()["a"].interpolate("cubic")
          tm.assert_series_equal(result, expected)
  
      def test_weekly_resample_buglet(self):
          # #1327
          rng = date_range('1/1/2000', freq='B', periods=20)
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          resampled = ts.resample('W').mean()
          expected = ts.resample('W-SUN').mean()
          assert_series_equal(resampled, expected)
  
      def test_monthly_resample_error(self):
          # #1451
          dates = date_range('4/16/2012 20:00', periods=5000, freq='h')
          ts = Series(np.random.randn(len(dates)), index=dates)
          # it works!
          ts.resample('M')
  
      def test_nanosecond_resample_error(self):
          # GH 12307 - Values falls after last bin when
          # Resampling using pd.tseries.offsets.Nano as period
          start = 1443707890427
          exp_start = 1443707890400
          indx = pd.date_range(
              start=pd.to_datetime(start),
              periods=10,
              freq='100n'
          )
          ts = Series(range(len(indx)), index=indx)
          r = ts.resample(pd.tseries.offsets.Nano(100))
          result = r.agg('mean')
  
          exp_indx = pd.date_range(
              start=pd.to_datetime(exp_start),
              periods=10,
              freq='100n'
          )
          exp = Series(range(len(exp_indx)), index=exp_indx)
  
          assert_series_equal(result, exp)
  
      def test_resample_anchored_intraday(self):
          # #1471, #1458
  
          rng = date_range('1/1/2012', '4/1/2012', freq='100min')
          df = DataFrame(rng.month, index=rng)
  
          result = df.resample('M').mean()
          expected = df.resample(
              'M', kind='period').mean().to_timestamp(how='end')
          tm.assert_frame_equal(result, expected)
  
          result = df.resample('M', closed='left').mean()
          exp = df.tshift(1, freq='D').resample('M', kind='period').mean()
          exp = exp.to_timestamp(how='end')
  
          tm.assert_frame_equal(result, exp)
  
          rng = date_range('1/1/2012', '4/1/2012', freq='100min')
          df = DataFrame(rng.month, index=rng)
  
          result = df.resample('Q').mean()
          expected = df.resample(
              'Q', kind='period').mean().to_timestamp(how='end')
          tm.assert_frame_equal(result, expected)
  
          result = df.resample('Q', closed='left').mean()
          expected = df.tshift(1, freq='D').resample('Q', kind='period',
                                                     closed='left').mean()
          expected = expected.to_timestamp(how='end')
          tm.assert_frame_equal(result, expected)
  
          ts = _simple_ts('2012-04-29 23:00', '2012-04-30 5:00', freq='h')
          resampled = ts.resample('M').mean()
          assert len(resampled) == 1
  
      def test_resample_anchored_monthstart(self):
          ts = _simple_ts('1/1/2000', '12/31/2002')
  
          freqs = ['MS', 'BMS', 'QS-MAR', 'AS-DEC', 'AS-JUN']
  
          for freq in freqs:
              ts.resample(freq).mean()
  
      def test_resample_anchored_multiday(self):
          # When resampling a range spanning multiple days, ensure that the
          # start date gets used to determine the offset.  Fixes issue where
          # a one day period is not a multiple of the frequency.
          #
          # See: https://github.com/pandas-dev/pandas/issues/8683
  
          index = pd.date_range(
              '2014-10-14 23:06:23.206', periods=3, freq='400L'
          ) | pd.date_range(
              '2014-10-15 23:00:00', periods=2, freq='2200L')
  
          s = Series(np.random.randn(5), index=index)
  
          # Ensure left closing works
          result = s.resample('2200L').mean()
          assert result.index[-1] == Timestamp('2014-10-15 23:00:02.000')
  
          # Ensure right closing works
          result = s.resample('2200L', label='right').mean()
          assert result.index[-1] == Timestamp('2014-10-15 23:00:04.200')
  
      def test_corner_cases(self):
          # miscellaneous test coverage
  
          rng = date_range('1/1/2000', periods=12, freq='t')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          result = ts.resample('5t', closed='right', label='left').mean()
          ex_index = date_range('1999-12-31 23:55', periods=4, freq='5t')
          tm.assert_index_equal(result.index, ex_index)
  
          len0pts = _simple_pts('2007-01', '2010-05', freq='M')[:0]
          # it works
          result = len0pts.resample('A-DEC').mean()
          assert len(result) == 0
  
          # resample to periods
          ts = _simple_ts('2000-04-28', '2000-04-30 11:00', freq='h')
          result = ts.resample('M', kind='period').mean()
          assert len(result) == 1
          assert result.index[0] == Period('2000-04', freq='M')
  
      def test_anchored_lowercase_buglet(self):
          dates = date_range('4/16/2012 20:00', periods=50000, freq='s')
          ts = Series(np.random.randn(len(dates)), index=dates)
          # it works!
          ts.resample('d').mean()
  
      def test_upsample_apply_functions(self):
          # #1596
          rng = pd.date_range('2012-06-12', periods=4, freq='h')
  
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          result = ts.resample('20min').aggregate(['mean', 'sum'])
          assert isinstance(result, DataFrame)
  
      def test_resample_not_monotonic(self):
          rng = pd.date_range('2012-06-12', periods=200, freq='h')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          ts = ts.take(np.random.permutation(len(ts)))
  
          result = ts.resample('D').sum()
          exp = ts.sort_index().resample('D').sum()
          assert_series_equal(result, exp)
  
      def test_resample_median_bug_1688(self):
  
          for dtype in ['int64', 'int32', 'float64', 'float32']:
              df = DataFrame([1, 2], index=[datetime(2012, 1, 1, 0, 0, 0),
                                            datetime(2012, 1, 1, 0, 5, 0)],
                             dtype=dtype)
  
              result = df.resample("T").apply(lambda x: x.mean())
              exp = df.asfreq('T')
              tm.assert_frame_equal(result, exp)
  
              result = df.resample("T").median()
              exp = df.asfreq('T')
              tm.assert_frame_equal(result, exp)
  
      def test_how_lambda_functions(self):
  
          ts = _simple_ts('1/1/2000', '4/1/2000')
  
          result = ts.resample('M').apply(lambda x: x.mean())
          exp = ts.resample('M').mean()
          tm.assert_series_equal(result, exp)
  
          foo_exp = ts.resample('M').mean()
          foo_exp.name = 'foo'
          bar_exp = ts.resample('M').std()
          bar_exp.name = 'bar'
  
          result = ts.resample('M').apply(
              [lambda x: x.mean(), lambda x: x.std(ddof=1)])
          result.columns = ['foo', 'bar']
          tm.assert_series_equal(result['foo'], foo_exp)
          tm.assert_series_equal(result['bar'], bar_exp)
  
          # this is a MI Series, so comparing the names of the results
          # doesn't make sense
          result = ts.resample('M').aggregate({'foo': lambda x: x.mean(),
                                               'bar': lambda x: x.std(ddof=1)})
          tm.assert_series_equal(result['foo'], foo_exp, check_names=False)
          tm.assert_series_equal(result['bar'], bar_exp, check_names=False)
  
      def test_resample_unequal_times(self):
          # #1772
          start = datetime(1999, 3, 1, 5)
          # end hour is less than start
          end = datetime(2012, 7, 31, 4)
          bad_ind = date_range(start, end, freq="30min")
          df = DataFrame({'close': 1}, index=bad_ind)
  
          # it works!
          df.resample('AS').sum()
  
      def test_resample_consistency(self):
  
          # GH 6418
          # resample with bfill / limit / reindex consistency
  
          i30 = pd.date_range('2002-02-02', periods=4, freq='30T')
          s = Series(np.arange(4.), index=i30)
          s[2] = np.NaN
  
          # Upsample by factor 3 with reindex() and resample() methods:
          i10 = pd.date_range(i30[0], i30[-1], freq='10T')
  
          s10 = s.reindex(index=i10, method='bfill')
          s10_2 = s.reindex(index=i10, method='bfill', limit=2)
          rl = s.reindex_like(s10, method='bfill', limit=2)
          r10_2 = s.resample('10Min').bfill(limit=2)
          r10 = s.resample('10Min').bfill()
  
          # s10_2, r10, r10_2, rl should all be equal
          assert_series_equal(s10_2, r10)
          assert_series_equal(s10_2, r10_2)
          assert_series_equal(s10_2, rl)
  
      def test_resample_timegrouper(self):
          # GH 7227
          dates1 = [datetime(2014, 10, 1), datetime(2014, 9, 3),
                    datetime(2014, 11, 5), datetime(2014, 9, 5),
                    datetime(2014, 10, 8), datetime(2014, 7, 15)]
  
          dates2 = dates1[:2] + [pd.NaT] + dates1[2:4] + [pd.NaT] + dates1[4:]
          dates3 = [pd.NaT] + dates1 + [pd.NaT]
  
          for dates in [dates1, dates2, dates3]:
              df = DataFrame(dict(A=dates, B=np.arange(len(dates))))
              result = df.set_index('A').resample('M').count()
              exp_idx = pd.DatetimeIndex(['2014-07-31', '2014-08-31',
                                          '2014-09-30',
                                          '2014-10-31', '2014-11-30'],
                                         freq='M', name='A')
              expected = DataFrame({'B': [1, 0, 2, 2, 1]}, index=exp_idx)
              assert_frame_equal(result, expected)
  
              result = df.groupby(pd.Grouper(freq='M', key='A')).count()
              assert_frame_equal(result, expected)
  
              df = DataFrame(dict(A=dates, B=np.arange(len(dates)), C=np.arange(
                  len(dates))))
              result = df.set_index('A').resample('M').count()
              expected = DataFrame({'B': [1, 0, 2, 2, 1], 'C': [1, 0, 2, 2, 1]},
                                   index=exp_idx, columns=['B', 'C'])
              assert_frame_equal(result, expected)
  
              result = df.groupby(pd.Grouper(freq='M', key='A')).count()
              assert_frame_equal(result, expected)
  
      def test_resample_nunique(self):
  
          # GH 12352
          df = DataFrame({
              'ID': {Timestamp('2015-06-05 00:00:00'): '0010100903',
                     Timestamp('2015-06-08 00:00:00'): '0010150847'},
              'DATE': {Timestamp('2015-06-05 00:00:00'): '2015-06-05',
                       Timestamp('2015-06-08 00:00:00'): '2015-06-08'}})
          r = df.resample('D')
          g = df.groupby(pd.Grouper(freq='D'))
          expected = df.groupby(pd.Grouper(freq='D')).ID.apply(lambda x:
                                                               x.nunique())
          assert expected.name == 'ID'
  
          for t in [r, g]:
              result = r.ID.nunique()
              assert_series_equal(result, expected)
  
          result = df.ID.resample('D').nunique()
          assert_series_equal(result, expected)
  
          result = df.ID.groupby(pd.Grouper(freq='D')).nunique()
          assert_series_equal(result, expected)
  
      def test_resample_nunique_with_date_gap(self):
          # GH 13453
          index = pd.date_range('1-1-2000', '2-15-2000', freq='h')
          index2 = pd.date_range('4-15-2000', '5-15-2000', freq='h')
          index3 = index.append(index2)
          s = Series(range(len(index3)), index=index3, dtype='int64')
          r = s.resample('M')
  
          # Since all elements are unique, these should all be the same
          results = [
              r.count(),
              r.nunique(),
              r.agg(Series.nunique),
              r.agg('nunique')
          ]
  
          assert_series_equal(results[0], results[1])
          assert_series_equal(results[0], results[2])
          assert_series_equal(results[0], results[3])
  
      def test_resample_group_info(self):  # GH10914
          for n, k in product((10000, 100000), (10, 100, 1000)):
              dr = date_range(start='2015-08-27', periods=n // 10, freq='T')
              ts = Series(np.random.randint(0, n // k, n).astype('int64'),
                          index=np.random.choice(dr, n))
  
              left = ts.resample('30T').nunique()
              ix = date_range(start=ts.index.min(), end=ts.index.max(),
                              freq='30T')
  
              vals = ts.values
              bins = np.searchsorted(ix.values, ts.index, side='right')
  
              sorter = np.lexsort((vals, bins))
              vals, bins = vals[sorter], bins[sorter]
  
              mask = np.r_[True, vals[1:] != vals[:-1]]
              mask |= np.r_[True, bins[1:] != bins[:-1]]
  
              arr = np.bincount(bins[mask] - 1,
                                minlength=len(ix)).astype('int64', copy=False)
              right = Series(arr, index=ix)
  
              assert_series_equal(left, right)
  
      def test_resample_size(self):
          n = 10000
          dr = date_range('2015-09-19', periods=n, freq='T')
          ts = Series(np.random.randn(n), index=np.random.choice(dr, n))
  
          left = ts.resample('7T').size()
          ix = date_range(start=left.index.min(), end=ts.index.max(), freq='7T')
  
          bins = np.searchsorted(ix.values, ts.index.values, side='right')
          val = np.bincount(bins, minlength=len(ix) + 1)[1:].astype('int64',
                                                                    copy=False)
  
          right = Series(val, index=ix)
          assert_series_equal(left, right)
  
      def test_resample_across_dst(self):
          # The test resamples a DatetimeIndex with values before and after a
          # DST change
          # Issue: 14682
  
          # The DatetimeIndex we will start with
          # (note that DST happens at 03:00+02:00 -> 02:00+01:00)
          # 2016-10-30 02:23:00+02:00, 2016-10-30 02:23:00+01:00
          df1 = DataFrame([1477786980, 1477790580], columns=['ts'])
          dti1 = DatetimeIndex(pd.to_datetime(df1.ts, unit='s')
                               .dt.tz_localize('UTC')
                               .dt.tz_convert('Europe/Madrid'))
  
          # The expected DatetimeIndex after resampling.
          # 2016-10-30 02:00:00+02:00, 2016-10-30 02:00:00+01:00
          df2 = DataFrame([1477785600, 1477789200], columns=['ts'])
          dti2 = DatetimeIndex(pd.to_datetime(df2.ts, unit='s')
                               .dt.tz_localize('UTC')
                               .dt.tz_convert('Europe/Madrid'))
          df = DataFrame([5, 5], index=dti1)
  
          result = df.resample(rule='H').sum()
          expected = DataFrame([5, 5], index=dti2)
  
          assert_frame_equal(result, expected)
  
      def test_resample_dst_anchor(self):
          # 5172
          dti = DatetimeIndex([datetime(2012, 11, 4, 23)], tz='US/Eastern')
          df = DataFrame([5], index=dti)
          assert_frame_equal(df.resample(rule='D').sum(),
                             DataFrame([5], index=df.index.normalize()))
          df.resample(rule='MS').sum()
          assert_frame_equal(
              df.resample(rule='MS').sum(),
              DataFrame([5], index=DatetimeIndex([datetime(2012, 11, 1)],
                                                 tz='US/Eastern')))
  
          dti = date_range('2013-09-30', '2013-11-02', freq='30Min',
                           tz='Europe/Paris')
          values = range(dti.size)
          df = DataFrame({"a": values,
                          "b": values,
                          "c": values}, index=dti, dtype='int64')
          how = {"a": "min", "b": "max", "c": "count"}
  
          assert_frame_equal(
              df.resample("W-MON").agg(how)[["a", "b", "c"]],
              DataFrame({"a": [0, 48, 384, 720, 1056, 1394],
                         "b": [47, 383, 719, 1055, 1393, 1586],
                         "c": [48, 336, 336, 336, 338, 193]},
                        index=date_range('9/30/2013', '11/4/2013',
                                         freq='W-MON', tz='Europe/Paris')),
              'W-MON Frequency')
  
          assert_frame_equal(
              df.resample("2W-MON").agg(how)[["a", "b", "c"]],
              DataFrame({"a": [0, 48, 720, 1394],
                         "b": [47, 719, 1393, 1586],
                         "c": [48, 672, 674, 193]},
                        index=date_range('9/30/2013', '11/11/2013',
                                         freq='2W-MON', tz='Europe/Paris')),
              '2W-MON Frequency')
  
          assert_frame_equal(
              df.resample("MS").agg(how)[["a", "b", "c"]],
              DataFrame({"a": [0, 48, 1538],
                         "b": [47, 1537, 1586],
                         "c": [48, 1490, 49]},
                        index=date_range('9/1/2013', '11/1/2013',
                                         freq='MS', tz='Europe/Paris')),
              'MS Frequency')
  
          assert_frame_equal(
              df.resample("2MS").agg(how)[["a", "b", "c"]],
              DataFrame({"a": [0, 1538],
                         "b": [1537, 1586],
                         "c": [1538, 49]},
                        index=date_range('9/1/2013', '11/1/2013',
                                         freq='2MS', tz='Europe/Paris')),
              '2MS Frequency')
  
          df_daily = df['10/26/2013':'10/29/2013']
          assert_frame_equal(
              df_daily.resample("D").agg({"a": "min", "b": "max", "c": "count"})
              [["a", "b", "c"]],
              DataFrame({"a": [1248, 1296, 1346, 1394],
                         "b": [1295, 1345, 1393, 1441],
                         "c": [48, 50, 48, 48]},
                        index=date_range('10/26/2013', '10/29/2013',
                                         freq='D', tz='Europe/Paris')),
              'D Frequency')
  
      def test_resample_with_nat(self):
          # GH 13020
          index = DatetimeIndex([pd.NaT,
                                 '1970-01-01 00:00:00',
                                 pd.NaT,
                                 '1970-01-01 00:00:01',
                                 '1970-01-01 00:00:02'])
          frame = DataFrame([2, 3, 5, 7, 11], index=index)
  
          index_1s = DatetimeIndex(['1970-01-01 00:00:00',
                                    '1970-01-01 00:00:01',
                                    '1970-01-01 00:00:02'])
          frame_1s = DataFrame([3, 7, 11], index=index_1s)
          assert_frame_equal(frame.resample('1s').mean(), frame_1s)
  
          index_2s = DatetimeIndex(['1970-01-01 00:00:00',
                                    '1970-01-01 00:00:02'])
          frame_2s = DataFrame([5, 11], index=index_2s)
          assert_frame_equal(frame.resample('2s').mean(), frame_2s)
  
          index_3s = DatetimeIndex(['1970-01-01 00:00:00'])
          frame_3s = DataFrame([7], index=index_3s)
          assert_frame_equal(frame.resample('3s').mean(), frame_3s)
  
          assert_frame_equal(frame.resample('60s').mean(), frame_3s)
  
      def test_resample_timedelta_values(self):
          # GH 13119
          # check that timedelta dtype is preserved when NaT values are
          # introduced by the resampling
  
          times = timedelta_range('1 day', '4 day', freq='4D')
          df = DataFrame({'time': times}, index=times)
  
          times2 = timedelta_range('1 day', '4 day', freq='2D')
          exp = Series(times2, index=times2, name='time')
          exp.iloc[1] = pd.NaT
  
          res = df.resample('2D').first()['time']
          tm.assert_series_equal(res, exp)
          res = df['time'].resample('2D').first()
          tm.assert_series_equal(res, exp)
  
      def test_resample_datetime_values(self):
          # GH 13119
          # check that datetime dtype is preserved when NaT values are
          # introduced by the resampling
  
          dates = [datetime(2016, 1, 15), datetime(2016, 1, 19)]
          df = DataFrame({'timestamp': dates}, index=dates)
  
          exp = Series([datetime(2016, 1, 15), pd.NaT, datetime(2016, 1, 19)],
                       index=date_range('2016-01-15', periods=3, freq='2D'),
                       name='timestamp')
  
          res = df.resample('2D').first()['timestamp']
          tm.assert_series_equal(res, exp)
          res = df['timestamp'].resample('2D').first()
          tm.assert_series_equal(res, exp)
  
  
  class TestPeriodIndex(Base):
      _index_factory = lambda x: period_range
  
      @pytest.fixture
      def _series_name(self):
          return 'pi'
  
      def create_series(self):
          # TODO: replace calls to .create_series() by injecting the series
          # fixture
          i = period_range(datetime(2005, 1, 1),
                           datetime(2005, 1, 10), freq='D')
  
          return Series(np.arange(len(i)), index=i, name='pi')
  
      @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] + 1).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):
          # test for fill value during resampling, issue 3715
  
          s = self.create_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)
  
      def test_annual_upsample_D_s_f(self):
          self._check_annual_upsample_cases('D', 'start', 'ffill')
  
      def test_annual_upsample_D_e_f(self):
          self._check_annual_upsample_cases('D', 'end', 'ffill')
  
      def test_annual_upsample_D_s_b(self):
          self._check_annual_upsample_cases('D', 'start', 'bfill')
  
      def test_annual_upsample_D_e_b(self):
          self._check_annual_upsample_cases('D', 'end', 'bfill')
  
      def test_annual_upsample_B_s_f(self):
          self._check_annual_upsample_cases('B', 'start', 'ffill')
  
      def test_annual_upsample_B_e_f(self):
          self._check_annual_upsample_cases('B', 'end', 'ffill')
  
      def test_annual_upsample_B_s_b(self):
          self._check_annual_upsample_cases('B', 'start', 'bfill')
  
      def test_annual_upsample_B_e_b(self):
          self._check_annual_upsample_cases('B', 'end', 'bfill')
  
      def test_annual_upsample_M_s_f(self):
          self._check_annual_upsample_cases('M', 'start', 'ffill')
  
      def test_annual_upsample_M_e_f(self):
          self._check_annual_upsample_cases('M', 'end', 'ffill')
  
      def test_annual_upsample_M_s_b(self):
          self._check_annual_upsample_cases('M', 'start', 'bfill')
  
      def test_annual_upsample_M_e_b(self):
          self._check_annual_upsample_cases('M', 'end', 'bfill')
  
      def _check_annual_upsample_cases(self, targ, conv, meth, end='12/31/1991'):
          for month in MONTHS:
              ts = _simple_pts('1/1/1990', end, 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):
          ts = _simple_pts('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):
          # These are incompatible period rules for resampling
          ts = _simple_pts('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):
          ts = _simple_pts('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):
          ts = _simple_pts('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)
  
      def test_quarterly_upsample(self):
          targets = ['D', 'B', 'M']
  
          for month in MONTHS:
              ts = _simple_pts('1/1/1990', '12/31/1995', freq='Q-%s' % month)
  
              for targ, conv in product(targets, ['start', 'end']):
                  result = ts.resample(targ, convention=conv).ffill()
                  expected = result.to_timestamp(targ, how=conv)
                  expected = expected.asfreq(targ, 'ffill').to_period()
                  assert_series_equal(result, expected)
  
      def test_monthly_upsample(self):
          targets = ['D', 'B']
  
          ts = _simple_pts('1/1/1990', '12/31/1995', freq='M')
  
          for targ, conv in product(targets, ['start', 'end']):
              result = ts.resample(targ, convention=conv).ffill()
              expected = result.to_timestamp(targ, how=conv)
              expected = expected.asfreq(targ, '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):
  
          # GH12770
          series = Series(range(3), index=pd.period_range(
              start='2000', periods=3, freq='M'))
          expected = series
  
          for method in resample_methods:
              result = getattr(series.resample('M'), 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') - 1)
          expected = Series(1, index=expected_index)
          assert_series_equal(result, expected)
  
      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') - 1)
          expected = Series(1, index=expected_index)
          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)
  
      def test_weekly_upsample(self):
          targets = ['D', 'B']
  
          for day in DAYS:
              ts = _simple_pts('1/1/1990', '12/31/1995', freq='W-%s' % day)
  
              for targ, conv in product(targets, ['start', 'end']):
                  result = ts.resample(targ, convention=conv).ffill()
                  expected = result.to_timestamp(targ, how=conv)
                  expected = expected.asfreq(targ, 'ffill').to_period()
                  assert_series_equal(result, expected)
  
      def test_resample_to_timestamps(self):
          ts = _simple_pts('1/1/1990', '12/31/1995', freq='M')
  
          result = ts.resample('A-DEC', kind='timestamp').mean()
          expected = ts.to_timestamp(how='end').resample('A-DEC').mean()
          assert_series_equal(result, expected)
  
      def test_resample_to_quarterly(self):
          for month in MONTHS:
              ts = _simple_pts('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_pts('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):
          ts = _simple_pts('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_pts('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 = DatetimeIndex(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='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.DatetimeIndex(expected_index_values,
                                   tz='UTC').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 = DatetimeIndex(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 = DatetimeIndex(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 = self.create_index(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 = PeriodIndex(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 = PeriodIndex(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)
  
  
  class TestTimedeltaIndex(Base):
      _index_factory = lambda x: timedelta_range
  
      @pytest.fixture
      def _index_start(self):
          return '1 day'
  
      @pytest.fixture
      def _index_end(self):
          return '10 day'
  
      @pytest.fixture
      def _series_name(self):
          return 'tdi'
  
      def create_series(self):
          i = timedelta_range('1 day',
                              '10 day', freq='D')
  
          return Series(np.arange(len(i)), index=i, name='tdi')
  
      def test_asfreq_bug(self):
          import datetime as dt
          df = DataFrame(data=[1, 3],
                         index=[dt.timedelta(), dt.timedelta(minutes=3)])
          result = df.resample('1T').asfreq()
          expected = DataFrame(data=[1, np.nan, np.nan, 3],
                               index=timedelta_range('0 day',
                                                     periods=4,
                                                     freq='1T'))
          assert_frame_equal(result, expected)
  
  
  class TestResamplerGrouper(object):
  
      def setup_method(self, method):
          self.frame = DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                                  'B': np.arange(40)},
                                 index=date_range('1/1/2000',
                                                  freq='s',
                                                  periods=40))
  
      def test_back_compat_v180(self):
  
          df = self.frame
          for how in ['sum', 'mean', 'prod', 'min', 'max', 'var', 'std']:
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = df.groupby('A').resample('4s', how=how)
                  expected = getattr(df.groupby('A').resample('4s'), how)()
                  assert_frame_equal(result, expected)
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              result = df.groupby('A').resample('4s', how='mean',
                                                fill_method='ffill')
              expected = df.groupby('A').resample('4s').mean().ffill()
              assert_frame_equal(result, expected)
  
      def test_tab_complete_ipython6_warning(self, ip):
          from IPython.core.completer import provisionalcompleter
          code = dedent("""\
          import pandas.util.testing as tm
          s = tm.makeTimeSeries()
          rs = s.resample("D")
          """)
          ip.run_code(code)
  
          with tm.assert_produces_warning(None):
              with provisionalcompleter('ignore'):
                  list(ip.Completer.completions('rs.', 1))
  
      def test_deferred_with_groupby(self):
  
          # GH 12486
          # support deferred resample ops with groupby
          data = [['2010-01-01', 'A', 2], ['2010-01-02', 'A', 3],
                  ['2010-01-05', 'A', 8], ['2010-01-10', 'A', 7],
                  ['2010-01-13', 'A', 3], ['2010-01-01', 'B', 5],
                  ['2010-01-03', 'B', 2], ['2010-01-04', 'B', 1],
                  ['2010-01-11', 'B', 7], ['2010-01-14', 'B', 3]]
  
          df = DataFrame(data, columns=['date', 'id', 'score'])
          df.date = pd.to_datetime(df.date)
          f = lambda x: x.set_index('date').resample('D').asfreq()
          expected = df.groupby('id').apply(f)
          result = df.set_index('date').groupby('id').resample('D').asfreq()
          assert_frame_equal(result, expected)
  
          df = DataFrame({'date': pd.date_range(start='2016-01-01',
                                                periods=4,
                                                freq='W'),
                          'group': [1, 1, 2, 2],
                          'val': [5, 6, 7, 8]}).set_index('date')
  
          f = lambda x: x.resample('1D').ffill()
          expected = df.groupby('group').apply(f)
          result = df.groupby('group').resample('1D').ffill()
          assert_frame_equal(result, expected)
  
      def test_getitem(self):
          g = self.frame.groupby('A')
  
          expected = g.B.apply(lambda x: x.resample('2s').mean())
  
          result = g.resample('2s').B.mean()
          assert_series_equal(result, expected)
  
          result = g.B.resample('2s').mean()
          assert_series_equal(result, expected)
  
          result = g.resample('2s').mean().B
          assert_series_equal(result, expected)
  
      def test_getitem_multiple(self):
  
          # GH 13174
          # multiple calls after selection causing an issue with aliasing
          data = [{'id': 1, 'buyer': 'A'}, {'id': 2, 'buyer': 'B'}]
          df = DataFrame(data, index=pd.date_range('2016-01-01', periods=2))
          r = df.groupby('id').resample('1D')
          result = r['buyer'].count()
          expected = Series([1, 1],
                            index=pd.MultiIndex.from_tuples(
                                [(1, Timestamp('2016-01-01')),
                                 (2, Timestamp('2016-01-02'))],
                                names=['id', None]),
                            name='buyer')
          assert_series_equal(result, expected)
  
          result = r['buyer'].count()
          assert_series_equal(result, expected)
  
      def test_nearest(self):
  
          # GH 17496
          # Resample nearest
          index = pd.date_range('1/1/2000', periods=3, freq='T')
          result = Series(range(3), index=index).resample('20s').nearest()
  
          expected = Series(
              [0, 0, 1, 1, 1, 2, 2],
              index=pd.DatetimeIndex(
                  ['2000-01-01 00:00:00', '2000-01-01 00:00:20',
                   '2000-01-01 00:00:40', '2000-01-01 00:01:00',
                   '2000-01-01 00:01:20', '2000-01-01 00:01:40',
                   '2000-01-01 00:02:00'],
                  dtype='datetime64[ns]',
                  freq='20S'))
          assert_series_equal(result, expected)
  
      def test_methods(self):
          g = self.frame.groupby('A')
          r = g.resample('2s')
  
          for f in ['first', 'last', 'median', 'sem', 'sum', 'mean',
                    'min', 'max']:
              result = getattr(r, f)()
              expected = g.apply(lambda x: getattr(x.resample('2s'), f)())
              assert_frame_equal(result, expected)
  
          for f in ['size']:
              result = getattr(r, f)()
              expected = g.apply(lambda x: getattr(x.resample('2s'), f)())
              assert_series_equal(result, expected)
  
          for f in ['count']:
              result = getattr(r, f)()
              expected = g.apply(lambda x: getattr(x.resample('2s'), f)())
              assert_frame_equal(result, expected)
  
          # series only
          for f in ['nunique']:
              result = getattr(r.B, f)()
              expected = g.B.apply(lambda x: getattr(x.resample('2s'), f)())
              assert_series_equal(result, expected)
  
          for f in ['nearest', 'backfill', 'ffill', 'asfreq']:
              result = getattr(r, f)()
              expected = g.apply(lambda x: getattr(x.resample('2s'), f)())
              assert_frame_equal(result, expected)
  
          result = r.ohlc()
          expected = g.apply(lambda x: x.resample('2s').ohlc())
          assert_frame_equal(result, expected)
  
          for f in ['std', 'var']:
              result = getattr(r, f)(ddof=1)
              expected = g.apply(lambda x: getattr(x.resample('2s'), f)(ddof=1))
              assert_frame_equal(result, expected)
  
      def test_apply(self):
  
          g = self.frame.groupby('A')
          r = g.resample('2s')
  
          # reduction
          expected = g.resample('2s').sum()
  
          def f(x):
              return x.resample('2s').sum()
  
          result = r.apply(f)
          assert_frame_equal(result, expected)
  
          def f(x):
              return x.resample('2s').apply(lambda y: y.sum())
  
          result = g.apply(f)
          assert_frame_equal(result, expected)
  
      def test_apply_with_mutated_index(self):
          # GH 15169
          index = pd.date_range('1-1-2015', '12-31-15', freq='D')
          df = DataFrame(data={'col1': np.random.rand(len(index))}, index=index)
  
          def f(x):
              s = Series([1, 2], index=['a', 'b'])
              return s
  
          expected = df.groupby(pd.Grouper(freq='M')).apply(f)
  
          result = df.resample('M').apply(f)
          assert_frame_equal(result, expected)
  
          # A case for series
          expected = df['col1'].groupby(pd.Grouper(freq='M')).apply(f)
          result = df['col1'].resample('M').apply(f)
          assert_series_equal(result, expected)
  
      def test_resample_groupby_with_label(self):
          # GH 13235
          index = date_range('2000-01-01', freq='2D', periods=5)
          df = DataFrame(index=index,
                         data={'col0': [0, 0, 1, 1, 2], 'col1': [1, 1, 1, 1, 1]}
                         )
          result = df.groupby('col0').resample('1W', label='left').sum()
  
          mi = [np.array([0, 0, 1, 2]),
                pd.to_datetime(np.array(['1999-12-26', '2000-01-02',
                                         '2000-01-02', '2000-01-02'])
                               )
                ]
          mindex = pd.MultiIndex.from_arrays(mi, names=['col0', None])
          expected = DataFrame(data={'col0': [0, 0, 2, 2], 'col1': [1, 1, 2, 1]},
                               index=mindex
                               )
  
          assert_frame_equal(result, expected)
  
      def test_consistency_with_window(self):
  
          # consistent return values with window
          df = self.frame
          expected = pd.Int64Index([1, 2, 3], name='A')
          result = df.groupby('A').resample('2s').mean()
          assert result.index.nlevels == 2
          tm.assert_index_equal(result.index.levels[0], expected)
  
          result = df.groupby('A').rolling(20).mean()
          assert result.index.nlevels == 2
          tm.assert_index_equal(result.index.levels[0], expected)
  
      def test_median_duplicate_columns(self):
          # GH 14233
  
          df = DataFrame(np.random.randn(20, 3),
                         columns=list('aaa'),
                         index=pd.date_range('2012-01-01', periods=20, freq='s'))
          df2 = df.copy()
          df2.columns = ['a', 'b', 'c']
          expected = df2.resample('5s').median()
          result = df.resample('5s').median()
          expected.columns = result.columns
          assert_frame_equal(result, expected)
  
  
  class TestTimeGrouper(object):
  
      def setup_method(self, method):
          self.ts = Series(np.random.randn(1000),
                           index=date_range('1/1/2000', periods=1000))
  
      def test_apply(self):
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              grouper = pd.TimeGrouper(freq='A', label='right', closed='right')
  
          grouped = self.ts.groupby(grouper)
  
          f = lambda x: x.sort_values()[-3:]
  
          applied = grouped.apply(f)
          expected = self.ts.groupby(lambda x: x.year).apply(f)
  
          applied.index = applied.index.droplevel(0)
          expected.index = expected.index.droplevel(0)
          assert_series_equal(applied, expected)
  
      def test_count(self):
          self.ts[::3] = np.nan
  
          expected = self.ts.groupby(lambda x: x.year).count()
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              grouper = pd.TimeGrouper(freq='A', label='right', closed='right')
          result = self.ts.groupby(grouper).count()
          expected.index = result.index
          assert_series_equal(result, expected)
  
          result = self.ts.resample('A').count()
          expected.index = result.index
          assert_series_equal(result, expected)
  
      def test_numpy_reduction(self):
          result = self.ts.resample('A', closed='right').prod()
  
          expected = self.ts.groupby(lambda x: x.year).agg(np.prod)
          expected.index = result.index
  
          assert_series_equal(result, expected)
  
      def test_apply_iteration(self):
          # #2300
          N = 1000
          ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
          df = DataFrame({'open': 1, 'close': 2}, index=ind)
          tg = TimeGrouper('M')
  
          _, grouper, _ = tg._get_grouper(df)
  
          # Errors
          grouped = df.groupby(grouper, group_keys=False)
          f = lambda df: df['close'] / df['open']
  
          # it works!
          result = grouped.apply(f)
          tm.assert_index_equal(result.index, df.index)
  
      def test_panel_aggregation(self):
          ind = pd.date_range('1/1/2000', periods=100)
          data = np.random.randn(2, len(ind), 4)
  
          with catch_warnings(record=True):
              wp = Panel(data, items=['Item1', 'Item2'], major_axis=ind,
                         minor_axis=['A', 'B', 'C', 'D'])
  
              tg = TimeGrouper('M', axis=1)
              _, grouper, _ = tg._get_grouper(wp)
              bingrouped = wp.groupby(grouper)
              binagg = bingrouped.mean()
  
              def f(x):
                  assert (isinstance(x, Panel))
                  return x.mean(1)
  
              result = bingrouped.agg(f)
              tm.assert_panel_equal(result, binagg)
  
      def test_fails_on_no_datetime_index(self):
          index_names = ('Int64Index', 'Index', 'Float64Index', 'MultiIndex')
          index_funcs = (tm.makeIntIndex,
                         tm.makeUnicodeIndex, tm.makeFloatIndex,
                         lambda m: tm.makeCustomIndex(m, 2))
          n = 2
          for name, func in zip(index_names, index_funcs):
              index = func(n)
              df = DataFrame({'a': np.random.randn(n)}, index=index)
              with tm.assert_raises_regex(TypeError,
                                          "Only valid with "
                                          "DatetimeIndex, TimedeltaIndex "
                                          "or PeriodIndex, but got an "
                                          "instance of %r" % name):
                  df.groupby(TimeGrouper('D'))
  
      def test_aaa_group_order(self):
          # GH 12840
          # check TimeGrouper perform stable sorts
          n = 20
          data = np.random.randn(n, 4)
          df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
          df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2),
                       datetime(2013, 1, 3), datetime(2013, 1, 4),
                       datetime(2013, 1, 5)] * 4
          grouped = df.groupby(TimeGrouper(key='key', freq='D'))
  
          tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)),
                                df[::5])
          tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)),
                                df[1::5])
          tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)),
                                df[2::5])
          tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)),
                                df[3::5])
          tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)),
                                df[4::5])
  
      def test_aggregate_normal(self):
          # check TimeGrouper's aggregation is identical as normal groupby
  
          n = 20
          data = np.random.randn(n, 4)
          normal_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
          normal_df['key'] = [1, 2, 3, 4, 5] * 4
  
          dt_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
          dt_df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2),
                          datetime(2013, 1, 3), datetime(2013, 1, 4),
                          datetime(2013, 1, 5)] * 4
  
          normal_grouped = normal_df.groupby('key')
          dt_grouped = dt_df.groupby(TimeGrouper(key='key', freq='D'))
  
          for func in ['min', 'max', 'prod', 'var', 'std', 'mean']:
              expected = getattr(normal_grouped, func)()
              dt_result = getattr(dt_grouped, func)()
              expected.index = date_range(start='2013-01-01', freq='D',
                                          periods=5, name='key')
              assert_frame_equal(expected, dt_result)
  
          for func in ['count', 'sum']:
              expected = getattr(normal_grouped, func)()
              expected.index = date_range(start='2013-01-01', freq='D',
                                          periods=5, name='key')
              dt_result = getattr(dt_grouped, func)()
              assert_frame_equal(expected, dt_result)
  
          # GH 7453
          for func in ['size']:
              expected = getattr(normal_grouped, func)()
              expected.index = date_range(start='2013-01-01', freq='D',
                                          periods=5, name='key')
              dt_result = getattr(dt_grouped, func)()
              assert_series_equal(expected, dt_result)
  
          # GH 7453
          for func in ['first', 'last']:
              expected = getattr(normal_grouped, func)()
              expected.index = date_range(start='2013-01-01', freq='D',
                                          periods=5, name='key')
              dt_result = getattr(dt_grouped, func)()
              assert_frame_equal(expected, dt_result)
  
          # if TimeGrouper is used included, 'nth' doesn't work yet
  
          """
          for func in ['nth']:
              expected = getattr(normal_grouped, func)(3)
              expected.index = date_range(start='2013-01-01',
                                          freq='D', periods=5, name='key')
              dt_result = getattr(dt_grouped, func)(3)
              assert_frame_equal(expected, dt_result)
          """
  
      def test_aggregate_with_nat(self):
          # check TimeGrouper's aggregation is identical as normal groupby
  
          n = 20
          data = np.random.randn(n, 4).astype('int64')
          normal_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
          normal_df['key'] = [1, 2, np.nan, 4, 5] * 4
  
          dt_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
          dt_df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2), pd.NaT,
                          datetime(2013, 1, 4), datetime(2013, 1, 5)] * 4
  
          normal_grouped = normal_df.groupby('key')
          dt_grouped = dt_df.groupby(TimeGrouper(key='key', freq='D'))
  
          for func in ['min', 'max', 'sum', 'prod']:
              normal_result = getattr(normal_grouped, func)()
              dt_result = getattr(dt_grouped, func)()
              pad = DataFrame([[np.nan, np.nan, np.nan, np.nan]], index=[3],
                              columns=['A', 'B', 'C', 'D'])
              expected = normal_result.append(pad)
              expected = expected.sort_index()
              expected.index = date_range(start='2013-01-01', freq='D',
                                          periods=5, name='key')
              assert_frame_equal(expected, dt_result)
  
          for func in ['count']:
              normal_result = getattr(normal_grouped, func)()
              pad = DataFrame([[0, 0, 0, 0]], index=[3],
                              columns=['A', 'B', 'C', 'D'])
              expected = normal_result.append(pad)
              expected = expected.sort_index()
              expected.index = date_range(start='2013-01-01', freq='D',
                                          periods=5, name='key')
              dt_result = getattr(dt_grouped, func)()
              assert_frame_equal(expected, dt_result)
  
          for func in ['size']:
              normal_result = getattr(normal_grouped, func)()
              pad = Series([0], index=[3])
              expected = normal_result.append(pad)
              expected = expected.sort_index()
              expected.index = date_range(start='2013-01-01', freq='D',
                                          periods=5, name='key')
              dt_result = getattr(dt_grouped, func)()
              assert_series_equal(expected, dt_result)
              # GH 9925
              assert dt_result.index.name == 'key'
  
              # if NaT is included, 'var', 'std', 'mean', 'first','last'
              # and 'nth' doesn't work yet
  
      def test_repr(self):
          # GH18203
          result = repr(TimeGrouper(key='A', freq='H'))
          expected = ("TimeGrouper(key='A', freq=<Hour>, axis=0, sort=True, "
                      "closed='left', label='left', how='mean', "
                      "convention='e', base=0)")
          assert result == expected
  