test_multi.py

# -*- coding: utf-8 -*-

import re
import warnings

from datetime import timedelta
from itertools import product

import pytest

import numpy as np

import pandas as pd

from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
                    compat, date_range, period_range)
from pandas.compat import PY3, long, lrange, lzip, range, u, PYPY
from pandas.errors import PerformanceWarning, UnsortedIndexError
from pandas.core.dtypes.dtypes import CategoricalDtype
from pandas.core.indexes.base import InvalidIndexError
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
from pandas._libs.tslib import Timestamp

import pandas.util.testing as tm

from pandas.util.testing import assert_almost_equal, assert_copy

from .common import Base


class TestMultiIndex(Base):
    _holder = MultiIndex
    _compat_props = ['shape', 'ndim', 'size', 'itemsize']

    def setup_method(self, method):
        major_axis = Index(['foo', 'bar', 'baz', 'qux'])
        minor_axis = Index(['one', 'two'])

        major_labels = np.array([0, 0, 1, 2, 3, 3])
        minor_labels = np.array([0, 1, 0, 1, 0, 1])
        self.index_names = ['first', 'second']
        self.indices = dict(index=MultiIndex(levels=[major_axis, minor_axis],
                                             labels=[major_labels, minor_labels
                                                     ], names=self.index_names,
                                             verify_integrity=False))
        self.setup_indices()

    def create_index(self):
        return self.index

    def test_boolean_context_compat2(self):

        # boolean context compat
        # GH7897
        i1 = MultiIndex.from_tuples([('A', 1), ('A', 2)])
        i2 = MultiIndex.from_tuples([('A', 1), ('A', 3)])
        common = i1.intersection(i2)

        def f():
            if common:
                pass

        tm.assert_raises_regex(ValueError, 'The truth value of a', f)

    def test_labels_dtypes(self):

        # GH 8456
        i = MultiIndex.from_tuples([('A', 1), ('A', 2)])
        assert i.labels[0].dtype == 'int8'
        assert i.labels[1].dtype == 'int8'

        i = MultiIndex.from_product([['a'], range(40)])
        assert i.labels[1].dtype == 'int8'
        i = MultiIndex.from_product([['a'], range(400)])
        assert i.labels[1].dtype == 'int16'
        i = MultiIndex.from_product([['a'], range(40000)])
        assert i.labels[1].dtype == 'int32'

        i = pd.MultiIndex.from_product([['a'], range(1000)])
        assert (i.labels[0] >= 0).all()
        assert (i.labels[1] >= 0).all()

    def test_where(self):
        i = MultiIndex.from_tuples([('A', 1), ('A', 2)])

        def f():
            i.where(True)

        pytest.raises(NotImplementedError, f)

    def test_where_array_like(self):
        i = MultiIndex.from_tuples([('A', 1), ('A', 2)])
        klasses = [list, tuple, np.array, pd.Series]
        cond = [False, True]

        for klass in klasses:
            f = lambda: i.where(klass(cond))
            pytest.raises(NotImplementedError, f)

    def test_repeat(self):
        reps = 2
        numbers = [1, 2, 3]
        names = np.array(['foo', 'bar'])

        m = MultiIndex.from_product([
            numbers, names], names=names)
        expected = MultiIndex.from_product([
            numbers, names.repeat(reps)], names=names)
        tm.assert_index_equal(m.repeat(reps), expected)

        with tm.assert_produces_warning(FutureWarning):
            result = m.repeat(n=reps)
            tm.assert_index_equal(result, expected)

    def test_numpy_repeat(self):
        reps = 2
        numbers = [1, 2, 3]
        names = np.array(['foo', 'bar'])

        m = MultiIndex.from_product([
            numbers, names], names=names)
        expected = MultiIndex.from_product([
            numbers, names.repeat(reps)], names=names)
        tm.assert_index_equal(np.repeat(m, reps), expected)

        msg = "the 'axis' parameter is not supported"
        tm.assert_raises_regex(
            ValueError, msg, np.repeat, m, reps, axis=1)

    def test_set_name_methods(self):
        # so long as these are synonyms, we don't need to test set_names
        assert self.index.rename == self.index.set_names
        new_names = [name + "SUFFIX" for name in self.index_names]
        ind = self.index.set_names(new_names)
        assert self.index.names == self.index_names
        assert ind.names == new_names
        with tm.assert_raises_regex(ValueError, "^Length"):
            ind.set_names(new_names + new_names)
        new_names2 = [name + "SUFFIX2" for name in new_names]
        res = ind.set_names(new_names2, inplace=True)
        assert res is None
        assert ind.names == new_names2

        # set names for specific level (# GH7792)
        ind = self.index.set_names(new_names[0], level=0)
        assert self.index.names == self.index_names
        assert ind.names == [new_names[0], self.index_names[1]]

        res = ind.set_names(new_names2[0], level=0, inplace=True)
        assert res is None
        assert ind.names == [new_names2[0], self.index_names[1]]

        # set names for multiple levels
        ind = self.index.set_names(new_names, level=[0, 1])
        assert self.index.names == self.index_names
        assert ind.names == new_names

        res = ind.set_names(new_names2, level=[0, 1], inplace=True)
        assert res is None
        assert ind.names == new_names2

    def test_set_levels_labels_directly(self):
        # setting levels/labels directly raises AttributeError

        levels = self.index.levels
        new_levels = [[lev + 'a' for lev in level] for level in levels]

        labels = self.index.labels
        major_labels, minor_labels = labels
        major_labels = [(x + 1) % 3 for x in major_labels]
        minor_labels = [(x + 1) % 1 for x in minor_labels]
        new_labels = [major_labels, minor_labels]

        with pytest.raises(AttributeError):
            self.index.levels = new_levels

        with pytest.raises(AttributeError):
            self.index.labels = new_labels

    def test_set_levels(self):
        # side note - you probably wouldn't want to use levels and labels
        # directly like this - but it is possible.
        levels = self.index.levels
        new_levels = [[lev + 'a' for lev in level] for level in levels]

        def assert_matching(actual, expected, check_dtype=False):
            # avoid specifying internal representation
            # as much as possible
            assert len(actual) == len(expected)
            for act, exp in zip(actual, expected):
                act = np.asarray(act)
                exp = np.asarray(exp)
                tm.assert_numpy_array_equal(act, exp, check_dtype=check_dtype)

        # level changing [w/o mutation]
        ind2 = self.index.set_levels(new_levels)
        assert_matching(ind2.levels, new_levels)
        assert_matching(self.index.levels, levels)

        # level changing [w/ mutation]
        ind2 = self.index.copy()
        inplace_return = ind2.set_levels(new_levels, inplace=True)
        assert inplace_return is None
        assert_matching(ind2.levels, new_levels)

        # level changing specific level [w/o mutation]
        ind2 = self.index.set_levels(new_levels[0], level=0)
        assert_matching(ind2.levels, [new_levels[0], levels[1]])
        assert_matching(self.index.levels, levels)

        ind2 = self.index.set_levels(new_levels[1], level=1)
        assert_matching(ind2.levels, [levels[0], new_levels[1]])
        assert_matching(self.index.levels, levels)

        # level changing multiple levels [w/o mutation]
        ind2 = self.index.set_levels(new_levels, level=[0, 1])
        assert_matching(ind2.levels, new_levels)
        assert_matching(self.index.levels, levels)

        # level changing specific level [w/ mutation]
        ind2 = self.index.copy()
        inplace_return = ind2.set_levels(new_levels[0], level=0, inplace=True)
        assert inplace_return is None
        assert_matching(ind2.levels, [new_levels[0], levels[1]])
        assert_matching(self.index.levels, levels)

        ind2 = self.index.copy()
        inplace_return = ind2.set_levels(new_levels[1], level=1, inplace=True)
        assert inplace_return is None
        assert_matching(ind2.levels, [levels[0], new_levels[1]])
        assert_matching(self.index.levels, levels)

        # level changing multiple levels [w/ mutation]
        ind2 = self.index.copy()
        inplace_return = ind2.set_levels(new_levels, level=[0, 1],
                                         inplace=True)
        assert inplace_return is None
        assert_matching(ind2.levels, new_levels)
        assert_matching(self.index.levels, levels)

        # illegal level changing should not change levels
        # GH 13754
        original_index = self.index.copy()
        for inplace in [True, False]:
            with tm.assert_raises_regex(ValueError, "^On"):
                self.index.set_levels(['c'], level=0, inplace=inplace)
            assert_matching(self.index.levels, original_index.levels,
                            check_dtype=True)

            with tm.assert_raises_regex(ValueError, "^On"):
                self.index.set_labels([0, 1, 2, 3, 4, 5], level=0,
                                      inplace=inplace)
            assert_matching(self.index.labels, original_index.labels,
                            check_dtype=True)

            with tm.assert_raises_regex(TypeError, "^Levels"):
                self.index.set_levels('c', level=0, inplace=inplace)
            assert_matching(self.index.levels, original_index.levels,
                            check_dtype=True)

            with tm.assert_raises_regex(TypeError, "^Labels"):
                self.index.set_labels(1, level=0, inplace=inplace)
            assert_matching(self.index.labels, original_index.labels,
                            check_dtype=True)

    def test_set_labels(self):
        # side note - you probably wouldn't want to use levels and labels
        # directly like this - but it is possible.
        labels = self.index.labels
        major_labels, minor_labels = labels
        major_labels = [(x + 1) % 3 for x in major_labels]
        minor_labels = [(x + 1) % 1 for x in minor_labels]
        new_labels = [major_labels, minor_labels]

        def assert_matching(actual, expected):
            # avoid specifying internal representation
            # as much as possible
            assert len(actual) == len(expected)
            for act, exp in zip(actual, expected):
                act = np.asarray(act)
                exp = np.asarray(exp, dtype=np.int8)
                tm.assert_numpy_array_equal(act, exp)

        # label changing [w/o mutation]
        ind2 = self.index.set_labels(new_labels)
        assert_matching(ind2.labels, new_labels)
        assert_matching(self.index.labels, labels)

        # label changing [w/ mutation]
        ind2 = self.index.copy()
        inplace_return = ind2.set_labels(new_labels, inplace=True)
        assert inplace_return is None
        assert_matching(ind2.labels, new_labels)

        # label changing specific level [w/o mutation]
        ind2 = self.index.set_labels(new_labels[0], level=0)
        assert_matching(ind2.labels, [new_labels[0], labels[1]])
        assert_matching(self.index.labels, labels)

        ind2 = self.index.set_labels(new_labels[1], level=1)
        assert_matching(ind2.labels, [labels[0], new_labels[1]])
        assert_matching(self.index.labels, labels)

        # label changing multiple levels [w/o mutation]
        ind2 = self.index.set_labels(new_labels, level=[0, 1])
        assert_matching(ind2.labels, new_labels)
        assert_matching(self.index.labels, labels)

        # label changing specific level [w/ mutation]
        ind2 = self.index.copy()
        inplace_return = ind2.set_labels(new_labels[0], level=0, inplace=True)
        assert inplace_return is None
        assert_matching(ind2.labels, [new_labels[0], labels[1]])
        assert_matching(self.index.labels, labels)

        ind2 = self.index.copy()
        inplace_return = ind2.set_labels(new_labels[1], level=1, inplace=True)
        assert inplace_return is None
        assert_matching(ind2.labels, [labels[0], new_labels[1]])
        assert_matching(self.index.labels, labels)

        # label changing multiple levels [w/ mutation]
        ind2 = self.index.copy()
        inplace_return = ind2.set_labels(new_labels, level=[0, 1],
                                         inplace=True)
        assert inplace_return is None
        assert_matching(ind2.labels, new_labels)
        assert_matching(self.index.labels, labels)

        # label changing for levels of different magnitude of categories
        ind = pd.MultiIndex.from_tuples([(0, i) for i in range(130)])
        new_labels = range(129, -1, -1)
        expected = pd.MultiIndex.from_tuples(
            [(0, i) for i in new_labels])

        # [w/o mutation]
        result = ind.set_labels(labels=new_labels, level=1)
        assert result.equals(expected)

        # [w/ mutation]
        result = ind.copy()
        result.set_labels(labels=new_labels, level=1, inplace=True)
        assert result.equals(expected)

    def test_set_levels_labels_names_bad_input(self):
        levels, labels = self.index.levels, self.index.labels
        names = self.index.names

        with tm.assert_raises_regex(ValueError, 'Length of levels'):
            self.index.set_levels([levels[0]])

        with tm.assert_raises_regex(ValueError, 'Length of labels'):
            self.index.set_labels([labels[0]])

        with tm.assert_raises_regex(ValueError, 'Length of names'):
            self.index.set_names([names[0]])

        # shouldn't scalar data error, instead should demand list-like
        with tm.assert_raises_regex(TypeError, 'list of lists-like'):
            self.index.set_levels(levels[0])

        # shouldn't scalar data error, instead should demand list-like
        with tm.assert_raises_regex(TypeError, 'list of lists-like'):
            self.index.set_labels(labels[0])

        # shouldn't scalar data error, instead should demand list-like
        with tm.assert_raises_regex(TypeError, 'list-like'):
            self.index.set_names(names[0])

        # should have equal lengths
        with tm.assert_raises_regex(TypeError, 'list of lists-like'):
            self.index.set_levels(levels[0], level=[0, 1])

        with tm.assert_raises_regex(TypeError, 'list-like'):
            self.index.set_levels(levels, level=0)

        # should have equal lengths
        with tm.assert_raises_regex(TypeError, 'list of lists-like'):
            self.index.set_labels(labels[0], level=[0, 1])

        with tm.assert_raises_regex(TypeError, 'list-like'):
            self.index.set_labels(labels, level=0)

        # should have equal lengths
        with tm.assert_raises_regex(ValueError, 'Length of names'):
            self.index.set_names(names[0], level=[0, 1])

        with tm.assert_raises_regex(TypeError, 'string'):
            self.index.set_names(names, level=0)

    def test_set_levels_categorical(self):
        # GH13854
        index = MultiIndex.from_arrays([list("xyzx"), [0, 1, 2, 3]])
        for ordered in [False, True]:
            cidx = CategoricalIndex(list("bac"), ordered=ordered)
            result = index.set_levels(cidx, 0)
            expected = MultiIndex(levels=[cidx, [0, 1, 2, 3]],
                                  labels=index.labels)
            tm.assert_index_equal(result, expected)

            result_lvl = result.get_level_values(0)
            expected_lvl = CategoricalIndex(list("bacb"),
                                            categories=cidx.categories,
                                            ordered=cidx.ordered)
            tm.assert_index_equal(result_lvl, expected_lvl)

    def test_metadata_immutable(self):
        levels, labels = self.index.levels, self.index.labels
        # shouldn't be able to set at either the top level or base level
        mutable_regex = re.compile('does not support mutable operations')
        with tm.assert_raises_regex(TypeError, mutable_regex):
            levels[0] = levels[0]
        with tm.assert_raises_regex(TypeError, mutable_regex):
            levels[0][0] = levels[0][0]
        # ditto for labels
        with tm.assert_raises_regex(TypeError, mutable_regex):
            labels[0] = labels[0]
        with tm.assert_raises_regex(TypeError, mutable_regex):
            labels[0][0] = labels[0][0]
        # and for names
        names = self.index.names
        with tm.assert_raises_regex(TypeError, mutable_regex):
            names[0] = names[0]

    def test_inplace_mutation_resets_values(self):
        levels = [['a', 'b', 'c'], [4]]
        levels2 = [[1, 2, 3], ['a']]
        labels = [[0, 1, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0]]

        mi1 = MultiIndex(levels=levels, labels=labels)
        mi2 = MultiIndex(levels=levels2, labels=labels)
        vals = mi1.values.copy()
        vals2 = mi2.values.copy()

        assert mi1._tuples is not None

        # Make sure level setting works
        new_vals = mi1.set_levels(levels2).values
        tm.assert_almost_equal(vals2, new_vals)

        # Non-inplace doesn't kill _tuples [implementation detail]
        tm.assert_almost_equal(mi1._tuples, vals)

        # ...and values is still same too
        tm.assert_almost_equal(mi1.values, vals)

        # Inplace should kill _tuples
        mi1.set_levels(levels2, inplace=True)
        tm.assert_almost_equal(mi1.values, vals2)

        # Make sure label setting works too
        labels2 = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
        exp_values = np.empty((6,), dtype=object)
        exp_values[:] = [(long(1), 'a')] * 6

        # Must be 1d array of tuples
        assert exp_values.shape == (6,)
        new_values = mi2.set_labels(labels2).values

        # Not inplace shouldn't change
        tm.assert_almost_equal(mi2._tuples, vals2)

        # Should have correct values
        tm.assert_almost_equal(exp_values, new_values)

        # ...and again setting inplace should kill _tuples, etc
        mi2.set_labels(labels2, inplace=True)
        tm.assert_almost_equal(mi2.values, new_values)

    def test_copy_in_constructor(self):
        levels = np.array(["a", "b", "c"])
        labels = np.array([1, 1, 2, 0, 0, 1, 1])
        val = labels[0]
        mi = MultiIndex(levels=[levels, levels], labels=[labels, labels],
                        copy=True)
        assert mi.labels[0][0] == val
        labels[0] = 15
        assert mi.labels[0][0] == val
        val = levels[0]
        levels[0] = "PANDA"
        assert mi.levels[0][0] == val

    def test_set_value_keeps_names(self):
        # motivating example from #3742
        lev1 = ['hans', 'hans', 'hans', 'grethe', 'grethe', 'grethe']
        lev2 = ['1', '2', '3'] * 2
        idx = pd.MultiIndex.from_arrays([lev1, lev2], names=['Name', 'Number'])
        df = pd.DataFrame(
            np.random.randn(6, 4),
            columns=['one', 'two', 'three', 'four'],
            index=idx)
        df = df.sort_index()
        assert df._is_copy is None
        assert df.index.names == ('Name', 'Number')
        df.at[('grethe', '4'), 'one'] = 99.34
        assert df._is_copy is None
        assert df.index.names == ('Name', 'Number')

    def test_copy_names(self):
        # Check that adding a "names" parameter to the copy is honored
        # GH14302
        multi_idx = pd.Index([(1, 2), (3, 4)], names=['MyName1', 'MyName2'])
        multi_idx1 = multi_idx.copy()

        assert multi_idx.equals(multi_idx1)
        assert multi_idx.names == ['MyName1', 'MyName2']
        assert multi_idx1.names == ['MyName1', 'MyName2']

        multi_idx2 = multi_idx.copy(names=['NewName1', 'NewName2'])

        assert multi_idx.equals(multi_idx2)
        assert multi_idx.names == ['MyName1', 'MyName2']
        assert multi_idx2.names == ['NewName1', 'NewName2']

        multi_idx3 = multi_idx.copy(name=['NewName1', 'NewName2'])

        assert multi_idx.equals(multi_idx3)
        assert multi_idx.names == ['MyName1', 'MyName2']
        assert multi_idx3.names == ['NewName1', 'NewName2']

    def test_names(self):

        # names are assigned in setup
        names = self.index_names
        level_names = [level.name for level in self.index.levels]
        assert names == level_names

        # setting bad names on existing
        index = self.index
        tm.assert_raises_regex(ValueError, "^Length of names",
                               setattr, index, "names",
                               list(index.names) + ["third"])
        tm.assert_raises_regex(ValueError, "^Length of names",
                               setattr, index, "names", [])

        # initializing with bad names (should always be equivalent)
        major_axis, minor_axis = self.index.levels
        major_labels, minor_labels = self.index.labels
        tm.assert_raises_regex(ValueError, "^Length of names", MultiIndex,
                               levels=[major_axis, minor_axis],
                               labels=[major_labels, minor_labels],
                               names=['first'])
        tm.assert_raises_regex(ValueError, "^Length of names", MultiIndex,
                               levels=[major_axis, minor_axis],
                               labels=[major_labels, minor_labels],
                               names=['first', 'second', 'third'])

        # names are assigned
        index.names = ["a", "b"]
        ind_names = list(index.names)
        level_names = [level.name for level in index.levels]
        assert ind_names == level_names

    def test_astype(self):
        expected = self.index.copy()
        actual = self.index.astype('O')
        assert_copy(actual.levels, expected.levels)
        assert_copy(actual.labels, expected.labels)
        self.check_level_names(actual, expected.names)

        with tm.assert_raises_regex(TypeError, "^Setting.*dtype.*object"):
            self.index.astype(np.dtype(int))

    @pytest.mark.parametrize('ordered', [True, False])
    def test_astype_category(self, ordered):
        # GH 18630
        msg = '> 1 ndim Categorical are not supported at this time'
        with tm.assert_raises_regex(NotImplementedError, msg):
            self.index.astype(CategoricalDtype(ordered=ordered))

        if ordered is False:
            # dtype='category' defaults to ordered=False, so only test once
            with tm.assert_raises_regex(NotImplementedError, msg):
                self.index.astype('category')

    def test_constructor_single_level(self):
        result = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                            labels=[[0, 1, 2, 3]], names=['first'])
        assert isinstance(result, MultiIndex)
        expected = Index(['foo', 'bar', 'baz', 'qux'], name='first')
        tm.assert_index_equal(result.levels[0], expected)
        assert result.names == ['first']

    def test_constructor_no_levels(self):
        tm.assert_raises_regex(ValueError, "non-zero number "
                                           "of levels/labels",
                               MultiIndex, levels=[], labels=[])
        both_re = re.compile('Must pass both levels and labels')
        with tm.assert_raises_regex(TypeError, both_re):
            MultiIndex(levels=[])
        with tm.assert_raises_regex(TypeError, both_re):
            MultiIndex(labels=[])

    def test_constructor_mismatched_label_levels(self):
        labels = [np.array([1]), np.array([2]), np.array([3])]
        levels = ["a"]
        tm.assert_raises_regex(ValueError, "Length of levels and labels "
                                           "must be the same", MultiIndex,
                               levels=levels, labels=labels)
        length_error = re.compile('>= length of level')
        label_error = re.compile(r'Unequal label lengths: \[4, 2\]')

        # important to check that it's looking at the right thing.
        with tm.assert_raises_regex(ValueError, length_error):
            MultiIndex(levels=[['a'], ['b']],
                       labels=[[0, 1, 2, 3], [0, 3, 4, 1]])

        with tm.assert_raises_regex(ValueError, label_error):
            MultiIndex(levels=[['a'], ['b']], labels=[[0, 0, 0, 0], [0, 0]])

        # external API
        with tm.assert_raises_regex(ValueError, length_error):
            self.index.copy().set_levels([['a'], ['b']])

        with tm.assert_raises_regex(ValueError, label_error):
            self.index.copy().set_labels([[0, 0, 0, 0], [0, 0]])

    @pytest.mark.parametrize('names', [['a', 'b', 'a'], [1, 1, 2],
                                       [1, 'a', 1]])
    def test_duplicate_level_names(self, names):
        # GH18872
        pytest.raises(ValueError, pd.MultiIndex.from_product,
                      [[0, 1]] * 3, names=names)

        # With .rename()
        mi = pd.MultiIndex.from_product([[0, 1]] * 3)
        tm.assert_raises_regex(ValueError, "Duplicated level name:",
                               mi.rename, names)

        # With .rename(., level=)
        mi.rename(names[0], level=1, inplace=True)
        tm.assert_raises_regex(ValueError, "Duplicated level name:",
                               mi.rename, names[:2], level=[0, 2])

    def assert_multiindex_copied(self, copy, original):
        # Levels should be (at least, shallow copied)
        tm.assert_copy(copy.levels, original.levels)
        tm.assert_almost_equal(copy.labels, original.labels)

        # Labels doesn't matter which way copied
        tm.assert_almost_equal(copy.labels, original.labels)
        assert copy.labels is not original.labels

        # Names doesn't matter which way copied
        assert copy.names == original.names
        assert copy.names is not original.names

        # Sort order should be copied
        assert copy.sortorder == original.sortorder

    def test_copy(self):
        i_copy = self.index.copy()

        self.assert_multiindex_copied(i_copy, self.index)

    def test_shallow_copy(self):
        i_copy = self.index._shallow_copy()

        self.assert_multiindex_copied(i_copy, self.index)

    def test_view(self):
        i_view = self.index.view()

        self.assert_multiindex_copied(i_view, self.index)

    def check_level_names(self, index, names):
        assert [level.name for level in index.levels] == list(names)

    def test_changing_names(self):

        # names should be applied to levels
        level_names = [level.name for level in self.index.levels]
        self.check_level_names(self.index, self.index.names)

        view = self.index.view()
        copy = self.index.copy()
        shallow_copy = self.index._shallow_copy()

        # changing names should change level names on object
        new_names = [name + "a" for name in self.index.names]
        self.index.names = new_names
        self.check_level_names(self.index, new_names)

        # but not on copies
        self.check_level_names(view, level_names)
        self.check_level_names(copy, level_names)
        self.check_level_names(shallow_copy, level_names)

        # and copies shouldn't change original
        shallow_copy.names = [name + "c" for name in shallow_copy.names]
        self.check_level_names(self.index, new_names)

    def test_get_level_number_integer(self):
        self.index.names = [1, 0]
        assert self.index._get_level_number(1) == 0
        assert self.index._get_level_number(0) == 1
        pytest.raises(IndexError, self.index._get_level_number, 2)
        tm.assert_raises_regex(KeyError, 'Level fourth not found',
                               self.index._get_level_number, 'fourth')

    def test_from_arrays(self):
        arrays = []
        for lev, lab in zip(self.index.levels, self.index.labels):
            arrays.append(np.asarray(lev).take(lab))

        # list of arrays as input
        result = MultiIndex.from_arrays(arrays, names=self.index.names)
        tm.assert_index_equal(result, self.index)

        # infer correctly
        result = MultiIndex.from_arrays([[pd.NaT, Timestamp('20130101')],
                                         ['a', 'b']])
        assert result.levels[0].equals(Index([Timestamp('20130101')]))
        assert result.levels[1].equals(Index(['a', 'b']))

    def test_from_arrays_iterator(self):
        # GH 18434
        arrays = []
        for lev, lab in zip(self.index.levels, self.index.labels):
            arrays.append(np.asarray(lev).take(lab))

        # iterator as input
        result = MultiIndex.from_arrays(iter(arrays), names=self.index.names)
        tm.assert_index_equal(result, self.index)

        # invalid iterator input
        with tm.assert_raises_regex(
                TypeError, "Input must be a list / sequence of array-likes."):
            MultiIndex.from_arrays(0)

    def test_from_arrays_index_series_datetimetz(self):
        idx1 = pd.date_range('2015-01-01 10:00', freq='D', periods=3,
                             tz='US/Eastern')
        idx2 = pd.date_range('2015-01-01 10:00', freq='H', periods=3,
                             tz='Asia/Tokyo')
        result = pd.MultiIndex.from_arrays([idx1, idx2])
        tm.assert_index_equal(result.get_level_values(0), idx1)
        tm.assert_index_equal(result.get_level_values(1), idx2)

        result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)])
        tm.assert_index_equal(result2.get_level_values(0), idx1)
        tm.assert_index_equal(result2.get_level_values(1), idx2)

        tm.assert_index_equal(result, result2)

    def test_from_arrays_index_series_timedelta(self):
        idx1 = pd.timedelta_range('1 days', freq='D', periods=3)
        idx2 = pd.timedelta_range('2 hours', freq='H', periods=3)
        result = pd.MultiIndex.from_arrays([idx1, idx2])
        tm.assert_index_equal(result.get_level_values(0), idx1)
        tm.assert_index_equal(result.get_level_values(1), idx2)

        result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)])
        tm.assert_index_equal(result2.get_level_values(0), idx1)
        tm.assert_index_equal(result2.get_level_values(1), idx2)

        tm.assert_index_equal(result, result2)

    def test_from_arrays_index_series_period(self):
        idx1 = pd.period_range('2011-01-01', freq='D', periods=3)
        idx2 = pd.period_range('2015-01-01', freq='H', periods=3)
        result = pd.MultiIndex.from_arrays([idx1, idx2])
        tm.assert_index_equal(result.get_level_values(0), idx1)
        tm.assert_index_equal(result.get_level_values(1), idx2)

        result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)])
        tm.assert_index_equal(result2.get_level_values(0), idx1)
        tm.assert_index_equal(result2.get_level_values(1), idx2)

        tm.assert_index_equal(result, result2)

    def test_from_arrays_index_datetimelike_mixed(self):
        idx1 = pd.date_range('2015-01-01 10:00', freq='D', periods=3,
                             tz='US/Eastern')
        idx2 = pd.date_range('2015-01-01 10:00', freq='H', periods=3)
        idx3 = pd.timedelta_range('1 days', freq='D', periods=3)
        idx4 = pd.period_range('2011-01-01', freq='D', periods=3)

        result = pd.MultiIndex.from_arrays([idx1, idx2, idx3, idx4])
        tm.assert_index_equal(result.get_level_values(0), idx1)
        tm.assert_index_equal(result.get_level_values(1), idx2)
        tm.assert_index_equal(result.get_level_values(2), idx3)
        tm.assert_index_equal(result.get_level_values(3), idx4)

        result2 = pd.MultiIndex.from_arrays([pd.Series(idx1),
                                             pd.Series(idx2),
                                             pd.Series(idx3),
                                             pd.Series(idx4)])
        tm.assert_index_equal(result2.get_level_values(0), idx1)
        tm.assert_index_equal(result2.get_level_values(1), idx2)
        tm.assert_index_equal(result2.get_level_values(2), idx3)
        tm.assert_index_equal(result2.get_level_values(3), idx4)

        tm.assert_index_equal(result, result2)

    def test_from_arrays_index_series_categorical(self):
        # GH13743
        idx1 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"),
                                   ordered=False)
        idx2 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"),
                                   ordered=True)

        result = pd.MultiIndex.from_arrays([idx1, idx2])
        tm.assert_index_equal(result.get_level_values(0), idx1)
        tm.assert_index_equal(result.get_level_values(1), idx2)

        result2 = pd.MultiIndex.from_arrays([pd.Series(idx1), pd.Series(idx2)])
        tm.assert_index_equal(result2.get_level_values(0), idx1)
        tm.assert_index_equal(result2.get_level_values(1), idx2)

        result3 = pd.MultiIndex.from_arrays([idx1.values, idx2.values])
        tm.assert_index_equal(result3.get_level_values(0), idx1)
        tm.assert_index_equal(result3.get_level_values(1), idx2)

    def test_from_arrays_empty(self):
        # 0 levels
        with tm.assert_raises_regex(
                ValueError, "Must pass non-zero number of levels/labels"):
            MultiIndex.from_arrays(arrays=[])

        # 1 level
        result = MultiIndex.from_arrays(arrays=[[]], names=['A'])
        assert isinstance(result, MultiIndex)
        expected = Index([], name='A')
        tm.assert_index_equal(result.levels[0], expected)

        # N levels
        for N in [2, 3]:
            arrays = [[]] * N
            names = list('ABC')[:N]
            result = MultiIndex.from_arrays(arrays=arrays, names=names)
            expected = MultiIndex(levels=[[]] * N, labels=[[]] * N,
                                  names=names)
            tm.assert_index_equal(result, expected)

    def test_from_arrays_invalid_input(self):
        invalid_inputs = [1, [1], [1, 2], [[1], 2],
                          'a', ['a'], ['a', 'b'], [['a'], 'b']]
        for i in invalid_inputs:
            pytest.raises(TypeError, MultiIndex.from_arrays, arrays=i)

    def test_from_arrays_different_lengths(self):
        # see gh-13599
        idx1 = [1, 2, 3]
        idx2 = ['a', 'b']
        tm.assert_raises_regex(ValueError, '^all arrays must '
                                           'be same length$',
                               MultiIndex.from_arrays, [idx1, idx2])

        idx1 = []
        idx2 = ['a', 'b']
        tm.assert_raises_regex(ValueError, '^all arrays must '
                                           'be same length$',
                               MultiIndex.from_arrays, [idx1, idx2])

        idx1 = [1, 2, 3]
        idx2 = []
        tm.assert_raises_regex(ValueError, '^all arrays must '
                                           'be same length$',
                               MultiIndex.from_arrays, [idx1, idx2])

    def test_from_product(self):

        first = ['foo', 'bar', 'buz']
        second = ['a', 'b', 'c']
        names = ['first', 'second']
        result = MultiIndex.from_product([first, second], names=names)

        tuples = [('foo', 'a'), ('foo', 'b'), ('foo', 'c'), ('bar', 'a'),
                  ('bar', 'b'), ('bar', 'c'), ('buz', 'a'), ('buz', 'b'),
                  ('buz', 'c')]
        expected = MultiIndex.from_tuples(tuples, names=names)

        tm.assert_index_equal(result, expected)

    def test_from_product_iterator(self):
        # GH 18434
        first = ['foo', 'bar', 'buz']
        second = ['a', 'b', 'c']
        names = ['first', 'second']
        tuples = [('foo', 'a'), ('foo', 'b'), ('foo', 'c'), ('bar', 'a'),
                  ('bar', 'b'), ('bar', 'c'), ('buz', 'a'), ('buz', 'b'),
                  ('buz', 'c')]
        expected = MultiIndex.from_tuples(tuples, names=names)

        # iterator as input
        result = MultiIndex.from_product(iter([first, second]), names=names)
        tm.assert_index_equal(result, expected)

        # Invalid non-iterable input
        with tm.assert_raises_regex(
                TypeError, "Input must be a list / sequence of iterables."):
            MultiIndex.from_product(0)

    def test_from_product_empty(self):
        # 0 levels
        with tm.assert_raises_regex(
                ValueError, "Must pass non-zero number of levels/labels"):
            MultiIndex.from_product([])

        # 1 level
        result = MultiIndex.from_product([[]], names=['A'])
        expected = pd.Index([], name='A')
        tm.assert_index_equal(result.levels[0], expected)

        # 2 levels
        l1 = [[], ['foo', 'bar', 'baz'], []]
        l2 = [[], [], ['a', 'b', 'c']]
        names = ['A', 'B']
        for first, second in zip(l1, l2):
            result = MultiIndex.from_product([first, second], names=names)
            expected = MultiIndex(levels=[first, second],
                                  labels=[[], []], names=names)
            tm.assert_index_equal(result, expected)

        # GH12258
        names = ['A', 'B', 'C']
        for N in range(4):
            lvl2 = lrange(N)
            result = MultiIndex.from_product([[], lvl2, []], names=names)
            expected = MultiIndex(levels=[[], lvl2, []],
                                  labels=[[], [], []], names=names)
            tm.assert_index_equal(result, expected)

    def test_from_product_invalid_input(self):
        invalid_inputs = [1, [1], [1, 2], [[1], 2],
                          'a', ['a'], ['a', 'b'], [['a'], 'b']]
        for i in invalid_inputs:
            pytest.raises(TypeError, MultiIndex.from_product, iterables=i)

    def test_from_product_datetimeindex(self):
        dt_index = date_range('2000-01-01', periods=2)
        mi = pd.MultiIndex.from_product([[1, 2], dt_index])
        etalon = construct_1d_object_array_from_listlike([(1, pd.Timestamp(
            '2000-01-01')), (1, pd.Timestamp('2000-01-02')), (2, pd.Timestamp(
                '2000-01-01')), (2, pd.Timestamp('2000-01-02'))])
        tm.assert_numpy_array_equal(mi.values, etalon)

    def test_from_product_index_series_categorical(self):
        # GH13743
        first = ['foo', 'bar']
        for ordered in [False, True]:
            idx = pd.CategoricalIndex(list("abcaab"), categories=list("bac"),
                                      ordered=ordered)
            expected = pd.CategoricalIndex(list("abcaab") + list("abcaab"),
                                           categories=list("bac"),
                                           ordered=ordered)

            for arr in [idx, pd.Series(idx), idx.values]:
                result = pd.MultiIndex.from_product([first, arr])
                tm.assert_index_equal(result.get_level_values(1), expected)

    def test_values_boxed(self):
        tuples = [(1, pd.Timestamp('2000-01-01')), (2, pd.NaT),
                  (3, pd.Timestamp('2000-01-03')),
                  (1, pd.Timestamp('2000-01-04')),
                  (2, pd.Timestamp('2000-01-02')),
                  (3, pd.Timestamp('2000-01-03'))]
        result = pd.MultiIndex.from_tuples(tuples)
        expected = construct_1d_object_array_from_listlike(tuples)
        tm.assert_numpy_array_equal(result.values, expected)
        # Check that code branches for boxed values produce identical results
        tm.assert_numpy_array_equal(result.values[:4], result[:4].values)

    def test_values_multiindex_datetimeindex(self):
        # Test to ensure we hit the boxing / nobox part of MI.values
        ints = np.arange(10 ** 18, 10 ** 18 + 5)
        naive = pd.DatetimeIndex(ints)
        aware = pd.DatetimeIndex(ints, tz='US/Central')

        idx = pd.MultiIndex.from_arrays([naive, aware])
        result = idx.values

        outer = pd.DatetimeIndex([x[0] for x in result])
        tm.assert_index_equal(outer, naive)

        inner = pd.DatetimeIndex([x[1] for x in result])
        tm.assert_index_equal(inner, aware)

        # n_lev > n_lab
        result = idx[:2].values

        outer = pd.DatetimeIndex([x[0] for x in result])
        tm.assert_index_equal(outer, naive[:2])

        inner = pd.DatetimeIndex([x[1] for x in result])
        tm.assert_index_equal(inner, aware[:2])

    def test_values_multiindex_periodindex(self):
        # Test to ensure we hit the boxing / nobox part of MI.values
        ints = np.arange(2007, 2012)
        pidx = pd.PeriodIndex(ints, freq='D')

        idx = pd.MultiIndex.from_arrays([ints, pidx])
        result = idx.values

        outer = pd.Int64Index([x[0] for x in result])
        tm.assert_index_equal(outer, pd.Int64Index(ints))

        inner = pd.PeriodIndex([x[1] for x in result])
        tm.assert_index_equal(inner, pidx)

        # n_lev > n_lab
        result = idx[:2].values

        outer = pd.Int64Index([x[0] for x in result])
        tm.assert_index_equal(outer, pd.Int64Index(ints[:2]))

        inner = pd.PeriodIndex([x[1] for x in result])
        tm.assert_index_equal(inner, pidx[:2])

    def test_append(self):
        result = self.index[:3].append(self.index[3:])
        assert result.equals(self.index)

        foos = [self.index[:1], self.index[1:3], self.index[3:]]
        result = foos[0].append(foos[1:])
        assert result.equals(self.index)

        # empty
        result = self.index.append([])
        assert result.equals(self.index)

    def test_append_mixed_dtypes(self):
        # 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_get_level_values(self):
        result = self.index.get_level_values(0)
        expected = Index(['foo', 'foo', 'bar', 'baz', 'qux', 'qux'],
                         name='first')
        tm.assert_index_equal(result, expected)
        assert result.name == 'first'

        result = self.index.get_level_values('first')
        expected = self.index.get_level_values(0)
        tm.assert_index_equal(result, expected)

        # GH 10460
        index = MultiIndex(
            levels=[CategoricalIndex(['A', 'B']),
                    CategoricalIndex([1, 2, 3])],
            labels=[np.array([0, 0, 0, 1, 1, 1]),
                    np.array([0, 1, 2, 0, 1, 2])])

        exp = CategoricalIndex(['A', 'A', 'A', 'B', 'B', 'B'])
        tm.assert_index_equal(index.get_level_values(0), exp)
        exp = CategoricalIndex([1, 2, 3, 1, 2, 3])
        tm.assert_index_equal(index.get_level_values(1), exp)

    def test_get_level_values_int_with_na(self):
        # GH 17924
        arrays = [['a', 'b', 'b'], [1, np.nan, 2]]
        index = pd.MultiIndex.from_arrays(arrays)
        result = index.get_level_values(1)
        expected = Index([1, np.nan, 2])
        tm.assert_index_equal(result, expected)

        arrays = [['a', 'b', 'b'], [np.nan, np.nan, 2]]
        index = pd.MultiIndex.from_arrays(arrays)
        result = index.get_level_values(1)
        expected = Index([np.nan, np.nan, 2])
        tm.assert_index_equal(result, expected)

    def test_get_level_values_na(self):
        arrays = [[np.nan, np.nan, np.nan], ['a', np.nan, 1]]
        index = pd.MultiIndex.from_arrays(arrays)
        result = index.get_level_values(0)
        expected = pd.Index([np.nan, np.nan, np.nan])
        tm.assert_index_equal(result, expected)

        result = index.get_level_values(1)
        expected = pd.Index(['a', np.nan, 1])
        tm.assert_index_equal(result, expected)

        arrays = [['a', 'b', 'b'], pd.DatetimeIndex([0, 1, pd.NaT])]
        index = pd.MultiIndex.from_arrays(arrays)
        result = index.get_level_values(1)
        expected = pd.DatetimeIndex([0, 1, pd.NaT])
        tm.assert_index_equal(result, expected)

        arrays = [[], []]
        index = pd.MultiIndex.from_arrays(arrays)
        result = index.get_level_values(0)
        expected = pd.Index([], dtype=object)
        tm.assert_index_equal(result, expected)

    def test_get_level_values_all_na(self):
        # GH 17924 when level entirely consists of nan
        arrays = [[np.nan, np.nan, np.nan], ['a', np.nan, 1]]
        index = pd.MultiIndex.from_arrays(arrays)
        result = index.get_level_values(0)
        expected = pd.Index([np.nan, np.nan, np.nan], dtype=np.float64)
        tm.assert_index_equal(result, expected)

        result = index.get_level_values(1)
        expected = pd.Index(['a', np.nan, 1], dtype=object)
        tm.assert_index_equal(result, expected)

    def test_reorder_levels(self):
        # this blows up
        tm.assert_raises_regex(IndexError, '^Too many levels',
                               self.index.reorder_levels, [2, 1, 0])

    def test_nlevels(self):
        assert self.index.nlevels == 2

    def test_iter(self):
        result = list(self.index)
        expected = [('foo', 'one'), ('foo', 'two'), ('bar', 'one'),
                    ('baz', 'two'), ('qux', 'one'), ('qux', 'two')]
        assert result == expected

    def test_legacy_pickle(self, datapath):
        if PY3:
            pytest.skip("testing for legacy pickles not "
                        "support on py3")

        path = datapath('indexes', 'data', 'multiindex_v1.pickle')
        obj = pd.read_pickle(path)

        obj2 = MultiIndex.from_tuples(obj.values)
        assert obj.equals(obj2)

        res = obj.get_indexer(obj)
        exp = np.arange(len(obj), dtype=np.intp)
        assert_almost_equal(res, exp)

        res = obj.get_indexer(obj2[::-1])
        exp = obj.get_indexer(obj[::-1])
        exp2 = obj2.get_indexer(obj2[::-1])
        assert_almost_equal(res, exp)
        assert_almost_equal(exp, exp2)

    def test_legacy_v2_unpickle(self, datapath):

        # 0.7.3 -> 0.8.0 format manage
        path = datapath('indexes', 'data', 'mindex_073.pickle')
        obj = pd.read_pickle(path)

        obj2 = MultiIndex.from_tuples(obj.values)
        assert obj.equals(obj2)

        res = obj.get_indexer(obj)
        exp = np.arange(len(obj), dtype=np.intp)
        assert_almost_equal(res, exp)

        res = obj.get_indexer(obj2[::-1])
        exp = obj.get_indexer(obj[::-1])
        exp2 = obj2.get_indexer(obj2[::-1])
        assert_almost_equal(res, exp)
        assert_almost_equal(exp, exp2)

    def test_roundtrip_pickle_with_tz(self):

        # GH 8367
        # round-trip of timezone
        index = MultiIndex.from_product(
            [[1, 2], ['a', 'b'], date_range('20130101', periods=3,
                                            tz='US/Eastern')
             ], names=['one', 'two', 'three'])
        unpickled = tm.round_trip_pickle(index)
        assert index.equal_levels(unpickled)

    def test_from_tuples_index_values(self):
        result = MultiIndex.from_tuples(self.index)
        assert (result.values == self.index.values).all()

    def test_contains(self):
        assert ('foo', 'two') in self.index
        assert ('bar', 'two') not in self.index
        assert None not in self.index

    def test_contains_top_level(self):
        midx = MultiIndex.from_product([['A', 'B'], [1, 2]])
        assert 'A' in midx
        assert 'A' not in midx._engine

    def test_contains_with_nat(self):
        # MI with a NaT
        mi = MultiIndex(levels=[['C'],
                                pd.date_range('2012-01-01', periods=5)],
                        labels=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
                        names=[None, 'B'])
        assert ('C', pd.Timestamp('2012-01-01')) in mi
        for val in mi.values:
            assert val in mi

    def test_is_all_dates(self):
        assert not self.index.is_all_dates

    def test_is_numeric(self):
        # MultiIndex is never numeric
        assert not self.index.is_numeric()

    def test_getitem(self):
        # scalar
        assert self.index[2] == ('bar', 'one')

        # slice
        result = self.index[2:5]
        expected = self.index[[2, 3, 4]]
        assert result.equals(expected)

        # boolean
        result = self.index[[True, False, True, False, True, True]]
        result2 = self.index[np.array([True, False, True, False, True, True])]
        expected = self.index[[0, 2, 4, 5]]
        assert result.equals(expected)
        assert result2.equals(expected)

    def test_getitem_group_select(self):
        sorted_idx, _ = self.index.sortlevel(0)
        assert sorted_idx.get_loc('baz') == slice(3, 4)
        assert sorted_idx.get_loc('foo') == slice(0, 2)

    def test_get_loc(self):
        assert self.index.get_loc(('foo', 'two')) == 1
        assert self.index.get_loc(('baz', 'two')) == 3
        pytest.raises(KeyError, self.index.get_loc, ('bar', 'two'))
        pytest.raises(KeyError, self.index.get_loc, 'quux')

        pytest.raises(NotImplementedError, self.index.get_loc, 'foo',
                      method='nearest')

        # 3 levels
        index = MultiIndex(levels=[Index(lrange(4)), Index(lrange(4)), Index(
            lrange(4))], labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
                [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])])
        pytest.raises(KeyError, index.get_loc, (1, 1))
        assert index.get_loc((2, 0)) == slice(3, 5)

    def test_get_loc_duplicates(self):
        index = Index([2, 2, 2, 2])
        result = index.get_loc(2)
        expected = slice(0, 4)
        assert result == expected
        # pytest.raises(Exception, index.get_loc, 2)

        index = Index(['c', 'a', 'a', 'b', 'b'])
        rs = index.get_loc('c')
        xp = 0
        assert rs == xp

    def test_get_value_duplicates(self):
        index = MultiIndex(levels=[['D', 'B', 'C'],
                                   [0, 26, 27, 37, 57, 67, 75, 82]],
                           labels=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2],
                                   [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]],
                           names=['tag', 'day'])

        assert index.get_loc('D') == slice(0, 3)
        with pytest.raises(KeyError):
            index._engine.get_value(np.array([]), 'D')

    def test_get_loc_level(self):
        index = MultiIndex(levels=[Index(lrange(4)), Index(lrange(4)), Index(
            lrange(4))], labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
                [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])])

        loc, new_index = index.get_loc_level((0, 1))
        expected = slice(1, 2)
        exp_index = index[expected].droplevel(0).droplevel(0)
        assert loc == expected
        assert new_index.equals(exp_index)

        loc, new_index = index.get_loc_level((0, 1, 0))
        expected = 1
        assert loc == expected
        assert new_index is None

        pytest.raises(KeyError, index.get_loc_level, (2, 2))

        index = MultiIndex(levels=[[2000], lrange(4)], labels=[np.array(
            [0, 0, 0, 0]), np.array([0, 1, 2, 3])])
        result, new_index = index.get_loc_level((2000, slice(None, None)))
        expected = slice(None, None)
        assert result == expected
        assert new_index.equals(index.droplevel(0))

    @pytest.mark.parametrize('level', [0, 1])
    @pytest.mark.parametrize('null_val', [np.nan, pd.NaT, None])
    def test_get_loc_nan(self, level, null_val):
        # GH 18485 : NaN in MultiIndex
        levels = [['a', 'b'], ['c', 'd']]
        key = ['b', 'd']
        levels[level] = np.array([0, null_val], dtype=type(null_val))
        key[level] = null_val
        idx = MultiIndex.from_product(levels)
        assert idx.get_loc(tuple(key)) == 3

    def test_get_loc_missing_nan(self):
        # GH 8569
        idx = MultiIndex.from_arrays([[1.0, 2.0], [3.0, 4.0]])
        assert isinstance(idx.get_loc(1), slice)
        pytest.raises(KeyError, idx.get_loc, 3)
        pytest.raises(KeyError, idx.get_loc, np.nan)
        pytest.raises(KeyError, idx.get_loc, [np.nan])

    @pytest.mark.parametrize('dtype1', [int, float, bool, str])
    @pytest.mark.parametrize('dtype2', [int, float, bool, str])
    def test_get_loc_multiple_dtypes(self, dtype1, dtype2):
        # GH 18520
        levels = [np.array([0, 1]).astype(dtype1),
                  np.array([0, 1]).astype(dtype2)]
        idx = pd.MultiIndex.from_product(levels)
        assert idx.get_loc(idx[2]) == 2

    @pytest.mark.parametrize('level', [0, 1])
    @pytest.mark.parametrize('dtypes', [[int, float], [float, int]])
    def test_get_loc_implicit_cast(self, level, dtypes):
        # GH 18818, GH 15994 : as flat index, cast int to float and vice-versa
        levels = [['a', 'b'], ['c', 'd']]
        key = ['b', 'd']
        lev_dtype, key_dtype = dtypes
        levels[level] = np.array([0, 1], dtype=lev_dtype)
        key[level] = key_dtype(1)
        idx = MultiIndex.from_product(levels)
        assert idx.get_loc(tuple(key)) == 3

    def test_get_loc_cast_bool(self):
        # GH 19086 : int is casted to bool, but not vice-versa
        levels = [[False, True], np.arange(2, dtype='int64')]
        idx = MultiIndex.from_product(levels)

        assert idx.get_loc((0, 1)) == 1
        assert idx.get_loc((1, 0)) == 2

        pytest.raises(KeyError, idx.get_loc, (False, True))
        pytest.raises(KeyError, idx.get_loc, (True, False))

    def test_slice_locs(self):
        df = tm.makeTimeDataFrame()
        stacked = df.stack()
        idx = stacked.index

        slob = slice(*idx.slice_locs(df.index[5], df.index[15]))
        sliced = stacked[slob]
        expected = df[5:16].stack()
        tm.assert_almost_equal(sliced.values, expected.values)

        slob = slice(*idx.slice_locs(df.index[5] + timedelta(seconds=30),
                                     df.index[15] - timedelta(seconds=30)))
        sliced = stacked[slob]
        expected = df[6:15].stack()
        tm.assert_almost_equal(sliced.values, expected.values)

    def test_slice_locs_with_type_mismatch(self):
        df = tm.makeTimeDataFrame()
        stacked = df.stack()
        idx = stacked.index
        tm.assert_raises_regex(TypeError, '^Level type mismatch',
                               idx.slice_locs, (1, 3))
        tm.assert_raises_regex(TypeError, '^Level type mismatch',
                               idx.slice_locs,
                               df.index[5] + timedelta(
                                   seconds=30), (5, 2))
        df = tm.makeCustomDataframe(5, 5)
        stacked = df.stack()
        idx = stacked.index
        with tm.assert_raises_regex(TypeError, '^Level type mismatch'):
            idx.slice_locs(timedelta(seconds=30))
        # TODO: Try creating a UnicodeDecodeError in exception message
        with tm.assert_raises_regex(TypeError, '^Level type mismatch'):
            idx.slice_locs(df.index[1], (16, "a"))

    def test_slice_locs_not_sorted(self):
        index = MultiIndex(levels=[Index(lrange(4)), Index(lrange(4)), Index(
            lrange(4))], labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
                [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])])

        tm.assert_raises_regex(KeyError, "[Kk]ey length.*greater than "
                                         "MultiIndex lexsort depth",
                               index.slice_locs, (1, 0, 1), (2, 1, 0))

        # works
        sorted_index, _ = index.sortlevel(0)
        # should there be a test case here???
        sorted_index.slice_locs((1, 0, 1), (2, 1, 0))

    def test_slice_locs_partial(self):
        sorted_idx, _ = self.index.sortlevel(0)

        result = sorted_idx.slice_locs(('foo', 'two'), ('qux', 'one'))
        assert result == (1, 5)

        result = sorted_idx.slice_locs(None, ('qux', 'one'))
        assert result == (0, 5)

        result = sorted_idx.slice_locs(('foo', 'two'), None)
        assert result == (1, len(sorted_idx))

        result = sorted_idx.slice_locs('bar', 'baz')
        assert result == (2, 4)

    def test_slice_locs_not_contained(self):
        # some searchsorted action

        index = MultiIndex(levels=[[0, 2, 4, 6], [0, 2, 4]],
                           labels=[[0, 0, 0, 1, 1, 2, 3, 3, 3],
                                   [0, 1, 2, 1, 2, 2, 0, 1, 2]], sortorder=0)

        result = index.slice_locs((1, 0), (5, 2))
        assert result == (3, 6)

        result = index.slice_locs(1, 5)
        assert result == (3, 6)

        result = index.slice_locs((2, 2), (5, 2))
        assert result == (3, 6)

        result = index.slice_locs(2, 5)
        assert result == (3, 6)

        result = index.slice_locs((1, 0), (6, 3))
        assert result == (3, 8)

        result = index.slice_locs(-1, 10)
        assert result == (0, len(index))

    def test_consistency(self):
        # need to construct an overflow
        major_axis = lrange(70000)
        minor_axis = lrange(10)

        major_labels = np.arange(70000)
        minor_labels = np.repeat(lrange(10), 7000)

        # the fact that is works means it's consistent
        index = MultiIndex(levels=[major_axis, minor_axis],
                           labels=[major_labels, minor_labels])

        # inconsistent
        major_labels = np.array([0, 0, 1, 1, 1, 2, 2, 3, 3])
        minor_labels = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1])
        index = MultiIndex(levels=[major_axis, minor_axis],
                           labels=[major_labels, minor_labels])

        assert not index.is_unique

    def test_truncate(self):
        major_axis = Index(lrange(4))
        minor_axis = Index(lrange(2))

        major_labels = np.array([0, 0, 1, 2, 3, 3])
        minor_labels = np.array([0, 1, 0, 1, 0, 1])

        index = MultiIndex(levels=[major_axis, minor_axis],
                           labels=[major_labels, minor_labels])

        result = index.truncate(before=1)
        assert 'foo' not in result.levels[0]
        assert 1 in result.levels[0]

        result = index.truncate(after=1)
        assert 2 not in result.levels[0]
        assert 1 in result.levels[0]

        result = index.truncate(before=1, after=2)
        assert len(result.levels[0]) == 2

        # after < before
        pytest.raises(ValueError, index.truncate, 3, 1)

    def test_get_indexer(self):
        major_axis = Index(lrange(4))
        minor_axis = Index(lrange(2))

        major_labels = np.array([0, 0, 1, 2, 2, 3, 3], dtype=np.intp)
        minor_labels = np.array([0, 1, 0, 0, 1, 0, 1], dtype=np.intp)

        index = MultiIndex(levels=[major_axis, minor_axis],
                           labels=[major_labels, minor_labels])
        idx1 = index[:5]
        idx2 = index[[1, 3, 5]]

        r1 = idx1.get_indexer(idx2)
        assert_almost_equal(r1, np.array([1, 3, -1], dtype=np.intp))

        r1 = idx2.get_indexer(idx1, method='pad')
        e1 = np.array([-1, 0, 0, 1, 1], dtype=np.intp)
        assert_almost_equal(r1, e1)

        r2 = idx2.get_indexer(idx1[::-1], method='pad')
        assert_almost_equal(r2, e1[::-1])

        rffill1 = idx2.get_indexer(idx1, method='ffill')
        assert_almost_equal(r1, rffill1)

        r1 = idx2.get_indexer(idx1, method='backfill')
        e1 = np.array([0, 0, 1, 1, 2], dtype=np.intp)
        assert_almost_equal(r1, e1)

        r2 = idx2.get_indexer(idx1[::-1], method='backfill')
        assert_almost_equal(r2, e1[::-1])

        rbfill1 = idx2.get_indexer(idx1, method='bfill')
        assert_almost_equal(r1, rbfill1)

        # pass non-MultiIndex
        r1 = idx1.get_indexer(idx2.values)
        rexp1 = idx1.get_indexer(idx2)
        assert_almost_equal(r1, rexp1)

        r1 = idx1.get_indexer([1, 2, 3])
        assert (r1 == [-1, -1, -1]).all()

        # create index with duplicates
        idx1 = Index(lrange(10) + lrange(10))
        idx2 = Index(lrange(20))

        msg = "Reindexing only valid with uniquely valued Index objects"
        with tm.assert_raises_regex(InvalidIndexError, msg):
            idx1.get_indexer(idx2)

    def test_get_indexer_nearest(self):
        midx = MultiIndex.from_tuples([('a', 1), ('b', 2)])
        with pytest.raises(NotImplementedError):
            midx.get_indexer(['a'], method='nearest')
        with pytest.raises(NotImplementedError):
            midx.get_indexer(['a'], method='pad', tolerance=2)

    def test_hash_collisions(self):
        # non-smoke test that we don't get hash collisions

        index = MultiIndex.from_product([np.arange(1000), np.arange(1000)],
                                        names=['one', 'two'])
        result = index.get_indexer(index.values)
        tm.assert_numpy_array_equal(result, np.arange(
            len(index), dtype='intp'))

        for i in [0, 1, len(index) - 2, len(index) - 1]:
            result = index.get_loc(index[i])
            assert result == i

    def test_format(self):
        self.index.format()
        self.index[:0].format()

    def test_format_integer_names(self):
        index = MultiIndex(levels=[[0, 1], [0, 1]],
                           labels=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[0, 1])
        index.format(names=True)

    def test_format_sparse_display(self):
        index = MultiIndex(levels=[[0, 1], [0, 1], [0, 1], [0]],
                           labels=[[0, 0, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1],
                                   [0, 1, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0]])

        result = index.format()
        assert result[3] == '1  0  0  0'

    def test_format_sparse_config(self):
        warn_filters = warnings.filters
        warnings.filterwarnings('ignore', category=FutureWarning,
                                module=".*format")
        # GH1538
        pd.set_option('display.multi_sparse', False)

        result = self.index.format()
        assert result[1] == 'foo  two'

        tm.reset_display_options()

        warnings.filters = warn_filters

    def test_to_frame(self):
        tuples = [(1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')]

        index = MultiIndex.from_tuples(tuples)
        result = index.to_frame(index=False)
        expected = DataFrame(tuples)
        tm.assert_frame_equal(result, expected)

        result = index.to_frame()
        expected.index = index
        tm.assert_frame_equal(result, expected)

        tuples = [(1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')]
        index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
        result = index.to_frame(index=False)
        expected = DataFrame(tuples)
        expected.columns = ['first', 'second']
        tm.assert_frame_equal(result, expected)

        result = index.to_frame()
        expected.index = index
        tm.assert_frame_equal(result, expected)

        index = MultiIndex.from_product([range(5),
                                         pd.date_range('20130101', periods=3)])
        result = index.to_frame(index=False)
        expected = DataFrame(
            {0: np.repeat(np.arange(5, dtype='int64'), 3),
             1: np.tile(pd.date_range('20130101', periods=3), 5)})
        tm.assert_frame_equal(result, expected)

        index = MultiIndex.from_product([range(5),
                                         pd.date_range('20130101', periods=3)])
        result = index.to_frame()
        expected.index = index
        tm.assert_frame_equal(result, expected)

    def test_to_hierarchical(self):
        index = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'), (
            2, 'two')])
        result = index.to_hierarchical(3)
        expected = MultiIndex(levels=[[1, 2], ['one', 'two']],
                              labels=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
                                      [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1]])
        tm.assert_index_equal(result, expected)
        assert result.names == index.names

        # K > 1
        result = index.to_hierarchical(3, 2)
        expected = MultiIndex(levels=[[1, 2], ['one', 'two']],
                              labels=[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],
                                      [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]])
        tm.assert_index_equal(result, expected)
        assert result.names == index.names

        # non-sorted
        index = MultiIndex.from_tuples([(2, 'c'), (1, 'b'),
                                        (2, 'a'), (2, 'b')],
                                       names=['N1', 'N2'])

        result = index.to_hierarchical(2)
        expected = MultiIndex.from_tuples([(2, 'c'), (2, 'c'), (1, 'b'),
                                           (1, 'b'),
                                           (2, 'a'), (2, 'a'),
                                           (2, 'b'), (2, 'b')],
                                          names=['N1', 'N2'])
        tm.assert_index_equal(result, expected)
        assert result.names == index.names

    def test_bounds(self):
        self.index._bounds

    def test_equals_multi(self):
        assert self.index.equals(self.index)
        assert not self.index.equals(self.index.values)
        assert self.index.equals(Index(self.index.values))

        assert self.index.equal_levels(self.index)
        assert not self.index.equals(self.index[:-1])
        assert not self.index.equals(self.index[-1])

        # different number of levels
        index = MultiIndex(levels=[Index(lrange(4)), Index(lrange(4)), Index(
            lrange(4))], labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
                [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])])

        index2 = MultiIndex(levels=index.levels[:-1], labels=index.labels[:-1])
        assert not index.equals(index2)
        assert not index.equal_levels(index2)

        # levels are different
        major_axis = Index(lrange(4))
        minor_axis = Index(lrange(2))

        major_labels = np.array([0, 0, 1, 2, 2, 3])
        minor_labels = np.array([0, 1, 0, 0, 1, 0])

        index = MultiIndex(levels=[major_axis, minor_axis],
                           labels=[major_labels, minor_labels])
        assert not self.index.equals(index)
        assert not self.index.equal_levels(index)

        # some of the labels are different
        major_axis = Index(['foo', 'bar', 'baz', 'qux'])
        minor_axis = Index(['one', 'two'])

        major_labels = np.array([0, 0, 2, 2, 3, 3])
        minor_labels = np.array([0, 1, 0, 1, 0, 1])

        index = MultiIndex(levels=[major_axis, minor_axis],
                           labels=[major_labels, minor_labels])
        assert not self.index.equals(index)

    def test_equals_missing_values(self):
        # make sure take is not using -1
        i = pd.MultiIndex.from_tuples([(0, pd.NaT),
                                       (0, pd.Timestamp('20130101'))])
        result = i[0:1].equals(i[0])
        assert not result
        result = i[1:2].equals(i[1])
        assert not result

    def test_identical(self):
        mi = self.index.copy()
        mi2 = self.index.copy()
        assert mi.identical(mi2)

        mi = mi.set_names(['new1', 'new2'])
        assert mi.equals(mi2)
        assert not mi.identical(mi2)

        mi2 = mi2.set_names(['new1', 'new2'])
        assert mi.identical(mi2)

        mi3 = Index(mi.tolist(), names=mi.names)
        mi4 = Index(mi.tolist(), names=mi.names, tupleize_cols=False)
        assert mi.identical(mi3)
        assert not mi.identical(mi4)
        assert mi.equals(mi4)

    def test_is_(self):
        mi = MultiIndex.from_tuples(lzip(range(10), range(10)))
        assert mi.is_(mi)
        assert mi.is_(mi.view())
        assert mi.is_(mi.view().view().view().view())
        mi2 = mi.view()
        # names are metadata, they don't change id
        mi2.names = ["A", "B"]
        assert mi2.is_(mi)
        assert mi.is_(mi2)

        assert mi.is_(mi.set_names(["C", "D"]))
        mi2 = mi.view()
        mi2.set_names(["E", "F"], inplace=True)
        assert mi.is_(mi2)
        # levels are inherent properties, they change identity
        mi3 = mi2.set_levels([lrange(10), lrange(10)])
        assert not mi3.is_(mi2)
        # shouldn't change
        assert mi2.is_(mi)
        mi4 = mi3.view()

        # GH 17464 - Remove duplicate MultiIndex levels
        mi4.set_levels([lrange(10), lrange(10)], inplace=True)
        assert not mi4.is_(mi3)
        mi5 = mi.view()
        mi5.set_levels(mi5.levels, inplace=True)
        assert not mi5.is_(mi)

    def test_union(self):
        piece1 = self.index[:5][::-1]
        piece2 = self.index[3:]

        the_union = piece1 | piece2

        tups = sorted(self.index.values)
        expected = MultiIndex.from_tuples(tups)

        assert the_union.equals(expected)

        # corner case, pass self or empty thing:
        the_union = self.index.union(self.index)
        assert the_union is self.index

        the_union = self.index.union(self.index[:0])
        assert the_union is self.index

        # won't work in python 3
        # tuples = self.index.values
        # result = self.index[:4] | tuples[4:]
        # assert result.equals(tuples)

        # not valid for python 3
        # def test_union_with_regular_index(self):
        #     other = Index(['A', 'B', 'C'])

        #     result = other.union(self.index)
        #     assert ('foo', 'one') in result
        #     assert 'B' in result

        #     result2 = self.index.union(other)
        #     assert result.equals(result2)

    def test_intersection(self):
        piece1 = self.index[:5][::-1]
        piece2 = self.index[3:]

        the_int = piece1 & piece2
        tups = sorted(self.index[3:5].values)
        expected = MultiIndex.from_tuples(tups)
        assert the_int.equals(expected)

        # corner case, pass self
        the_int = self.index.intersection(self.index)
        assert the_int is self.index

        # empty intersection: disjoint
        empty = self.index[:2] & self.index[2:]
        expected = self.index[:0]
        assert empty.equals(expected)

        # can't do in python 3
        # tuples = self.index.values
        # result = self.index & tuples
        # assert result.equals(tuples)

    def test_sub(self):

        first = self.index

        # - now raises (previously was set op difference)
        with pytest.raises(TypeError):
            first - self.index[-3:]
        with pytest.raises(TypeError):
            self.index[-3:] - first
        with pytest.raises(TypeError):
            self.index[-3:] - first.tolist()
        with pytest.raises(TypeError):
            first.tolist() - self.index[-3:]

    def test_difference(self):

        first = self.index
        result = first.difference(self.index[-3:])
        expected = MultiIndex.from_tuples(sorted(self.index[:-3].values),
                                          sortorder=0,
                                          names=self.index.names)

        assert isinstance(result, MultiIndex)
        assert result.equals(expected)
        assert result.names == self.index.names

        # empty difference: reflexive
        result = self.index.difference(self.index)
        expected = self.index[:0]
        assert result.equals(expected)
        assert result.names == self.index.names

        # empty difference: superset
        result = self.index[-3:].difference(self.index)
        expected = self.index[:0]
        assert result.equals(expected)
        assert result.names == self.index.names

        # empty difference: degenerate
        result = self.index[:0].difference(self.index)
        expected = self.index[:0]
        assert result.equals(expected)
        assert result.names == self.index.names

        # names not the same
        chunklet = self.index[-3:]
        chunklet.names = ['foo', 'baz']
        result = first.difference(chunklet)
        assert result.names == (None, None)

        # empty, but non-equal
        result = self.index.difference(self.index.sortlevel(1)[0])
        assert len(result) == 0

        # raise Exception called with non-MultiIndex
        result = first.difference(first.values)
        assert result.equals(first[:0])

        # name from empty array
        result = first.difference([])
        assert first.equals(result)
        assert first.names == result.names

        # name from non-empty array
        result = first.difference([('foo', 'one')])
        expected = pd.MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'), (
            'foo', 'two'), ('qux', 'one'), ('qux', 'two')])
        expected.names = first.names
        assert first.names == result.names
        tm.assert_raises_regex(TypeError, "other must be a MultiIndex "
                                          "or a list of tuples",
                               first.difference, [1, 2, 3, 4, 5])

    def test_from_tuples(self):
        tm.assert_raises_regex(TypeError, 'Cannot infer number of levels '
                                          'from empty list',
                               MultiIndex.from_tuples, [])

        expected = MultiIndex(levels=[[1, 3], [2, 4]],
                              labels=[[0, 1], [0, 1]],
                              names=['a', 'b'])

        # input tuples
        result = MultiIndex.from_tuples(((1, 2), (3, 4)), names=['a', 'b'])
        tm.assert_index_equal(result, expected)

    def test_from_tuples_iterator(self):
        # GH 18434
        # input iterator for tuples
        expected = MultiIndex(levels=[[1, 3], [2, 4]],
                              labels=[[0, 1], [0, 1]],
                              names=['a', 'b'])

        result = MultiIndex.from_tuples(zip([1, 3], [2, 4]), names=['a', 'b'])
        tm.assert_index_equal(result, expected)

        # input non-iterables
        with tm.assert_raises_regex(
                TypeError, 'Input must be a list / sequence of tuple-likes.'):
            MultiIndex.from_tuples(0)

    def test_from_tuples_empty(self):
        # GH 16777
        result = MultiIndex.from_tuples([], names=['a', 'b'])
        expected = MultiIndex.from_arrays(arrays=[[], []],
                                          names=['a', 'b'])
        tm.assert_index_equal(result, expected)

    def test_argsort(self):
        result = self.index.argsort()
        expected = self.index.values.argsort()
        tm.assert_numpy_array_equal(result, expected)

    def test_sortlevel(self):
        import random

        tuples = list(self.index)
        random.shuffle(tuples)

        index = MultiIndex.from_tuples(tuples)

        sorted_idx, _ = index.sortlevel(0)
        expected = MultiIndex.from_tuples(sorted(tuples))
        assert sorted_idx.equals(expected)

        sorted_idx, _ = index.sortlevel(0, ascending=False)
        assert sorted_idx.equals(expected[::-1])

        sorted_idx, _ = index.sortlevel(1)
        by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
        expected = MultiIndex.from_tuples(by1)
        assert sorted_idx.equals(expected)

        sorted_idx, _ = index.sortlevel(1, ascending=False)
        assert sorted_idx.equals(expected[::-1])

    def test_sortlevel_not_sort_remaining(self):
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        sorted_idx, _ = mi.sortlevel('A', sort_remaining=False)
        assert sorted_idx.equals(mi)

    def test_sortlevel_deterministic(self):
        tuples = [('bar', 'one'), ('foo', 'two'), ('qux', 'two'),
                  ('foo', 'one'), ('baz', 'two'), ('qux', 'one')]

        index = MultiIndex.from_tuples(tuples)

        sorted_idx, _ = index.sortlevel(0)
        expected = MultiIndex.from_tuples(sorted(tuples))
        assert sorted_idx.equals(expected)

        sorted_idx, _ = index.sortlevel(0, ascending=False)
        assert sorted_idx.equals(expected[::-1])

        sorted_idx, _ = index.sortlevel(1)
        by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
        expected = MultiIndex.from_tuples(by1)
        assert sorted_idx.equals(expected)

        sorted_idx, _ = index.sortlevel(1, ascending=False)
        assert sorted_idx.equals(expected[::-1])

    def test_dims(self):
        pass

    def test_drop(self):
        dropped = self.index.drop([('foo', 'two'), ('qux', 'one')])

        index = MultiIndex.from_tuples([('foo', 'two'), ('qux', 'one')])
        dropped2 = self.index.drop(index)

        expected = self.index[[0, 2, 3, 5]]
        tm.assert_index_equal(dropped, expected)
        tm.assert_index_equal(dropped2, expected)

        dropped = self.index.drop(['bar'])
        expected = self.index[[0, 1, 3, 4, 5]]
        tm.assert_index_equal(dropped, expected)

        dropped = self.index.drop('foo')
        expected = self.index[[2, 3, 4, 5]]
        tm.assert_index_equal(dropped, expected)

        index = MultiIndex.from_tuples([('bar', 'two')])
        pytest.raises(KeyError, self.index.drop, [('bar', 'two')])
        pytest.raises(KeyError, self.index.drop, index)
        pytest.raises(KeyError, self.index.drop, ['foo', 'two'])

        # partially correct argument
        mixed_index = MultiIndex.from_tuples([('qux', 'one'), ('bar', 'two')])
        pytest.raises(KeyError, self.index.drop, mixed_index)

        # error='ignore'
        dropped = self.index.drop(index, errors='ignore')
        expected = self.index[[0, 1, 2, 3, 4, 5]]
        tm.assert_index_equal(dropped, expected)

        dropped = self.index.drop(mixed_index, errors='ignore')
        expected = self.index[[0, 1, 2, 3, 5]]
        tm.assert_index_equal(dropped, expected)

        dropped = self.index.drop(['foo', 'two'], errors='ignore')
        expected = self.index[[2, 3, 4, 5]]
        tm.assert_index_equal(dropped, expected)

        # mixed partial / full drop
        dropped = self.index.drop(['foo', ('qux', 'one')])
        expected = self.index[[2, 3, 5]]
        tm.assert_index_equal(dropped, expected)

        # mixed partial / full drop / error='ignore'
        mixed_index = ['foo', ('qux', 'one'), 'two']
        pytest.raises(KeyError, self.index.drop, mixed_index)
        dropped = self.index.drop(mixed_index, errors='ignore')
        expected = self.index[[2, 3, 5]]
        tm.assert_index_equal(dropped, expected)

    def test_droplevel_with_names(self):
        index = self.index[self.index.get_loc('foo')]
        dropped = index.droplevel(0)
        assert dropped.name == 'second'

        index = MultiIndex(
            levels=[Index(lrange(4)), Index(lrange(4)), Index(lrange(4))],
            labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
                [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])],
            names=['one', 'two', 'three'])
        dropped = index.droplevel(0)
        assert dropped.names == ('two', 'three')

        dropped = index.droplevel('two')
        expected = index.droplevel(1)
        assert dropped.equals(expected)

    def test_droplevel_multiple(self):
        index = MultiIndex(
            levels=[Index(lrange(4)), Index(lrange(4)), Index(lrange(4))],
            labels=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
                [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])],
            names=['one', 'two', 'three'])

        dropped = index[:2].droplevel(['three', 'one'])
        expected = index[:2].droplevel(2).droplevel(0)
        assert dropped.equals(expected)

    def test_drop_not_lexsorted(self):
        # GH 12078

        # define the lexsorted version of the multi-index
        tuples = [('a', ''), ('b1', 'c1'), ('b2', 'c2')]
        lexsorted_mi = MultiIndex.from_tuples(tuples, names=['b', 'c'])
        assert lexsorted_mi.is_lexsorted()

        # and the not-lexsorted version
        df = pd.DataFrame(columns=['a', 'b', 'c', 'd'],
                          data=[[1, 'b1', 'c1', 3], [1, 'b2', 'c2', 4]])
        df = df.pivot_table(index='a', columns=['b', 'c'], values='d')
        df = df.reset_index()
        not_lexsorted_mi = df.columns
        assert not not_lexsorted_mi.is_lexsorted()

        # compare the results
        tm.assert_index_equal(lexsorted_mi, not_lexsorted_mi)
        with tm.assert_produces_warning(PerformanceWarning):
            tm.assert_index_equal(lexsorted_mi.drop('a'),
                                  not_lexsorted_mi.drop('a'))

    def test_insert(self):
        # key contained in all levels
        new_index = self.index.insert(0, ('bar', 'two'))
        assert new_index.equal_levels(self.index)
        assert new_index[0] == ('bar', 'two')

        # key not contained in all levels
        new_index = self.index.insert(0, ('abc', 'three'))

        exp0 = Index(list(self.index.levels[0]) + ['abc'], name='first')
        tm.assert_index_equal(new_index.levels[0], exp0)

        exp1 = Index(list(self.index.levels[1]) + ['three'], name='second')
        tm.assert_index_equal(new_index.levels[1], exp1)
        assert new_index[0] == ('abc', 'three')

        # key wrong length
        msg = "Item must have length equal to number of levels"
        with tm.assert_raises_regex(ValueError, msg):
            self.index.insert(0, ('foo2',))

        left = pd.DataFrame([['a', 'b', 0], ['b', 'd', 1]],
                            columns=['1st', '2nd', '3rd'])
        left.set_index(['1st', '2nd'], inplace=True)
        ts = left['3rd'].copy(deep=True)

        left.loc[('b', 'x'), '3rd'] = 2
        left.loc[('b', 'a'), '3rd'] = -1
        left.loc[('b', 'b'), '3rd'] = 3
        left.loc[('a', 'x'), '3rd'] = 4
        left.loc[('a', 'w'), '3rd'] = 5
        left.loc[('a', 'a'), '3rd'] = 6

        ts.loc[('b', 'x')] = 2
        ts.loc['b', 'a'] = -1
        ts.loc[('b', 'b')] = 3
        ts.loc['a', 'x'] = 4
        ts.loc[('a', 'w')] = 5
        ts.loc['a', 'a'] = 6

        right = pd.DataFrame([['a', 'b', 0], ['b', 'd', 1], ['b', 'x', 2],
                              ['b', 'a', -1], ['b', 'b', 3], ['a', 'x', 4],
                              ['a', 'w', 5], ['a', 'a', 6]],
                             columns=['1st', '2nd', '3rd'])
        right.set_index(['1st', '2nd'], inplace=True)
        # FIXME data types changes to float because
        # of intermediate nan insertion;
        tm.assert_frame_equal(left, right, check_dtype=False)
        tm.assert_series_equal(ts, right['3rd'])

        # GH9250
        idx = [('test1', i) for i in range(5)] + \
              [('test2', i) for i in range(6)] + \
              [('test', 17), ('test', 18)]

        left = pd.Series(np.linspace(0, 10, 11),
                         pd.MultiIndex.from_tuples(idx[:-2]))

        left.loc[('test', 17)] = 11
        left.loc[('test', 18)] = 12

        right = pd.Series(np.linspace(0, 12, 13),
                          pd.MultiIndex.from_tuples(idx))

        tm.assert_series_equal(left, right)

    def test_take_preserve_name(self):
        taken = self.index.take([3, 0, 1])
        assert taken.names == self.index.names

    def test_take_fill_value(self):
        # GH 12631
        vals = [['A', 'B'],
                [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]]
        idx = pd.MultiIndex.from_product(vals, names=['str', 'dt'])

        result = idx.take(np.array([1, 0, -1]))
        exp_vals = [('A', pd.Timestamp('2011-01-02')),
                    ('A', pd.Timestamp('2011-01-01')),
                    ('B', pd.Timestamp('2011-01-02'))]
        expected = pd.MultiIndex.from_tuples(exp_vals, names=['str', 'dt'])
        tm.assert_index_equal(result, expected)

        # fill_value
        result = idx.take(np.array([1, 0, -1]), fill_value=True)
        exp_vals = [('A', pd.Timestamp('2011-01-02')),
                    ('A', pd.Timestamp('2011-01-01')),
                    (np.nan, pd.NaT)]
        expected = pd.MultiIndex.from_tuples(exp_vals, names=['str', 'dt'])
        tm.assert_index_equal(result, expected)

        # allow_fill=False
        result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                          fill_value=True)
        exp_vals = [('A', pd.Timestamp('2011-01-02')),
                    ('A', pd.Timestamp('2011-01-01')),
                    ('B', pd.Timestamp('2011-01-02'))]
        expected = pd.MultiIndex.from_tuples(exp_vals, names=['str', 'dt'])
        tm.assert_index_equal(result, expected)

        msg = ('When allow_fill=True and fill_value is not None, '
               'all indices must be >= -1')
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -2]), fill_value=True)
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -5]), fill_value=True)

        with pytest.raises(IndexError):
            idx.take(np.array([1, -5]))

    def take_invalid_kwargs(self):
        vals = [['A', 'B'],
                [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]]
        idx = pd.MultiIndex.from_product(vals, names=['str', 'dt'])
        indices = [1, 2]

        msg = r"take\(\) got an unexpected keyword argument 'foo'"
        tm.assert_raises_regex(TypeError, msg, idx.take,
                               indices, foo=2)

        msg = "the 'out' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, idx.take,
                               indices, out=indices)

        msg = "the 'mode' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, idx.take,
                               indices, mode='clip')

    @pytest.mark.parametrize('other',
                             [Index(['three', 'one', 'two']),
                              Index(['one']),
                              Index(['one', 'three'])])
    def test_join_level(self, other, join_type):
        join_index, lidx, ridx = other.join(self.index, how=join_type,
                                            level='second',
                                            return_indexers=True)

        exp_level = other.join(self.index.levels[1], how=join_type)
        assert join_index.levels[0].equals(self.index.levels[0])
        assert join_index.levels[1].equals(exp_level)

        # pare down levels
        mask = np.array(
            [x[1] in exp_level for x in self.index], dtype=bool)
        exp_values = self.index.values[mask]
        tm.assert_numpy_array_equal(join_index.values, exp_values)

        if join_type in ('outer', 'inner'):
            join_index2, ridx2, lidx2 = \
                self.index.join(other, how=join_type, level='second',
                                return_indexers=True)

            assert join_index.equals(join_index2)
            tm.assert_numpy_array_equal(lidx, lidx2)
            tm.assert_numpy_array_equal(ridx, ridx2)
            tm.assert_numpy_array_equal(join_index2.values, exp_values)

    def test_join_level_corner_case(self):
        # some corner cases
        idx = Index(['three', 'one', 'two'])
        result = idx.join(self.index, level='second')
        assert isinstance(result, MultiIndex)

        tm.assert_raises_regex(TypeError, "Join.*MultiIndex.*ambiguous",
                               self.index.join, self.index, level=1)

    def test_join_self(self, join_type):
        res = self.index
        joined = res.join(res, how=join_type)
        assert res is joined

    def test_join_multi(self):
        # GH 10665
        midx = pd.MultiIndex.from_product(
            [np.arange(4), np.arange(4)], names=['a', 'b'])
        idx = pd.Index([1, 2, 5], name='b')

        # inner
        jidx, lidx, ridx = midx.join(idx, how='inner', return_indexers=True)
        exp_idx = pd.MultiIndex.from_product(
            [np.arange(4), [1, 2]], names=['a', 'b'])
        exp_lidx = np.array([1, 2, 5, 6, 9, 10, 13, 14], dtype=np.intp)
        exp_ridx = np.array([0, 1, 0, 1, 0, 1, 0, 1], dtype=np.intp)
        tm.assert_index_equal(jidx, exp_idx)
        tm.assert_numpy_array_equal(lidx, exp_lidx)
        tm.assert_numpy_array_equal(ridx, exp_ridx)
        # flip
        jidx, ridx, lidx = idx.join(midx, how='inner', return_indexers=True)
        tm.assert_index_equal(jidx, exp_idx)
        tm.assert_numpy_array_equal(lidx, exp_lidx)
        tm.assert_numpy_array_equal(ridx, exp_ridx)

        # keep MultiIndex
        jidx, lidx, ridx = midx.join(idx, how='left', return_indexers=True)
        exp_ridx = np.array([-1, 0, 1, -1, -1, 0, 1, -1, -1, 0, 1, -1, -1, 0,
                             1, -1], dtype=np.intp)
        tm.assert_index_equal(jidx, midx)
        assert lidx is None
        tm.assert_numpy_array_equal(ridx, exp_ridx)
        # flip
        jidx, ridx, lidx = idx.join(midx, how='right', return_indexers=True)
        tm.assert_index_equal(jidx, midx)
        assert lidx is None
        tm.assert_numpy_array_equal(ridx, exp_ridx)

    def test_reindex(self):
        result, indexer = self.index.reindex(list(self.index[:4]))
        assert isinstance(result, MultiIndex)
        self.check_level_names(result, self.index[:4].names)

        result, indexer = self.index.reindex(list(self.index))
        assert isinstance(result, MultiIndex)
        assert indexer is None
        self.check_level_names(result, self.index.names)

    def test_reindex_level(self):
        idx = Index(['one'])

        target, indexer = self.index.reindex(idx, level='second')
        target2, indexer2 = idx.reindex(self.index, level='second')

        exp_index = self.index.join(idx, level='second', how='right')
        exp_index2 = self.index.join(idx, level='second', how='left')

        assert target.equals(exp_index)
        exp_indexer = np.array([0, 2, 4])
        tm.assert_numpy_array_equal(indexer, exp_indexer, check_dtype=False)

        assert target2.equals(exp_index2)
        exp_indexer2 = np.array([0, -1, 0, -1, 0, -1])
        tm.assert_numpy_array_equal(indexer2, exp_indexer2, check_dtype=False)

        tm.assert_raises_regex(TypeError, "Fill method not supported",
                               self.index.reindex, self.index,
                               method='pad', level='second')

        tm.assert_raises_regex(TypeError, "Fill method not supported",
                               idx.reindex, idx, method='bfill',
                               level='first')

    def test_duplicates(self):
        assert not self.index.has_duplicates
        assert self.index.append(self.index).has_duplicates

        index = MultiIndex(levels=[[0, 1], [0, 1, 2]], labels=[
            [0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]])
        assert index.has_duplicates

        # GH 9075
        t = [(u('x'), u('out'), u('z'), 5, u('y'), u('in'), u('z'), 169),
             (u('x'), u('out'), u('z'), 7, u('y'), u('in'), u('z'), 119),
             (u('x'), u('out'), u('z'), 9, u('y'), u('in'), u('z'), 135),
             (u('x'), u('out'), u('z'), 13, u('y'), u('in'), u('z'), 145),
             (u('x'), u('out'), u('z'), 14, u('y'), u('in'), u('z'), 158),
             (u('x'), u('out'), u('z'), 16, u('y'), u('in'), u('z'), 122),
             (u('x'), u('out'), u('z'), 17, u('y'), u('in'), u('z'), 160),
             (u('x'), u('out'), u('z'), 18, u('y'), u('in'), u('z'), 180),
             (u('x'), u('out'), u('z'), 20, u('y'), u('in'), u('z'), 143),
             (u('x'), u('out'), u('z'), 21, u('y'), u('in'), u('z'), 128),
             (u('x'), u('out'), u('z'), 22, u('y'), u('in'), u('z'), 129),
             (u('x'), u('out'), u('z'), 25, u('y'), u('in'), u('z'), 111),
             (u('x'), u('out'), u('z'), 28, u('y'), u('in'), u('z'), 114),
             (u('x'), u('out'), u('z'), 29, u('y'), u('in'), u('z'), 121),
             (u('x'), u('out'), u('z'), 31, u('y'), u('in'), u('z'), 126),
             (u('x'), u('out'), u('z'), 32, u('y'), u('in'), u('z'), 155),
             (u('x'), u('out'), u('z'), 33, u('y'), u('in'), u('z'), 123),
             (u('x'), u('out'), u('z'), 12, u('y'), u('in'), u('z'), 144)]

        index = pd.MultiIndex.from_tuples(t)
        assert not index.has_duplicates

        # handle int64 overflow if possible
        def check(nlevels, with_nulls):
            labels = np.tile(np.arange(500), 2)
            level = np.arange(500)

            if with_nulls:  # inject some null values
                labels[500] = -1  # common nan value
                labels = [labels.copy() for i in range(nlevels)]
                for i in range(nlevels):
                    labels[i][500 + i - nlevels // 2] = -1

                labels += [np.array([-1, 1]).repeat(500)]
            else:
                labels = [labels] * nlevels + [np.arange(2).repeat(500)]

            levels = [level] * nlevels + [[0, 1]]

            # no dups
            index = MultiIndex(levels=levels, labels=labels)
            assert not index.has_duplicates

            # with a dup
            if with_nulls:
                f = lambda a: np.insert(a, 1000, a[0])
                labels = list(map(f, labels))
                index = MultiIndex(levels=levels, labels=labels)
            else:
                values = index.values.tolist()
                index = MultiIndex.from_tuples(values + [values[0]])

            assert index.has_duplicates

        # no overflow
        check(4, False)
        check(4, True)

        # overflow possible
        check(8, False)
        check(8, True)

        # GH 9125
        n, k = 200, 5000
        levels = [np.arange(n), tm.makeStringIndex(n), 1000 + np.arange(n)]
        labels = [np.random.choice(n, k * n) for lev in levels]
        mi = MultiIndex(levels=levels, labels=labels)

        for keep in ['first', 'last', False]:
            left = mi.duplicated(keep=keep)
            right = pd._libs.hashtable.duplicated_object(mi.values, keep=keep)
            tm.assert_numpy_array_equal(left, right)

        # GH5873
        for a in [101, 102]:
            mi = MultiIndex.from_arrays([[101, a], [3.5, np.nan]])
            assert not mi.has_duplicates
            assert mi.get_duplicates() == []
            tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(
                2, dtype='bool'))

        for n in range(1, 6):  # 1st level shape
            for m in range(1, 5):  # 2nd level shape
                # all possible unique combinations, including nan
                lab = product(range(-1, n), range(-1, m))
                mi = MultiIndex(levels=[list('abcde')[:n], list('WXYZ')[:m]],
                                labels=np.random.permutation(list(lab)).T)
                assert len(mi) == (n + 1) * (m + 1)
                assert not mi.has_duplicates
                assert mi.get_duplicates() == []
                tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(
                    len(mi), dtype='bool'))

    def test_duplicate_meta_data(self):
        # GH 10115
        index = MultiIndex(
            levels=[[0, 1], [0, 1, 2]],
            labels=[[0, 0, 0, 0, 1, 1, 1],
                    [0, 1, 2, 0, 0, 1, 2]])

        for idx in [index,
                    index.set_names([None, None]),
                    index.set_names([None, 'Num']),
                    index.set_names(['Upper', 'Num']), ]:
            assert idx.has_duplicates
            assert idx.drop_duplicates().names == idx.names

    def test_get_unique_index(self):
        idx = self.index[[0, 1, 0, 1, 1, 0, 0]]
        expected = self.index._shallow_copy(idx[[0, 1]])

        for dropna in [False, True]:
            result = idx._get_unique_index(dropna=dropna)
            assert result.unique
            tm.assert_index_equal(result, expected)

    def test_unique(self):
        mi = pd.MultiIndex.from_arrays([[1, 2, 1, 2], [1, 1, 1, 2]])

        res = mi.unique()
        exp = pd.MultiIndex.from_arrays([[1, 2, 2], [1, 1, 2]])
        tm.assert_index_equal(res, exp)

        mi = pd.MultiIndex.from_arrays([list('aaaa'), list('abab')])
        res = mi.unique()
        exp = pd.MultiIndex.from_arrays([list('aa'), list('ab')])
        tm.assert_index_equal(res, exp)

        mi = pd.MultiIndex.from_arrays([list('aaaa'), list('aaaa')])
        res = mi.unique()
        exp = pd.MultiIndex.from_arrays([['a'], ['a']])
        tm.assert_index_equal(res, exp)

    @pytest.mark.parametrize('level', [0, 'first', 1, 'second'])
    def test_unique_level(self, level):
        # GH #17896 - with level= argument
        result = self.index.unique(level=level)
        expected = self.index.get_level_values(level).unique()
        tm.assert_index_equal(result, expected)

        # With already unique level
        mi = pd.MultiIndex.from_arrays([[1, 3, 2, 4], [1, 3, 2, 5]],
                                       names=['first', 'second'])
        result = mi.unique(level=level)
        expected = mi.get_level_values(level)
        tm.assert_index_equal(result, expected)

    def test_unique_datetimelike(self):
        idx1 = pd.DatetimeIndex(['2015-01-01', '2015-01-01', '2015-01-01',
                                 '2015-01-01', 'NaT', 'NaT'])
        idx2 = pd.DatetimeIndex(['2015-01-01', '2015-01-01', '2015-01-02',
                                 '2015-01-02', 'NaT', '2015-01-01'],
                                tz='Asia/Tokyo')
        result = pd.MultiIndex.from_arrays([idx1, idx2]).unique()

        eidx1 = pd.DatetimeIndex(['2015-01-01', '2015-01-01', 'NaT', 'NaT'])
        eidx2 = pd.DatetimeIndex(['2015-01-01', '2015-01-02',
                                  'NaT', '2015-01-01'],
                                 tz='Asia/Tokyo')
        exp = pd.MultiIndex.from_arrays([eidx1, eidx2])
        tm.assert_index_equal(result, exp)

    def test_tolist(self):
        result = self.index.tolist()
        exp = list(self.index.values)
        assert result == exp

    def test_repr_with_unicode_data(self):
        with pd.core.config.option_context("display.encoding", 'UTF-8'):
            d = {"a": [u("\u05d0"), 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
            index = pd.DataFrame(d).set_index(["a", "b"]).index
            assert "\\u" not in repr(index)  # we don't want unicode-escaped

    def test_repr_roundtrip(self):

        mi = MultiIndex.from_product([list('ab'), range(3)],
                                     names=['first', 'second'])
        str(mi)

        if PY3:
            tm.assert_index_equal(eval(repr(mi)), mi, exact=True)
        else:
            result = eval(repr(mi))
            # string coerces to unicode
            tm.assert_index_equal(result, mi, exact=False)
            assert mi.get_level_values('first').inferred_type == 'string'
            assert result.get_level_values('first').inferred_type == 'unicode'

        mi_u = MultiIndex.from_product(
            [list(u'ab'), range(3)], names=['first', 'second'])
        result = eval(repr(mi_u))
        tm.assert_index_equal(result, mi_u, exact=True)

        # formatting
        if PY3:
            str(mi)
        else:
            compat.text_type(mi)

        # long format
        mi = MultiIndex.from_product([list('abcdefg'), range(10)],
                                     names=['first', 'second'])

        if PY3:
            tm.assert_index_equal(eval(repr(mi)), mi, exact=True)
        else:
            result = eval(repr(mi))
            # string coerces to unicode
            tm.assert_index_equal(result, mi, exact=False)
            assert mi.get_level_values('first').inferred_type == 'string'
            assert result.get_level_values('first').inferred_type == 'unicode'

        result = eval(repr(mi_u))
        tm.assert_index_equal(result, mi_u, exact=True)

    def test_str(self):
        # tested elsewhere
        pass

    def test_unicode_string_with_unicode(self):
        d = {"a": [u("\u05d0"), 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
        idx = pd.DataFrame(d).set_index(["a", "b"]).index

        if PY3:
            str(idx)
        else:
            compat.text_type(idx)

    def test_bytestring_with_unicode(self):
        d = {"a": [u("\u05d0"), 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
        idx = pd.DataFrame(d).set_index(["a", "b"]).index

        if PY3:
            bytes(idx)
        else:
            str(idx)

    def test_slice_keep_name(self):
        x = MultiIndex.from_tuples([('a', 'b'), (1, 2), ('c', 'd')],
                                   names=['x', 'y'])
        assert x[1:].names == x.names

    def test_isna_behavior(self):
        # should not segfault GH5123
        # NOTE: if MI representation changes, may make sense to allow
        # isna(MI)
        with pytest.raises(NotImplementedError):
            pd.isna(self.index)

    def test_level_setting_resets_attributes(self):
        ind = pd.MultiIndex.from_arrays([
            ['A', 'A', 'B', 'B', 'B'], [1, 2, 1, 2, 3]
        ])
        assert ind.is_monotonic
        ind.set_levels([['A', 'B'], [1, 3, 2]], inplace=True)
        # if this fails, probably didn't reset the cache correctly.
        assert not ind.is_monotonic

    def test_is_monotonic_increasing(self):
        i = MultiIndex.from_product([np.arange(10),
                                     np.arange(10)], names=['one', 'two'])
        assert i.is_monotonic
        assert i._is_strictly_monotonic_increasing
        assert Index(i.values).is_monotonic
        assert i._is_strictly_monotonic_increasing

        i = MultiIndex.from_product([np.arange(10, 0, -1),
                                     np.arange(10)], names=['one', 'two'])
        assert not i.is_monotonic
        assert not i._is_strictly_monotonic_increasing
        assert not Index(i.values).is_monotonic
        assert not Index(i.values)._is_strictly_monotonic_increasing

        i = MultiIndex.from_product([np.arange(10),
                                     np.arange(10, 0, -1)],
                                    names=['one', 'two'])
        assert not i.is_monotonic
        assert not i._is_strictly_monotonic_increasing
        assert not Index(i.values).is_monotonic
        assert not Index(i.values)._is_strictly_monotonic_increasing

        i = MultiIndex.from_product([[1.0, np.nan, 2.0], ['a', 'b', 'c']])
        assert not i.is_monotonic
        assert not i._is_strictly_monotonic_increasing
        assert not Index(i.values).is_monotonic
        assert not Index(i.values)._is_strictly_monotonic_increasing

        # string ordering
        i = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
                               ['one', 'two', 'three']],
                       labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                               [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                       names=['first', 'second'])
        assert not i.is_monotonic
        assert not Index(i.values).is_monotonic
        assert not i._is_strictly_monotonic_increasing
        assert not Index(i.values)._is_strictly_monotonic_increasing

        i = MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'],
                               ['mom', 'next', 'zenith']],
                       labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                               [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                       names=['first', 'second'])
        assert i.is_monotonic
        assert Index(i.values).is_monotonic
        assert i._is_strictly_monotonic_increasing
        assert Index(i.values)._is_strictly_monotonic_increasing

        # mixed levels, hits the TypeError
        i = MultiIndex(
            levels=[[1, 2, 3, 4], ['gb00b03mlx29', 'lu0197800237',
                                   'nl0000289783',
                                   'nl0000289965', 'nl0000301109']],
            labels=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
            names=['household_id', 'asset_id'])

        assert not i.is_monotonic
        assert not i._is_strictly_monotonic_increasing

        # empty
        i = MultiIndex.from_arrays([[], []])
        assert i.is_monotonic
        assert Index(i.values).is_monotonic
        assert i._is_strictly_monotonic_increasing
        assert Index(i.values)._is_strictly_monotonic_increasing

    def test_is_monotonic_decreasing(self):
        i = MultiIndex.from_product([np.arange(9, -1, -1),
                                     np.arange(9, -1, -1)],
                                    names=['one', 'two'])
        assert i.is_monotonic_decreasing
        assert i._is_strictly_monotonic_decreasing
        assert Index(i.values).is_monotonic_decreasing
        assert i._is_strictly_monotonic_decreasing

        i = MultiIndex.from_product([np.arange(10),
                                     np.arange(10, 0, -1)],
                                    names=['one', 'two'])
        assert not i.is_monotonic_decreasing
        assert not i._is_strictly_monotonic_decreasing
        assert not Index(i.values).is_monotonic_decreasing
        assert not Index(i.values)._is_strictly_monotonic_decreasing

        i = MultiIndex.from_product([np.arange(10, 0, -1),
                                     np.arange(10)], names=['one', 'two'])
        assert not i.is_monotonic_decreasing
        assert not i._is_strictly_monotonic_decreasing
        assert not Index(i.values).is_monotonic_decreasing
        assert not Index(i.values)._is_strictly_monotonic_decreasing

        i = MultiIndex.from_product([[2.0, np.nan, 1.0], ['c', 'b', 'a']])
        assert not i.is_monotonic_decreasing
        assert not i._is_strictly_monotonic_decreasing
        assert not Index(i.values).is_monotonic_decreasing
        assert not Index(i.values)._is_strictly_monotonic_decreasing

        # string ordering
        i = MultiIndex(levels=[['qux', 'foo', 'baz', 'bar'],
                               ['three', 'two', 'one']],
                       labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                               [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                       names=['first', 'second'])
        assert not i.is_monotonic_decreasing
        assert not Index(i.values).is_monotonic_decreasing
        assert not i._is_strictly_monotonic_decreasing
        assert not Index(i.values)._is_strictly_monotonic_decreasing

        i = MultiIndex(levels=[['qux', 'foo', 'baz', 'bar'],
                               ['zenith', 'next', 'mom']],
                       labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                               [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                       names=['first', 'second'])
        assert i.is_monotonic_decreasing
        assert Index(i.values).is_monotonic_decreasing
        assert i._is_strictly_monotonic_decreasing
        assert Index(i.values)._is_strictly_monotonic_decreasing

        # mixed levels, hits the TypeError
        i = MultiIndex(
            levels=[[4, 3, 2, 1], ['nl0000301109', 'nl0000289965',
                                   'nl0000289783', 'lu0197800237',
                                   'gb00b03mlx29']],
            labels=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
            names=['household_id', 'asset_id'])

        assert not i.is_monotonic_decreasing
        assert not i._is_strictly_monotonic_decreasing

        # empty
        i = MultiIndex.from_arrays([[], []])
        assert i.is_monotonic_decreasing
        assert Index(i.values).is_monotonic_decreasing
        assert i._is_strictly_monotonic_decreasing
        assert Index(i.values)._is_strictly_monotonic_decreasing

    def test_is_strictly_monotonic_increasing(self):
        idx = pd.MultiIndex(levels=[['bar', 'baz'], ['mom', 'next']],
                            labels=[[0, 0, 1, 1], [0, 0, 0, 1]])
        assert idx.is_monotonic_increasing
        assert not idx._is_strictly_monotonic_increasing

    def test_is_strictly_monotonic_decreasing(self):
        idx = pd.MultiIndex(levels=[['baz', 'bar'], ['next', 'mom']],
                            labels=[[0, 0, 1, 1], [0, 0, 0, 1]])
        assert idx.is_monotonic_decreasing
        assert not idx._is_strictly_monotonic_decreasing

    def test_reconstruct_sort(self):

        # starts off lexsorted & monotonic
        mi = MultiIndex.from_arrays([
            ['A', 'A', 'B', 'B', 'B'], [1, 2, 1, 2, 3]
        ])
        assert mi.is_lexsorted()
        assert mi.is_monotonic

        recons = mi._sort_levels_monotonic()
        assert recons.is_lexsorted()
        assert recons.is_monotonic
        assert mi is recons

        assert mi.equals(recons)
        assert Index(mi.values).equals(Index(recons.values))

        # cannot convert to lexsorted
        mi = pd.MultiIndex.from_tuples([('z', 'a'), ('x', 'a'), ('y', 'b'),
                                        ('x', 'b'), ('y', 'a'), ('z', 'b')],
                                       names=['one', 'two'])
        assert not mi.is_lexsorted()
        assert not mi.is_monotonic

        recons = mi._sort_levels_monotonic()
        assert not recons.is_lexsorted()
        assert not recons.is_monotonic

        assert mi.equals(recons)
        assert Index(mi.values).equals(Index(recons.values))

        # cannot convert to lexsorted
        mi = MultiIndex(levels=[['b', 'd', 'a'], [1, 2, 3]],
                        labels=[[0, 1, 0, 2], [2, 0, 0, 1]],
                        names=['col1', 'col2'])
        assert not mi.is_lexsorted()
        assert not mi.is_monotonic

        recons = mi._sort_levels_monotonic()
        assert not recons.is_lexsorted()
        assert not recons.is_monotonic

        assert mi.equals(recons)
        assert Index(mi.values).equals(Index(recons.values))

    def test_reconstruct_remove_unused(self):
        # xref to GH 2770
        df = DataFrame([['deleteMe', 1, 9],
                        ['keepMe', 2, 9],
                        ['keepMeToo', 3, 9]],
                       columns=['first', 'second', 'third'])
        df2 = df.set_index(['first', 'second'], drop=False)
        df2 = df2[df2['first'] != 'deleteMe']

        # removed levels are there
        expected = MultiIndex(levels=[['deleteMe', 'keepMe', 'keepMeToo'],
                                      [1, 2, 3]],
                              labels=[[1, 2], [1, 2]],
                              names=['first', 'second'])
        result = df2.index
        tm.assert_index_equal(result, expected)

        expected = MultiIndex(levels=[['keepMe', 'keepMeToo'],
                                      [2, 3]],
                              labels=[[0, 1], [0, 1]],
                              names=['first', 'second'])
        result = df2.index.remove_unused_levels()
        tm.assert_index_equal(result, expected)

        # idempotent
        result2 = result.remove_unused_levels()
        tm.assert_index_equal(result2, expected)
        assert result2.is_(result)

    @pytest.mark.parametrize('level0', [['a', 'd', 'b'],
                                        ['a', 'd', 'b', 'unused']])
    @pytest.mark.parametrize('level1', [['w', 'x', 'y', 'z'],
                                        ['w', 'x', 'y', 'z', 'unused']])
    def test_remove_unused_nan(self, level0, level1):
        # GH 18417
        mi = pd.MultiIndex(levels=[level0, level1],
                           labels=[[0, 2, -1, 1, -1], [0, 1, 2, 3, 2]])

        result = mi.remove_unused_levels()
        tm.assert_index_equal(result, mi)
        for level in 0, 1:
            assert('unused' not in result.levels[level])

    @pytest.mark.parametrize('first_type,second_type', [
        ('int64', 'int64'),
        ('datetime64[D]', 'str')])
    def test_remove_unused_levels_large(self, first_type, second_type):
        # GH16556

        # because tests should be deterministic (and this test in particular
        # checks that levels are removed, which is not the case for every
        # random input):
        rng = np.random.RandomState(4)  # seed is arbitrary value that works

        size = 1 << 16
        df = DataFrame(dict(
            first=rng.randint(0, 1 << 13, size).astype(first_type),
            second=rng.randint(0, 1 << 10, size).astype(second_type),
            third=rng.rand(size)))
        df = df.groupby(['first', 'second']).sum()
        df = df[df.third < 0.1]

        result = df.index.remove_unused_levels()
        assert len(result.levels[0]) < len(df.index.levels[0])
        assert len(result.levels[1]) < len(df.index.levels[1])
        assert result.equals(df.index)

        expected = df.reset_index().set_index(['first', 'second']).index
        tm.assert_index_equal(result, expected)

    def test_isin(self):
        values = [('foo', 2), ('bar', 3), ('quux', 4)]

        idx = MultiIndex.from_arrays([['qux', 'baz', 'foo', 'bar'], np.arange(
            4)])
        result = idx.isin(values)
        expected = np.array([False, False, True, True])
        tm.assert_numpy_array_equal(result, expected)

        # empty, return dtype bool
        idx = MultiIndex.from_arrays([[], []])
        result = idx.isin(values)
        assert len(result) == 0
        assert result.dtype == np.bool_

    @pytest.mark.skipif(PYPY, reason="tuples cmp recursively on PyPy")
    def test_isin_nan_not_pypy(self):
        idx = MultiIndex.from_arrays([['foo', 'bar'], [1.0, np.nan]])
        tm.assert_numpy_array_equal(idx.isin([('bar', np.nan)]),
                                    np.array([False, False]))
        tm.assert_numpy_array_equal(idx.isin([('bar', float('nan'))]),
                                    np.array([False, False]))

    @pytest.mark.skipif(not PYPY, reason="tuples cmp recursively on PyPy")
    def test_isin_nan_pypy(self):
        idx = MultiIndex.from_arrays([['foo', 'bar'], [1.0, np.nan]])
        tm.assert_numpy_array_equal(idx.isin([('bar', np.nan)]),
                                    np.array([False, True]))
        tm.assert_numpy_array_equal(idx.isin([('bar', float('nan'))]),
                                    np.array([False, True]))

    def test_isin_level_kwarg(self):
        idx = MultiIndex.from_arrays([['qux', 'baz', 'foo', 'bar'], np.arange(
            4)])

        vals_0 = ['foo', 'bar', 'quux']
        vals_1 = [2, 3, 10]

        expected = np.array([False, False, True, True])
        tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=0))
        tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=-2))

        tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=1))
        tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=-1))

        pytest.raises(IndexError, idx.isin, vals_0, level=5)
        pytest.raises(IndexError, idx.isin, vals_0, level=-5)

        pytest.raises(KeyError, idx.isin, vals_0, level=1.0)
        pytest.raises(KeyError, idx.isin, vals_1, level=-1.0)
        pytest.raises(KeyError, idx.isin, vals_1, level='A')

        idx.names = ['A', 'B']
        tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level='A'))
        tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level='B'))

        pytest.raises(KeyError, idx.isin, vals_1, level='C')

    def test_reindex_preserves_names_when_target_is_list_or_ndarray(self):
        # GH6552
        idx = self.index.copy()
        target = idx.copy()
        idx.names = target.names = [None, None]

        other_dtype = pd.MultiIndex.from_product([[1, 2], [3, 4]])

        # list & ndarray cases
        assert idx.reindex([])[0].names == [None, None]
        assert idx.reindex(np.array([]))[0].names == [None, None]
        assert idx.reindex(target.tolist())[0].names == [None, None]
        assert idx.reindex(target.values)[0].names == [None, None]
        assert idx.reindex(other_dtype.tolist())[0].names == [None, None]
        assert idx.reindex(other_dtype.values)[0].names == [None, None]

        idx.names = ['foo', 'bar']
        assert idx.reindex([])[0].names == ['foo', 'bar']
        assert idx.reindex(np.array([]))[0].names == ['foo', 'bar']
        assert idx.reindex(target.tolist())[0].names == ['foo', 'bar']
        assert idx.reindex(target.values)[0].names == ['foo', 'bar']
        assert idx.reindex(other_dtype.tolist())[0].names == ['foo', 'bar']
        assert idx.reindex(other_dtype.values)[0].names == ['foo', 'bar']

    def test_reindex_lvl_preserves_names_when_target_is_list_or_array(self):
        # GH7774
        idx = pd.MultiIndex.from_product([[0, 1], ['a', 'b']],
                                         names=['foo', 'bar'])
        assert idx.reindex([], level=0)[0].names == ['foo', 'bar']
        assert idx.reindex([], level=1)[0].names == ['foo', 'bar']

    def test_reindex_lvl_preserves_type_if_target_is_empty_list_or_array(self):
        # GH7774
        idx = pd.MultiIndex.from_product([[0, 1], ['a', 'b']])
        assert idx.reindex([], level=0)[0].levels[0].dtype.type == np.int64
        assert idx.reindex([], level=1)[0].levels[1].dtype.type == np.object_

    def test_groupby(self):
        groups = self.index.groupby(np.array([1, 1, 1, 2, 2, 2]))
        labels = self.index.get_values().tolist()
        exp = {1: labels[:3], 2: labels[3:]}
        tm.assert_dict_equal(groups, exp)

        # GH5620
        groups = self.index.groupby(self.index)
        exp = {key: [key] for key in self.index}
        tm.assert_dict_equal(groups, exp)

    def test_index_name_retained(self):
        # GH9857
        result = pd.DataFrame({'x': [1, 2, 6],
                               'y': [2, 2, 8],
                               'z': [-5, 0, 5]})
        result = result.set_index('z')
        result.loc[10] = [9, 10]
        df_expected = pd.DataFrame({'x': [1, 2, 6, 9],
                                    'y': [2, 2, 8, 10],
                                    'z': [-5, 0, 5, 10]})
        df_expected = df_expected.set_index('z')
        tm.assert_frame_equal(result, df_expected)

    def test_equals_operator(self):
        # GH9785
        assert (self.index == self.index).all()

    def test_large_multiindex_error(self):
        # GH12527
        df_below_1000000 = pd.DataFrame(
            1, index=pd.MultiIndex.from_product([[1, 2], range(499999)]),
            columns=['dest'])
        with pytest.raises(KeyError):
            df_below_1000000.loc[(-1, 0), 'dest']
        with pytest.raises(KeyError):
            df_below_1000000.loc[(3, 0), 'dest']
        df_above_1000000 = pd.DataFrame(
            1, index=pd.MultiIndex.from_product([[1, 2], range(500001)]),
            columns=['dest'])
        with pytest.raises(KeyError):
            df_above_1000000.loc[(-1, 0), 'dest']
        with pytest.raises(KeyError):
            df_above_1000000.loc[(3, 0), 'dest']

    def test_partial_string_timestamp_multiindex(self):
        # GH10331
        dr = pd.date_range('2016-01-01', '2016-01-03', freq='12H')
        abc = ['a', 'b', 'c']
        ix = pd.MultiIndex.from_product([dr, abc])
        df = pd.DataFrame({'c1': range(0, 15)}, index=ix)
        idx = pd.IndexSlice

        #                        c1
        # 2016-01-01 00:00:00 a   0
        #                     b   1
        #                     c   2
        # 2016-01-01 12:00:00 a   3
        #                     b   4
        #                     c   5
        # 2016-01-02 00:00:00 a   6
        #                     b   7
        #                     c   8
        # 2016-01-02 12:00:00 a   9
        #                     b  10
        #                     c  11
        # 2016-01-03 00:00:00 a  12
        #                     b  13
        #                     c  14

        # partial string matching on a single index
        for df_swap in (df.swaplevel(),
                        df.swaplevel(0),
                        df.swaplevel(0, 1)):
            df_swap = df_swap.sort_index()
            just_a = df_swap.loc['a']
            result = just_a.loc['2016-01-01']
            expected = df.loc[idx[:, 'a'], :].iloc[0:2]
            expected.index = expected.index.droplevel(1)
            tm.assert_frame_equal(result, expected)

        # indexing with IndexSlice
        result = df.loc[idx['2016-01-01':'2016-02-01', :], :]
        expected = df
        tm.assert_frame_equal(result, expected)

        # match on secondary index
        result = df_swap.loc[idx[:, '2016-01-01':'2016-01-01'], :]
        expected = df_swap.iloc[[0, 1, 5, 6, 10, 11]]
        tm.assert_frame_equal(result, expected)

        # Even though this syntax works on a single index, this is somewhat
        # ambiguous and we don't want to extend this behavior forward to work
        # in multi-indexes. This would amount to selecting a scalar from a
        # column.
        with pytest.raises(KeyError):
            df['2016-01-01']

        # partial string match on year only
        result = df.loc['2016']
        expected = df
        tm.assert_frame_equal(result, expected)

        # partial string match on date
        result = df.loc['2016-01-01']
        expected = df.iloc[0:6]
        tm.assert_frame_equal(result, expected)

        # partial string match on date and hour, from middle
        result = df.loc['2016-01-02 12']
        expected = df.iloc[9:12]
        tm.assert_frame_equal(result, expected)

        # partial string match on secondary index
        result = df_swap.loc[idx[:, '2016-01-02'], :]
        expected = df_swap.iloc[[2, 3, 7, 8, 12, 13]]
        tm.assert_frame_equal(result, expected)

        # tuple selector with partial string match on date
        result = df.loc[('2016-01-01', 'a'), :]
        expected = df.iloc[[0, 3]]
        tm.assert_frame_equal(result, expected)

        # Slicing date on first level should break (of course)
        with pytest.raises(KeyError):
            df_swap.loc['2016-01-01']

        # GH12685 (partial string with daily resolution or below)
        dr = date_range('2013-01-01', periods=100, freq='D')
        ix = MultiIndex.from_product([dr, ['a', 'b']])
        df = DataFrame(np.random.randn(200, 1), columns=['A'], index=ix)

        result = df.loc[idx['2013-03':'2013-03', :], :]
        expected = df.iloc[118:180]
        tm.assert_frame_equal(result, expected)

    def test_rangeindex_fallback_coercion_bug(self):
        # GH 12893
        foo = pd.DataFrame(np.arange(100).reshape((10, 10)))
        bar = pd.DataFrame(np.arange(100).reshape((10, 10)))
        df = pd.concat({'foo': foo.stack(), 'bar': bar.stack()}, axis=1)
        df.index.names = ['fizz', 'buzz']

        str(df)
        expected = pd.DataFrame({'bar': np.arange(100),
                                 'foo': np.arange(100)},
                                index=pd.MultiIndex.from_product(
                                    [range(10), range(10)],
                                    names=['fizz', 'buzz']))
        tm.assert_frame_equal(df, expected, check_like=True)

        result = df.index.get_level_values('fizz')
        expected = pd.Int64Index(np.arange(10), name='fizz').repeat(10)
        tm.assert_index_equal(result, expected)

        result = df.index.get_level_values('buzz')
        expected = pd.Int64Index(np.tile(np.arange(10), 10), name='buzz')
        tm.assert_index_equal(result, expected)

    def test_dropna(self):
        # GH 6194
        idx = pd.MultiIndex.from_arrays([[1, np.nan, 3, np.nan, 5],
                                         [1, 2, np.nan, np.nan, 5],
                                         ['a', 'b', 'c', np.nan, 'e']])

        exp = pd.MultiIndex.from_arrays([[1, 5],
                                         [1, 5],
                                         ['a', 'e']])
        tm.assert_index_equal(idx.dropna(), exp)
        tm.assert_index_equal(idx.dropna(how='any'), exp)

        exp = pd.MultiIndex.from_arrays([[1, np.nan, 3, 5],
                                         [1, 2, np.nan, 5],
                                         ['a', 'b', 'c', 'e']])
        tm.assert_index_equal(idx.dropna(how='all'), exp)

        msg = "invalid how option: xxx"
        with tm.assert_raises_regex(ValueError, msg):
            idx.dropna(how='xxx')

    def test_unsortedindex(self):
        # GH 11897
        mi = pd.MultiIndex.from_tuples([('z', 'a'), ('x', 'a'), ('y', 'b'),
                                        ('x', 'b'), ('y', 'a'), ('z', 'b')],
                                       names=['one', 'two'])
        df = pd.DataFrame([[i, 10 * i] for i in lrange(6)], index=mi,
                          columns=['one', 'two'])

        # GH 16734: not sorted, but no real slicing
        result = df.loc(axis=0)['z', 'a']
        expected = df.iloc[0]
        tm.assert_series_equal(result, expected)

        with pytest.raises(UnsortedIndexError):
            df.loc(axis=0)['z', slice('a')]
        df.sort_index(inplace=True)
        assert len(df.loc(axis=0)['z', :]) == 2

        with pytest.raises(KeyError):
            df.loc(axis=0)['q', :]

    def test_unsortedindex_doc_examples(self):
        # http://pandas.pydata.org/pandas-docs/stable/advanced.html#sorting-a-multiindex  # noqa
        dfm = DataFrame({'jim': [0, 0, 1, 1],
                         'joe': ['x', 'x', 'z', 'y'],
                         'jolie': np.random.rand(4)})

        dfm = dfm.set_index(['jim', 'joe'])
        with tm.assert_produces_warning(PerformanceWarning):
            dfm.loc[(1, 'z')]

        with pytest.raises(UnsortedIndexError):
            dfm.loc[(0, 'y'):(1, 'z')]

        assert not dfm.index.is_lexsorted()
        assert dfm.index.lexsort_depth == 1

        # sort it
        dfm = dfm.sort_index()
        dfm.loc[(1, 'z')]
        dfm.loc[(0, 'y'):(1, 'z')]

        assert dfm.index.is_lexsorted()
        assert dfm.index.lexsort_depth == 2

    def test_tuples_with_name_string(self):
        # GH 15110 and GH 14848

        li = [(0, 0, 1), (0, 1, 0), (1, 0, 0)]
        with pytest.raises(ValueError):
            pd.Index(li, name='abc')
        with pytest.raises(ValueError):
            pd.Index(li, name='a')

    def test_nan_stays_float(self):

        # GH 7031
        idx0 = pd.MultiIndex(levels=[["A", "B"], []],
                             labels=[[1, 0], [-1, -1]],
                             names=[0, 1])
        idx1 = pd.MultiIndex(levels=[["C"], ["D"]],
                             labels=[[0], [0]],
                             names=[0, 1])
        idxm = idx0.join(idx1, how='outer')
        assert pd.isna(idx0.get_level_values(1)).all()
        # the following failed in 0.14.1
        assert pd.isna(idxm.get_level_values(1)[:-1]).all()

        df0 = pd.DataFrame([[1, 2]], index=idx0)
        df1 = pd.DataFrame([[3, 4]], index=idx1)
        dfm = df0 - df1
        assert pd.isna(df0.index.get_level_values(1)).all()
        # the following failed in 0.14.1
        assert pd.isna(dfm.index.get_level_values(1)[:-1]).all()

    def test_million_record_attribute_error(self):
        # GH 18165
        r = list(range(1000000))
        df = pd.DataFrame({'a': r, 'b': r},
                          index=pd.MultiIndex.from_tuples([(x, x) for x in r]))

        with tm.assert_raises_regex(AttributeError,
                                    "'Series' object has no attribute 'foo'"):
            df['a'].foo()

    def test_duplicate_multiindex_labels(self):
        # GH 17464
        # Make sure that a MultiIndex with duplicate levels throws a ValueError
        with pytest.raises(ValueError):
            ind = pd.MultiIndex([['A'] * 10, range(10)], [[0] * 10, range(10)])

        # And that using set_levels with duplicate levels fails
        ind = MultiIndex.from_arrays([['A', 'A', 'B', 'B', 'B'],
                                      [1, 2, 1, 2, 3]])
        with pytest.raises(ValueError):
            ind.set_levels([['A', 'B', 'A', 'A', 'B'], [2, 1, 3, -2, 5]],
                           inplace=True)