Python pandas.util.testing.assert_frame_equal() Examples

def test_rank_hits(self):
        ranks = pd.DataFrame(
            [
                [1., 4., 1., 5., 7.],
                [2., 6., 9., 2., 8.],
                [2., 2., 6., 8., 4.]
            ],
            index=['OTU_1', 'OTU_2', 'OTU_3'],
            columns=['MS_1', 'MS_2', 'MS_3', 'MS_4', 'MS_5']
        )
        res = rank_hits(ranks, k=2)
        exp = pd.DataFrame(
            [
                ['OTU_1', 5., 'MS_4'],
                ['OTU_2', 8., 'MS_5'],
                ['OTU_3', 6., 'MS_3'],
                ['OTU_1', 7., 'MS_5'],
                ['OTU_2', 9., 'MS_3'],
                ['OTU_3', 8., 'MS_4']
            ], columns=['src', 'rank', 'dest'],
        )

        pdt.assert_frame_equal(res, exp) 

def test_rolling_corr_cov(self):
        g = self.frame.groupby('A')
        r = g.rolling(window=4)

        for f in ['corr', 'cov']:
            result = getattr(r, f)(self.frame)

            def func(x):
                return getattr(x.rolling(4), f)(self.frame)
            expected = g.apply(func)
            tm.assert_frame_equal(result, expected)

            result = getattr(r.B, f)(pairwise=True)

            def func(x):
                return getattr(x.B.rolling(4), f)(pairwise=True)
            expected = g.apply(func)
            tm.assert_series_equal(result, expected) 

def test_dataframe_append_should_add_new_column(library):
    data = np.zeros((2,), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')])
    data[:] = [(1, 2., 'Hello'), (2, 3., "World")]
    df = DataFrame(data, index=DatetimeIndex(np.array([dt(2013, 1, 1),
                                                       dt(2013, 1, 2)]).astype('datetime64[ns]'), name='DATETIME'))
    data2 = np.zeros((1,), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10'), ('D', 'f4')])
    data2[:] = [(4, 5., 'Hi', 6.)]
    df2 = DataFrame(data2, index=DatetimeIndex(np.array([dt(2013, 1, 3)]).astype('datetime64[ns]'), name='DATETIME'))
    expected_data = np.zeros((3,), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10'), ('D', 'f4')])
    expected_data[:] = [(1, 2., 'Hello', np.nan), (2, 3., "World", np.nan), (4, 5., 'Hi', 6.)]
    expected = DataFrame(expected_data, index=DatetimeIndex(np.array([dt(2013, 1, 1),
                                                                       dt(2013, 1, 2),
                                                                       dt(2013, 1, 3)]).astype('datetime64[ns]'), name='DATETIME'))

    library.write('pandas', df)
    library.append('pandas', df2)
    actual = library.read('pandas').data

    assert_frame_equal(expected, actual) 

def test_custom_asserts(self):
        # This would always trigger the KeyError from trying to put
        # an array of equal-length UserDicts inside an ndarray.
        data = JSONArray([collections.UserDict({'a': 1}),
                          collections.UserDict({'b': 2}),
                          collections.UserDict({'c': 3})])
        a = pd.Series(data)
        self.assert_series_equal(a, a)
        self.assert_frame_equal(a.to_frame(), a.to_frame())

        b = pd.Series(data.take([0, 0, 1]))
        with pytest.raises(AssertionError):
            self.assert_series_equal(a, b)

        with pytest.raises(AssertionError):
            self.assert_frame_equal(a.to_frame(), b.to_frame()) 

def test_dataframe_append_should_promote_string_column(library):
    data = np.zeros((2,), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')])
    data[:] = [(1, 2., 'Hello'), (2, 3., "World")]
    df = DataFrame(data, index=DatetimeIndex(np.array([dt(2013, 1, 1),
                                                       dt(2013, 1, 2)]).astype('datetime64[ns]'), name='DATETIME'))
    data2 = np.zeros((1,), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a30')])
    data2[:] = [(3, 4., 'Hello World - Good Morning')]
    df2 = DataFrame(data2, index=DatetimeIndex(np.array([dt(2013, 1, 3)]).astype('datetime64[ns]'), name='DATETIME'))
    expected_data = np.zeros((3,), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a30')])
    expected_data[:] = [(1, 2., 'Hello'), (2, 3., "World"), (3, 4., 'Hello World - Good Morning')]
    expected = DataFrame(expected_data, index=DatetimeIndex(np.array([dt(2013, 1, 1),
                                                                       dt(2013, 1, 2),
                                                                       dt(2013, 1, 3)]).astype('datetime64[ns]'), name='DATETIME'))

    library.write('pandas', df)
    library.append('pandas', df2)
    actual = library.read('pandas').data

    assert_frame_equal(expected, actual) 

def assert_frame_equal(self, left, right, *args, **kwargs):
        # TODO(EA): select_dtypes
        tm.assert_index_equal(
            left.columns, right.columns,
            exact=kwargs.get('check_column_type', 'equiv'),
            check_names=kwargs.get('check_names', True),
            check_exact=kwargs.get('check_exact', False),
            check_categorical=kwargs.get('check_categorical', True),
            obj='{obj}.columns'.format(obj=kwargs.get('obj', 'DataFrame')))

        decimals = (left.dtypes == 'decimal').index

        for col in decimals:
            self.assert_series_equal(left[col], right[col],
                                     *args, **kwargs)

        left = left.drop(columns=decimals)
        right = right.drop(columns=decimals)
        tm.assert_frame_equal(left, right, *args, **kwargs) 

def test_dataframe_append_should_add_new_columns_and_reorder(library):
    data = np.zeros((2,), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')])
    data[:] = [(1, 2., 'Hello'), (2, 3., "World")]
    df = DataFrame(data, index=DatetimeIndex(np.array([dt(2013, 1, 1),
                                                       dt(2013, 1, 2)]).astype('datetime64[ns]'), name='DATETIME'))
    data2 = np.zeros((1,), dtype=[('C', 'a10'), ('A', 'i4'), ('E', 'a1'), ('B', 'f4'), ('D', 'f4'), ('F', 'i4')])
    data2[:] = [('Hi', 4, 'Y', 5., 6., 7)]
    df2 = DataFrame(data2, index=DatetimeIndex(np.array([dt(2013, 1, 3)]).astype('datetime64[ns]'), name='DATETIME'))
    expected_data = np.zeros((3,), dtype=[('C', 'a10'), ('A', 'i4'), ('E', 'a1'),
                                          ('B', 'f4'), ('D', 'f4'), ('F', 'i4')])
    expected_data[:] = [('Hello', 1, '', 2., np.nan, 0), ("World", 2, '', 3., np.nan, 0), ('Hi', 4, 'Y', 5., 6., 7)]
    expected = DataFrame(expected_data, index=DatetimeIndex(np.array([dt(2013, 1, 1),
                                                                       dt(2013, 1, 2),
                                                                       dt(2013, 1, 3)]).astype('datetime64[ns]'), name='DATETIME'))

    library.write('pandas', df)
    library.append('pandas', df2)
    actual = library.read('pandas').data

    assert_frame_equal(expected, actual)


# -- auto generated tests --- # 

def test_window_with_args(self):
        # make sure that we are aggregating window functions correctly with arg
        r = Series(np.random.randn(100)).rolling(window=10, min_periods=1,
                                                 win_type='gaussian')
        expected = concat([r.mean(std=10), r.mean(std=.01)], axis=1)
        expected.columns = ['<lambda>', '<lambda>']
        result = r.aggregate([lambda x: x.mean(std=10),
                              lambda x: x.mean(std=.01)])
        tm.assert_frame_equal(result, expected)

        def a(x):
            return x.mean(std=10)

        def b(x):
            return x.mean(std=0.01)

        expected = concat([r.mean(std=10), r.mean(std=.01)], axis=1)
        expected.columns = ['a', 'b']
        result = r.aggregate([a, b])
        tm.assert_frame_equal(result, expected) 

def test_rolling_axis(self, axis_frame):
        # see gh-23372.
        df = DataFrame(np.ones((10, 20)))
        axis = df._get_axis_number(axis_frame)

        if axis == 0:
            expected = DataFrame({
                i: [np.nan] * 2 + [3.0] * 8
                for i in range(20)
            })
        else:
            # axis == 1
            expected = DataFrame([
                [np.nan] * 2 + [3.0] * 18
            ] * 10)

        result = df.rolling(3, axis=axis_frame).sum()
        tm.assert_frame_equal(result, expected) 

def test_rolling_apply_with_pandas_objects(self, window):
        # 5071
        df = pd.DataFrame({'A': np.random.randn(5),
                           'B': np.random.randint(0, 10, size=5)},
                          index=pd.date_range('20130101', periods=5, freq='s'))

        # we have an equal spaced timeseries index
        # so simulate removing the first period
        def f(x):
            if x.index[0] == df.index[0]:
                return np.nan
            return x.iloc[-1]

        result = df.rolling(window).apply(f, raw=False)
        expected = df.iloc[2:].reindex_like(df)
        tm.assert_frame_equal(result, expected)

        with pytest.raises(AttributeError):
            df.rolling(window).apply(f, raw=True) 

def test_flex_binary_frame(self, method):
        series = self.frame[1]

        res = getattr(series.rolling(window=10), method)(self.frame)
        res2 = getattr(self.frame.rolling(window=10), method)(series)
        exp = self.frame.apply(lambda x: getattr(
            series.rolling(window=10), method)(x))

        tm.assert_frame_equal(res, exp)
        tm.assert_frame_equal(res2, exp)

        frame2 = self.frame.copy()
        frame2.values[:] = np.random.randn(*frame2.shape)

        res3 = getattr(self.frame.rolling(window=10), method)(frame2)
        exp = DataFrame({k: getattr(self.frame[k].rolling(
            window=10), method)(frame2[k]) for k in self.frame})
        tm.assert_frame_equal(res3, exp) 

def test_expanding_apply_args_kwargs(self, raw):

        def mean_w_arg(x, const):
            return np.mean(x) + const

        df = DataFrame(np.random.rand(20, 3))

        expected = df.expanding().apply(np.mean, raw=raw) + 20.

        result = df.expanding().apply(mean_w_arg,
                                      raw=raw,
                                      args=(20, ))
        tm.assert_frame_equal(result, expected)

        result = df.expanding().apply(mean_w_arg,
                                      raw=raw,
                                      kwargs={'const': 20})
        tm.assert_frame_equal(result, expected) 

def test_rolling_functions_window_non_shrinkage_binary(self):

        # corr/cov return a MI DataFrame
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]],
                       columns=Index(['A', 'B'], name='foo'),
                       index=Index(range(4), name='bar'))
        df_expected = DataFrame(
            columns=Index(['A', 'B'], name='foo'),
            index=pd.MultiIndex.from_product([df.index, df.columns],
                                             names=['bar', 'foo']),
            dtype='float64')
        functions = [lambda x: (x.rolling(window=10, min_periods=5)
                                .cov(x, pairwise=True)),
                     lambda x: (x.rolling(window=10, min_periods=5)
                                .corr(x, pairwise=True))]
        for f in functions:
            df_result = f(df)
            tm.assert_frame_equal(df_result, df_expected) 

def test_expanding_cov_pairwise_diff_length(self):
        # GH 7512
        df1 = DataFrame([[1, 5], [3, 2], [3, 9]],
                        columns=Index(['A', 'B'], name='foo'))
        df1a = DataFrame([[1, 5], [3, 9]],
                         index=[0, 2],
                         columns=Index(['A', 'B'], name='foo'))
        df2 = DataFrame([[5, 6], [None, None], [2, 1]],
                        columns=Index(['X', 'Y'], name='foo'))
        df2a = DataFrame([[5, 6], [2, 1]],
                         index=[0, 2],
                         columns=Index(['X', 'Y'], name='foo'))
        # TODO: xref gh-15826
        # .loc is not preserving the names
        result1 = df1.expanding().cov(df2a, pairwise=True).loc[2]
        result2 = df1.expanding().cov(df2a, pairwise=True).loc[2]
        result3 = df1a.expanding().cov(df2, pairwise=True).loc[2]
        result4 = df1a.expanding().cov(df2a, pairwise=True).loc[2]
        expected = DataFrame([[-3.0, -6.0], [-5.0, -10.0]],
                             columns=Index(['A', 'B'], name='foo'),
                             index=Index(['X', 'Y'], name='foo'))
        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)
        tm.assert_frame_equal(result3, expected)
        tm.assert_frame_equal(result4, expected) 

def test_expanding_corr_pairwise_diff_length(self):
        # GH 7512
        df1 = DataFrame([[1, 2], [3, 2], [3, 4]],
                        columns=['A', 'B'],
                        index=Index(range(3), name='bar'))
        df1a = DataFrame([[1, 2], [3, 4]],
                         index=Index([0, 2], name='bar'),
                         columns=['A', 'B'])
        df2 = DataFrame([[5, 6], [None, None], [2, 1]],
                        columns=['X', 'Y'],
                        index=Index(range(3), name='bar'))
        df2a = DataFrame([[5, 6], [2, 1]],
                         index=Index([0, 2], name='bar'),
                         columns=['X', 'Y'])
        result1 = df1.expanding().corr(df2, pairwise=True).loc[2]
        result2 = df1.expanding().corr(df2a, pairwise=True).loc[2]
        result3 = df1a.expanding().corr(df2, pairwise=True).loc[2]
        result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2]
        expected = DataFrame([[-1.0, -1.0], [-1.0, -1.0]],
                             columns=['A', 'B'],
                             index=Index(['X', 'Y']))
        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)
        tm.assert_frame_equal(result3, expected)
        tm.assert_frame_equal(result4, expected) 

def test_rolling_functions_window_non_shrinkage(self, f):
        # GH 7764
        s = Series(range(4))
        s_expected = Series(np.nan, index=s.index)
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
        df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)

        try:
            s_result = f(s)
            tm.assert_series_equal(s_result, s_expected)

            df_result = f(df)
            tm.assert_frame_equal(df_result, df_expected)
        except (ImportError):

            # scipy needed for rolling_window
            pytest.skip("scipy not available") 

def test_rolling_apply_mutability(self):
        # GH 14013
        df = pd.DataFrame({'A': ['foo'] * 3 + ['bar'] * 3, 'B': [1] * 6})
        g = df.groupby('A')

        mi = pd.MultiIndex.from_tuples([('bar', 3), ('bar', 4), ('bar', 5),
                                        ('foo', 0), ('foo', 1), ('foo', 2)])

        mi.names = ['A', None]
        # Grouped column should not be a part of the output
        expected = pd.DataFrame([np.nan, 2., 2.] * 2, columns=['B'], index=mi)

        result = g.rolling(window=2).sum()
        tm.assert_frame_equal(result, expected)

        # Call an arbitrary function on the groupby
        g.sum()

        # Make sure nothing has been mutated
        result = g.rolling(window=2).sum()
        tm.assert_frame_equal(result, expected) 

def test_expanding(self):
        g = self.frame.groupby('A')
        r = g.expanding()

        for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']:

            result = getattr(r, f)()
            expected = g.apply(lambda x: getattr(x.expanding(), f)())
            tm.assert_frame_equal(result, expected)

        for f in ['std', 'var']:
            result = getattr(r, f)(ddof=0)
            expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
            tm.assert_frame_equal(result, expected)

        result = r.quantile(0.5)
        expected = g.apply(lambda x: x.expanding().quantile(0.5))
        tm.assert_frame_equal(result, expected) 

def test_expanding_corr_cov(self):
        g = self.frame.groupby('A')
        r = g.expanding()

        for f in ['corr', 'cov']:
            result = getattr(r, f)(self.frame)

            def func(x):
                return getattr(x.expanding(), f)(self.frame)
            expected = g.apply(func)
            tm.assert_frame_equal(result, expected)

            result = getattr(r.B, f)(pairwise=True)

            def func(x):
                return getattr(x.B.expanding(), f)(pairwise=True)
            expected = g.apply(func)
            tm.assert_series_equal(result, expected) 

def test_basic_regular(self):

        df = self.regular.copy()

        df.index = pd.date_range('20130101', periods=5, freq='D')
        expected = df.rolling(window=1, min_periods=1).sum()
        result = df.rolling(window='1D').sum()
        tm.assert_frame_equal(result, expected)

        df.index = pd.date_range('20130101', periods=5, freq='2D')
        expected = df.rolling(window=1, min_periods=1).sum()
        result = df.rolling(window='2D', min_periods=1).sum()
        tm.assert_frame_equal(result, expected)

        expected = df.rolling(window=1, min_periods=1).sum()
        result = df.rolling(window='2D', min_periods=1).sum()
        tm.assert_frame_equal(result, expected)

        expected = df.rolling(window=1).sum()
        result = df.rolling(window='2D').sum()
        tm.assert_frame_equal(result, expected) 

def test_ragged_std(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).std(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='1s', min_periods=1).std(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='3s', min_periods=1).std(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] + [0.5] * 4
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).std(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan, 0.707107, 1.0, 1.0, 1.290994]
        tm.assert_frame_equal(result, expected) 

def test_ragged_var(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).var(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='1s', min_periods=1).var(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='3s', min_periods=1).var(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] + [0.25] * 4
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).var(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.]
        tm.assert_frame_equal(result, expected) 

def test_ragged_count(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).count()
        expected = df.copy()
        expected['B'] = [1.0, 1, 1, 1, 1]
        tm.assert_frame_equal(result, expected)

        df = self.ragged
        result = df.rolling(window='1s').count()
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).count()
        expected = df.copy()
        expected['B'] = [1.0, 1, 2, 1, 2]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=2).count()
        expected = df.copy()
        expected['B'] = [np.nan, np.nan, 2, np.nan, 2]
        tm.assert_frame_equal(result, expected) 

def test_ragged_min(self):

        df = self.ragged

        result = df.rolling(window='1s', min_periods=1).min()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).min()
        expected = df.copy()
        expected['B'] = [0.0, 1, 1, 3, 3]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).min()
        expected = df.copy()
        expected['B'] = [0.0, 0, 0, 1, 1]
        tm.assert_frame_equal(result, expected) 

def test_ragged_max(self):

        df = self.ragged

        result = df.rolling(window='1s', min_periods=1).max()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).max()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).max()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected) 

def test_all(self):

        # simple comparison of integer vs time-based windowing
        df = self.regular * 2
        er = df.rolling(window=1)
        r = df.rolling(window='1s')

        for f in ['sum', 'mean', 'count', 'median', 'std',
                  'var', 'kurt', 'skew', 'min', 'max']:

            result = getattr(r, f)()
            expected = getattr(er, f)()
            tm.assert_frame_equal(result, expected)

        result = r.quantile(0.5)
        expected = er.quantile(0.5)
        tm.assert_frame_equal(result, expected) 

def test_truncate(self):
        dates = self.panel.major_axis
        start, end = dates[1], dates[5]

        trunced = self.panel.truncate(start, end, axis='major')
        expected = self.panel['ItemA'].truncate(start, end)

        assert_frame_equal(trunced['ItemA'], expected)

        trunced = self.panel.truncate(before=start, axis='major')
        expected = self.panel['ItemA'].truncate(before=start)

        assert_frame_equal(trunced['ItemA'], expected)

        trunced = self.panel.truncate(after=end, axis='major')
        expected = self.panel['ItemA'].truncate(after=end)

        assert_frame_equal(trunced['ItemA'], expected) 

def test_abs(self):

        result = self.panel.abs()
        result2 = abs(self.panel)
        expected = np.abs(self.panel)
        assert_panel_equal(result, expected)
        assert_panel_equal(result2, expected)

        df = self.panel['ItemA']
        result = df.abs()
        result2 = abs(df)
        expected = np.abs(df)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        s = df['A']
        result = s.abs()
        result2 = abs(s)
        expected = np.abs(s)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)
        assert result.name == 'A'
        assert result2.name == 'A' 

def test_getitem_fancy_xs(self):
        p = self.panel
        item = 'ItemB'

        date = p.major_axis[5]
        col = 'C'

        # get DataFrame
        # item
        assert_frame_equal(p.loc[item], p[item])
        assert_frame_equal(p.loc[item, :], p[item])
        assert_frame_equal(p.loc[item, :, :], p[item])

        # major axis, axis=1
        assert_frame_equal(p.loc[:, date], p.major_xs(date))
        assert_frame_equal(p.loc[:, date, :], p.major_xs(date))

        # minor axis, axis=2
        assert_frame_equal(p.loc[:, :, 'C'], p.minor_xs('C'))

        # get Series
        assert_series_equal(p.loc[item, date], p[item].loc[date])
        assert_series_equal(p.loc[item, date, :], p[item].loc[date])
        assert_series_equal(p.loc[item, :, col], p[item][col])
        assert_series_equal(p.loc[:, date, col], p.major_xs(date).loc[col]) 

def test_getitem_fancy_xs_check_view(self):
        item = 'ItemB'
        date = self.panel.major_axis[5]

        # make sure it's always a view
        NS = slice(None, None)

        # DataFrames
        comp = assert_frame_equal
        self._check_view(item, comp)
        self._check_view((item, NS), comp)
        self._check_view((item, NS, NS), comp)
        self._check_view((NS, date), comp)
        self._check_view((NS, date, NS), comp)
        self._check_view((NS, NS, 'C'), comp)

        # Series
        comp = assert_series_equal
        self._check_view((item, date), comp)
        self._check_view((item, date, NS), comp)
        self._check_view((item, NS, 'C'), comp)
        self._check_view((NS, date, 'C'), comp)