def test_multiindex_objects():
    mi = MultiIndex(levels=[["b", "d", "a"], [1, 2, 3]],
                    codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
                    names=["col1", "col2"])
    recons = mi._sort_levels_monotonic()

    # These are equal.
    assert mi.equals(recons)
    assert Index(mi.values).equals(Index(recons.values))

    # _hashed_values and hash_pandas_object(..., index=False) equivalency.
    expected = hash_pandas_object(mi, index=False).values
    result = mi._hashed_values

    tm.assert_numpy_array_equal(result, expected)

    expected = hash_pandas_object(recons, index=False).values
    result = recons._hashed_values

    tm.assert_numpy_array_equal(result, expected)

    expected = mi._hashed_values
    result = recons._hashed_values

    # Values should match, but in different order.
    tm.assert_numpy_array_equal(np.sort(result), np.sort(expected))

    def test_multiindex_objects(self):
        mi = MultiIndex(levels=[['b', 'd', 'a'], [1, 2, 3]],
                        labels=[[0, 1, 0, 2], [2, 0, 0, 1]],
                        names=['col1', 'col2'])
        recons = mi._sort_levels_monotonic()

        # these are equal
        assert mi.equals(recons)
        assert Index(mi.values).equals(Index(recons.values))

        # _hashed_values and hash_pandas_object(..., index=False)
        # equivalency
        expected = hash_pandas_object(
            mi, index=False).values
        result = mi._hashed_values
        tm.assert_numpy_array_equal(result, expected)

        expected = hash_pandas_object(
            recons, index=False).values
        result = recons._hashed_values
        tm.assert_numpy_array_equal(result, expected)

        expected = mi._hashed_values
        result = recons._hashed_values

        # values should match, but in different order
        tm.assert_numpy_array_equal(np.sort(result),
                                    np.sort(expected))

    def test_na_value_dict(self):
        data = """A,B,C
foo,bar,NA
bar,foo,foo
foo,bar,NA
bar,foo,foo"""

        df = read_csv(StringIO(data),
                      na_values={'A': ['foo'], 'B': ['bar']})
        expected = DataFrame({'A': [np.nan, 'bar', np.nan, 'bar'],
                              'B': [np.nan, 'foo', np.nan, 'foo'],
                              'C': [np.nan, 'foo', np.nan, 'foo']})
        assert_frame_equal(df, expected)

        data = """\
a,b,c,d
0,NA,1,5
"""
        xp = DataFrame({'b': [np.nan], 'c': [1], 'd': [5]}, index=[0])
        xp.index.name = 'a'
        df = read_csv(StringIO(data), na_values={}, index_col=0)
        assert_frame_equal(df, xp)

        xp = DataFrame({'b': [np.nan], 'd': [5]},
                       MultiIndex.from_tuples([(0, 1)]))
        df = read_csv(StringIO(data), na_values={}, index_col=[0, 2])
        assert_frame_equal(df, xp)

        xp = DataFrame({'b': [np.nan], 'd': [5]},
                       MultiIndex.from_tuples([(0, 1)]))
        df = read_csv(StringIO(data), na_values={}, index_col=['a', 'c'])
        assert_frame_equal(df, xp)

    def test_loc_getitem_series(self):
        # GH14730
        # passing a series as a key with a MultiIndex
        index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
        x = Series(index=index, data=range(9), dtype=np.float64)
        y = Series([1, 3])
        expected = Series(
            data=[0, 1, 2, 6, 7, 8],
            index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]),
            dtype=np.float64)
        result = x.loc[y]
        tm.assert_series_equal(result, expected)

        result = x.loc[[1, 3]]
        tm.assert_series_equal(result, expected)

        # GH15424
        y1 = Series([1, 3], index=[1, 2])
        result = x.loc[y1]
        tm.assert_series_equal(result, expected)

        empty = Series(data=[], dtype=np.float64)
        expected = Series([], index=MultiIndex(
            levels=index.levels, labels=[[], []], dtype=np.float64))
        result = x.loc[empty]
        tm.assert_series_equal(result, expected)

    def test_loc_multiindex_incomplete(self):

        # GH 7399
        # incomplete indexers
        s = Series(np.arange(15, dtype='int64'),
                   MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
        expected = s.loc[:, 'a':'c']

        result = s.loc[0:4, 'a':'c']
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result, expected)

        result = s.loc[:4, 'a':'c']
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result, expected)

        result = s.loc[0:, 'a':'c']
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result, expected)

        # GH 7400
        # multiindexer gettitem with list of indexers skips wrong element
        s = Series(np.arange(15, dtype='int64'),
                   MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
        expected = s.iloc[[6, 7, 8, 12, 13, 14]]
        result = s.loc[2:4:2, 'a':'c']
        tm.assert_series_equal(result, expected)

    def test_loc_getitem_nested_indexer(self, indexer_type_1, indexer_type_2):
        # GH #19686
        # .loc should work with nested indexers which can be
        # any list-like objects (see `pandas.api.types.is_list_like`) or slices

        def convert_nested_indexer(indexer_type, keys):
            if indexer_type == np.ndarray:
                return np.array(keys)
            if indexer_type == slice:
                return slice(*keys)
            return indexer_type(keys)

        a = [10, 20, 30]
        b = [1, 2, 3]
        index = MultiIndex.from_product([a, b])
        df = DataFrame(
            np.arange(len(index), dtype='int64'),
            index=index, columns=['Data'])

        keys = ([10, 20], [2, 3])
        types = (indexer_type_1, indexer_type_2)

        # check indexers with all the combinations of nested objects
        # of all the valid types
        indexer = tuple(
            convert_nested_indexer(indexer_type, k)
            for indexer_type, k in zip(types, keys))

        result = df.loc[indexer, 'Data']
        expected = Series(
            [1, 2, 4, 5], name='Data',
            index=MultiIndex.from_product(keys))

        tm.assert_series_equal(result, expected)

    def test_reset_index(self):
        df = tm.makeDataFrame()[:5]
        ser = df.stack()
        ser.index.names = ['hash', 'category']

        ser.name = 'value'
        df = ser.reset_index()
        assert 'value' in df

        df = ser.reset_index(name='value2')
        assert 'value2' in df

        # check inplace
        s = ser.reset_index(drop=True)
        s2 = ser
        s2.reset_index(drop=True, inplace=True)
        tm.assert_series_equal(s, s2)

        # level
        index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
                           codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
                                  [0, 1, 0, 1, 0, 1]])
        s = Series(np.random.randn(6), index=index)
        rs = s.reset_index(level=1)
        assert len(rs.columns) == 2

        rs = s.reset_index(level=[0, 2], drop=True)
        tm.assert_index_equal(rs.index, Index(index.get_level_values(1)))
        assert isinstance(rs, Series)

def test_groupby_as_index_apply(df):
    # GH #4648 and #3417
    df = DataFrame({'item_id': ['b', 'b', 'a', 'c', 'a', 'b'],
                    'user_id': [1, 2, 1, 1, 3, 1],
                    'time': range(6)})

    g_as = df.groupby('user_id', as_index=True)
    g_not_as = df.groupby('user_id', as_index=False)

    res_as = g_as.head(2).index
    res_not_as = g_not_as.head(2).index
    exp = Index([0, 1, 2, 4])
    tm.assert_index_equal(res_as, exp)
    tm.assert_index_equal(res_not_as, exp)

    res_as_apply = g_as.apply(lambda x: x.head(2)).index
    res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index

    # apply doesn't maintain the original ordering
    # changed in GH5610 as the as_index=False returns a MI here
    exp_not_as_apply = MultiIndex.from_tuples([(0, 0), (0, 2), (1, 1), (
        2, 4)])
    tp = [(1, 0), (1, 2), (2, 1), (3, 4)]
    exp_as_apply = MultiIndex.from_tuples(tp, names=['user_id', None])

    tm.assert_index_equal(res_as_apply, exp_as_apply)
    tm.assert_index_equal(res_not_as_apply, exp_not_as_apply)

    ind = Index(list('abcde'))
    df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind)
    res = df.groupby(0, as_index=False).apply(lambda x: x).index
    tm.assert_index_equal(res, ind)

    def test_sort_index_multiindex(self, level):
        # GH13496

        # sort rows by specified level of multi-index
        mi = MultiIndex.from_tuples([
            [2, 1, 3], [2, 1, 2], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4], [5, 6]], index=mi)

        expected_mi = MultiIndex.from_tuples([
            [1, 1, 1],
            [2, 1, 2],
            [2, 1, 3]], names=list('ABC'))
        expected = pd.DataFrame([
            [5, 6],
            [3, 4],
            [1, 2]], index=expected_mi)
        result = df.sort_index(level=level)
        assert_frame_equal(result, expected)

        # sort_remaining=False
        expected_mi = MultiIndex.from_tuples([
            [1, 1, 1],
            [2, 1, 3],
            [2, 1, 2]], names=list('ABC'))
        expected = pd.DataFrame([
            [5, 6],
            [1, 2],
            [3, 4]], index=expected_mi)
        result = df.sort_index(level=level, sort_remaining=False)
        assert_frame_equal(result, expected)

    def test_set_index_names(self):
        df = pd.util.testing.makeDataFrame()
        df.index.name = 'name'

        self.assertEqual(df.set_index(df.index).index.names, ['name'])

        mi = MultiIndex.from_arrays(df[['A', 'B']].T.values, names=['A', 'B'])
        mi2 = MultiIndex.from_arrays(df[['A', 'B', 'A', 'B']].T.values,
                                     names=['A', 'B', 'A', 'B'])

        df = df.set_index(['A', 'B'])

        self.assertEqual(df.set_index(df.index).index.names, ['A', 'B'])

        # Check that set_index isn't converting a MultiIndex into an Index
        self.assertTrue(isinstance(df.set_index(df.index).index, MultiIndex))

        # Check actual equality
        tm.assert_index_equal(df.set_index(df.index).index, mi)

        # Check that [MultiIndex, MultiIndex] yields a MultiIndex rather
        # than a pair of tuples
        self.assertTrue(isinstance(df.set_index(
            [df.index, df.index]).index, MultiIndex))

        # Check equality
        tm.assert_index_equal(df.set_index([df.index, df.index]).index, mi2)

    def test_hash_pandas_object(self):

        for obj in [Series([1, 2, 3]),
                    Series([1.0, 1.5, 3.2]),
                    Series([1.0, 1.5, np.nan]),
                    Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
                    Series(['a', 'b', 'c']),
                    Series(['a', np.nan, 'c']),
                    Series(['a', None, 'c']),
                    Series([True, False, True]),
                    Series(),
                    Index([1, 2, 3]),
                    Index([True, False, True]),
                    DataFrame({'x': ['a', 'b', 'c'], 'y': [1, 2, 3]}),
                    DataFrame(),
                    tm.makeMissingDataframe(),
                    tm.makeMixedDataFrame(),
                    tm.makeTimeDataFrame(),
                    tm.makeTimeSeries(),
                    tm.makeTimedeltaIndex(),
                    tm.makePeriodIndex(),
                    Series(tm.makePeriodIndex()),
                    Series(pd.date_range('20130101',
                                         periods=3, tz='US/Eastern')),
                    MultiIndex.from_product(
                        [range(5),
                         ['foo', 'bar', 'baz'],
                         pd.date_range('20130101', periods=2)]),
                    MultiIndex.from_product(
                        [pd.CategoricalIndex(list('aabc')),
                         range(3)])]:
            self.check_equal(obj)
            self.check_not_equal_with_index(obj)

    def setup_method(self, method):

        self.series_ints = Series(np.random.rand(4), index=lrange(0, 8, 2))
        self.frame_ints = DataFrame(np.random.randn(4, 4),
                                    index=lrange(0, 8, 2),
                                    columns=lrange(0, 12, 3))

        self.series_uints = Series(np.random.rand(4),
                                   index=UInt64Index(lrange(0, 8, 2)))
        self.frame_uints = DataFrame(np.random.randn(4, 4),
                                     index=UInt64Index(lrange(0, 8, 2)),
                                     columns=UInt64Index(lrange(0, 12, 3)))

        self.series_floats = Series(np.random.rand(4),
                                    index=Float64Index(range(0, 8, 2)))
        self.frame_floats = DataFrame(np.random.randn(4, 4),
                                      index=Float64Index(range(0, 8, 2)),
                                      columns=Float64Index(range(0, 12, 3)))

        m_idces = [MultiIndex.from_product([[1, 2], [3, 4]]),
                   MultiIndex.from_product([[5, 6], [7, 8]]),
                   MultiIndex.from_product([[9, 10], [11, 12]])]

        self.series_multi = Series(np.random.rand(4),
                                   index=m_idces[0])
        self.frame_multi = DataFrame(np.random.randn(4, 4),
                                     index=m_idces[0],
                                     columns=m_idces[1])

        self.series_labels = Series(np.random.randn(4), index=list('abcd'))
        self.frame_labels = DataFrame(np.random.randn(4, 4),
                                      index=list('abcd'), columns=list('ABCD'))

        self.series_mixed = Series(np.random.randn(4), index=[2, 4, 'null', 8])
        self.frame_mixed = DataFrame(np.random.randn(4, 4),
                                     index=[2, 4, 'null', 8])

        self.series_ts = Series(np.random.randn(4),
                                index=date_range('20130101', periods=4))
        self.frame_ts = DataFrame(np.random.randn(4, 4),
                                  index=date_range('20130101', periods=4))

        dates_rev = (date_range('20130101', periods=4)
                     .sort_values(ascending=False))
        self.series_ts_rev = Series(np.random.randn(4),
                                    index=dates_rev)
        self.frame_ts_rev = DataFrame(np.random.randn(4, 4),
                                      index=dates_rev)

        self.frame_empty = DataFrame()
        self.series_empty = Series()

        # form agglomerates
        for o in self._objs:

            d = dict()
            for t in self._typs:
                d[t] = getattr(self, '%s_%s' % (o, t), None)

            setattr(self, o, d)

def create_data():
    """ create the pickle/msgpack data """

    data = {
        'A': [0., 1., 2., 3., np.nan],
        'B': [0, 1, 0, 1, 0],
        'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
        'D': date_range('1/1/2009', periods=5),
        'E': [0., 1, Timestamp('20100101'), 'foo', 2.]
    }

    index = dict(int=Index(np.arange(10)),
                 date=date_range('20130101', periods=10),
                 period=period_range('2013-01-01', freq='M', periods=10))

    mi = dict(reg2=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                                                      ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])),
                                          names=['first', 'second']))
    series = dict(float=Series(data['A']),
                  int=Series(data['B']),
                  mixed=Series(data['E']),
                  ts=TimeSeries(np.arange(10).astype(np.int64), index=date_range('20130101',periods=10)),
                  mi=Series(np.arange(5).astype(np.float64),
                            index=MultiIndex.from_tuples(tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])),
                                                         names=['one', 'two'])),
                  dup=Series(np.arange(5).astype(np.float64), index=['A', 'B', 'C', 'D', 'A']),
                  cat=Series(Categorical(['foo', 'bar', 'baz'])),
                  per=Series([Period('2000Q1')] * 5))

    mixed_dup_df = DataFrame(data)
    mixed_dup_df.columns = list("ABCDA")
    frame = dict(float=DataFrame(dict(A=series['float'], B=series['float'] + 1)),
                 int=DataFrame(dict(A=series['int'], B=series['int'] + 1)),
                 mixed=DataFrame(dict([(k, data[k]) for k in ['A', 'B', 'C', 'D']])),
                 mi=DataFrame(dict(A=np.arange(5).astype(np.float64), B=np.arange(5).astype(np.int64)),
                              index=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'baz'],
                                                                       ['one', 'two', 'one', 'two', 'three']])),
                                                           names=['first', 'second'])),
                 dup=DataFrame(np.arange(15).reshape(5, 3).astype(np.float64),
                               columns=['A', 'B', 'A']),
                 cat_onecol=DataFrame(dict(A=Categorical(['foo', 'bar']))),
                 cat_and_float=DataFrame(dict(A=Categorical(['foo', 'bar', 'baz']),
                                              B=np.arange(3).astype(np.int64))),
                 mixed_dup=mixed_dup_df)

    mixed_dup_panel = Panel(dict(ItemA=frame['float'], ItemB=frame['int']))
    mixed_dup_panel.items = ['ItemA', 'ItemA']
    panel = dict(float=Panel(dict(ItemA=frame['float'], ItemB=frame['float'] + 1)),
                 dup=Panel(np.arange(30).reshape(3, 5, 2).astype(np.float64),
                           items=['A', 'B', 'A']),
                 mixed_dup=mixed_dup_panel)

    return dict(series=series,
                frame=frame,
                panel=panel,
                index=index,
                mi=mi,
                sp_series=dict(float=_create_sp_series(),
                               ts=_create_sp_tsseries()),
                sp_frame=dict(float=_create_sp_frame()))

    def __init__(self, node_name, parents=None, node_domain=None):
        super(CPT, self).__init__(node_name)

        if node_domain is None or node_domain.__len__() == 0:
            self._domain = ['T', 'F']
        else:
            self._domain = node_domain[:]

        self.m = 1
        self.n = self._domain.__len__()

        if parents is None or parents.__len__() == 0:
            self.rows = [self._name]
            self.cols = MultiIndex.from_product([self._domain])
        else:
            parents_names = []
            parents_domains = []
            for parent in parents:
                parents_names.append(parent.name)
                parents_domains.append(parent.domain)
                self.m = self.m * parent.domain.__len__()
            self.cols = MultiIndex.from_product([self._domain], names=[self._name])
            self.rows = MultiIndex.from_product(parents_domains, names=parents_names)

        self._values = np.zeros((self.m, self.n))
        self._table = DataFrame(self._values, index=self.rows, columns=self.cols)

def test_append_mixed_dtypes():
    # GH 13660
    dti = date_range('2011-01-01', freq='M', periods=3, )
    dti_tz = date_range('2011-01-01', freq='M', periods=3, tz='US/Eastern')
    pi = period_range('2011-01', freq='M', periods=3)

    mi = MultiIndex.from_arrays([[1, 2, 3],
                                 [1.1, np.nan, 3.3],
                                 ['a', 'b', 'c'],
                                 dti, dti_tz, pi])
    assert mi.nlevels == 6

    res = mi.append(mi)
    exp = MultiIndex.from_arrays([[1, 2, 3, 1, 2, 3],
                                  [1.1, np.nan, 3.3, 1.1, np.nan, 3.3],
                                  ['a', 'b', 'c', 'a', 'b', 'c'],
                                  dti.append(dti),
                                  dti_tz.append(dti_tz),
                                  pi.append(pi)])
    tm.assert_index_equal(res, exp)

    other = MultiIndex.from_arrays([['x', 'y', 'z'], ['x', 'y', 'z'],
                                    ['x', 'y', 'z'], ['x', 'y', 'z'],
                                    ['x', 'y', 'z'], ['x', 'y', 'z']])

    res = mi.append(other)
    exp = MultiIndex.from_arrays([[1, 2, 3, 'x', 'y', 'z'],
                                  [1.1, np.nan, 3.3, 'x', 'y', 'z'],
                                  ['a', 'b', 'c', 'x', 'y', 'z'],
                                  dti.append(pd.Index(['x', 'y', 'z'])),
                                  dti_tz.append(pd.Index(['x', 'y', 'z'])),
                                  pi.append(pd.Index(['x', 'y', 'z']))])
    tm.assert_index_equal(res, exp)

    def test_join_multiindex(self):
        index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'],
                                         [1, 2, 3, 1, 2, 3]],
                                        names=['first', 'second'])

        index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'],
                                         [1, 2, 3, 1, 2, 3]],
                                        names=['first', 'second'])

        df1 = DataFrame(data=np.random.randn(6), index=index1,
                        columns=['var X'])
        df2 = DataFrame(data=np.random.randn(6), index=index2,
                        columns=['var Y'])

        df1 = df1.sort_index(level=0)
        df2 = df2.sort_index(level=0)

        joined = df1.join(df2, how='outer')
        ex_index = Index(index1.values).union(Index(index2.values))
        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
        expected.index.names = index1.names
        assert_frame_equal(joined, expected)
        assert joined.index.names == index1.names

        df1 = df1.sort_index(level=1)
        df2 = df2.sort_index(level=1)

        joined = df1.join(df2, how='outer').sort_index(level=0)
        ex_index = Index(index1.values).union(Index(index2.values))
        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
        expected.index.names = index1.names

        assert_frame_equal(joined, expected)
        assert joined.index.names == index1.names

    def test_loc_getitem_int_slice(self):
        # GH 3053
        # loc should treat integer slices like label slices

        index = MultiIndex.from_tuples([t for t in itertools.product(
            [6, 7, 8], ['a', 'b'])])
        df = DataFrame(np.random.randn(6, 6), index, index)
        result = df.loc[6:8, :]
        expected = df
        tm.assert_frame_equal(result, expected)

        index = MultiIndex.from_tuples([t
                                        for t in itertools.product(
                                            [10, 20, 30], ['a', 'b'])])
        df = DataFrame(np.random.randn(6, 6), index, index)
        result = df.loc[20:30, :]
        expected = df.iloc[2:]
        tm.assert_frame_equal(result, expected)

        # doc examples
        result = df.loc[10, :]
        expected = df.iloc[0:2]
        expected.index = ['a', 'b']
        tm.assert_frame_equal(result, expected)

        result = df.loc[:, 10]
        expected = df[10]
        tm.assert_frame_equal(result, expected)

    def test_join_multi_to_multi(self, join_type):
        # GH 20475
        leftindex = MultiIndex.from_product([list('abc'), list('xy'), [1, 2]],
                                            names=['abc', 'xy', 'num'])
        left = DataFrame({'v1': range(12)}, index=leftindex)

        rightindex = MultiIndex.from_product([list('abc'), list('xy')],
                                             names=['abc', 'xy'])
        right = DataFrame({'v2': [100 * i for i in range(1, 7)]},
                          index=rightindex)

        result = left.join(right, on=['abc', 'xy'], how=join_type)
        expected = (left.reset_index()
                        .merge(right.reset_index(),
                               on=['abc', 'xy'], how=join_type)
                        .set_index(['abc', 'xy', 'num'])
                    )
        assert_frame_equal(expected, result)

        msg = (r'len\(left_on\) must equal the number of levels in the index'
               ' of "right"')
        with pytest.raises(ValueError, match=msg):
            left.join(right, on='xy', how=join_type)

        with pytest.raises(ValueError, match=msg):
            right.join(left, on=['abc', 'xy'], how=join_type)

def test_loc_getitem_duplicates_multiindex_missing_indexers(indexer, is_level1,
                                                            expected_error):
    # GH 7866
    # multi-index slicing with missing indexers
    idx = MultiIndex.from_product([['A', 'B', 'C'],
                                   ['foo', 'bar', 'baz']],
                                  names=['one', 'two'])
    s = Series(np.arange(9, dtype='int64'), index=idx).sort_index()

    if indexer == []:
        expected = s.iloc[[]]
    elif is_level1:
        expected = Series([0, 3, 6], index=MultiIndex.from_product(
            [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index()
    else:
        exp_idx = MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']],
                                          names=['one', 'two'])
        expected = Series(np.arange(3, dtype='int64'),
                          index=exp_idx).sort_index()

    if expected_error is not None:
        with pytest.raises(KeyError, match=expected_error):
            s.loc[indexer]
    else:
        result = s.loc[indexer]
        tm.assert_series_equal(result, expected)

def test_get_info_after_update(chunkstore_lib):
    df = DataFrame(data={'data': [1.1, 2.1, 3.1]},
                   index=MultiIndex.from_tuples([(dt(2016, 1, 1), 1),
                                                 (dt(2016, 1, 2), 1),
                                                 (dt(2016, 1, 3), 1)],
                                                names=['date', 'id'])
                   )
    chunkstore_lib.write('test_df', df, 'D')
    df2 = DataFrame(data={'data': [1.1, 1.1, 1.1]},
                    index=MultiIndex.from_tuples([(dt(2016, 1, 1), 2),
                                                  (dt(2016, 1, 2), 2),
                                                  (dt(2016, 1, 4), 1)],
                                                 names=['date', 'id'])
                    )
    chunkstore_lib.update('test_df', df2)
    assert_frame_equal(chunkstore_lib.read('test_df'), pd.concat([df, df2]).sort())

    info = {'rows': 6,
            'dtype': [('date', '<M8[ns]'), ('id', '<i8'), ('data', '<f8')],
            'chunk_count': 4,
            'col_names': {u'index': [u'date', u'id'], u'index_tz': [None, None], u'columns': [u'data']},
            'type': u'df',
            'size': 144}

    assert(chunkstore_lib.get_info('test_df') == info)