manyi/venv/Lib/site-packages/pandas/tests/test_multilevel.py

# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101,W0141
import datetime
import itertools
from warnings import catch_warnings, simplefilter

import numpy as np
from numpy.random import randn
import pytest
import pytz

from pandas.compat import (
    StringIO, lrange, lzip, product as cart_product, range, u, zip)

from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype

import pandas as pd
from pandas import DataFrame, Panel, Series, Timestamp, isna
from pandas.core.index import Index, MultiIndex
import pandas.util.testing as tm

AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew', 'mad',
                 'std', 'var', 'sem']


class Base(object):

    def setup_method(self, method):

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
                                                                  'three']],
                           codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                  [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])
        self.frame = DataFrame(np.random.randn(10, 3), index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

        self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                                       codes=[[0, 1, 2, 3]], names=['first'])

        # create test series object
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = lzip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        self.tdf = tm.makeTimeDataFrame(100)
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
                                     lambda x: x.day]).sum()

        # use Int64Index, to make sure things work
        self.ymd.index.set_levels([lev.astype('i8')
                                   for lev in self.ymd.index.levels],
                                  inplace=True)
        self.ymd.index.set_names(['year', 'month', 'day'], inplace=True)


class TestMultiLevel(Base):

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a['A'].append(b['A'])
        tm.assert_series_equal(result, self.frame['A'])

    def test_append_index(self):
        idx1 = Index([1.1, 1.2, 1.3])
        idx2 = pd.date_range('2011-01-01', freq='D', periods=3,
                             tz='Asia/Tokyo')
        idx3 = Index(['A', 'B', 'C'])

        midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
        midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])

        result = idx1.append(midx_lv2)

        # see gh-7112
        tz = pytz.timezone('Asia/Tokyo')
        expected_tuples = [(1.1, tz.localize(datetime.datetime(2011, 1, 1))),
                           (1.2, tz.localize(datetime.datetime(2011, 1, 2))),
                           (1.3, tz.localize(datetime.datetime(2011, 1, 3)))]
        expected = Index([1.1, 1.2, 1.3] + expected_tuples)
        tm.assert_index_equal(result, expected)

        result = midx_lv2.append(idx1)
        expected = Index(expected_tuples + [1.1, 1.2, 1.3])
        tm.assert_index_equal(result, expected)

        result = midx_lv2.append(midx_lv2)
        expected = MultiIndex.from_arrays([idx1.append(idx1),
                                           idx2.append(idx2)])
        tm.assert_index_equal(result, expected)

        result = midx_lv2.append(midx_lv3)
        tm.assert_index_equal(result, expected)

        result = midx_lv3.append(midx_lv2)
        expected = Index._simple_new(
            np.array([(1.1, tz.localize(datetime.datetime(2011, 1, 1)), 'A'),
                      (1.2, tz.localize(datetime.datetime(2011, 1, 2)), 'B'),
                      (1.3, tz.localize(datetime.datetime(2011, 1, 3)), 'C')] +
                     expected_tuples), None)
        tm.assert_index_equal(result, expected)

    def test_dataframe_constructor(self):
        multi = DataFrame(np.random.randn(4, 4),
                          index=[np.array(['a', 'a', 'b', 'b']),
                                 np.array(['x', 'y', 'x', 'y'])])
        assert isinstance(multi.index, MultiIndex)
        assert not isinstance(multi.columns, MultiIndex)

        multi = DataFrame(np.random.randn(4, 4),
                          columns=[['a', 'a', 'b', 'b'],
                                   ['x', 'y', 'x', 'y']])
        assert isinstance(multi.columns, MultiIndex)

    def test_series_constructor(self):
        multi = Series(1., index=[np.array(['a', 'a', 'b', 'b']), np.array(
            ['x', 'y', 'x', 'y'])])
        assert isinstance(multi.index, MultiIndex)

        multi = Series(1., index=[['a', 'a', 'b', 'b'], ['x', 'y', 'x', 'y']])
        assert isinstance(multi.index, MultiIndex)

        multi = Series(lrange(4), index=[['a', 'a', 'b', 'b'],
                                         ['x', 'y', 'x', 'y']])
        assert isinstance(multi.index, MultiIndex)

    def test_reindex_level(self):
        # axis=0
        month_sums = self.ymd.sum(level='month')
        result = month_sums.reindex(self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum)

        tm.assert_frame_equal(result, expected)

        # Series
        result = month_sums['A'].reindex(self.ymd.index, level=1)
        expected = self.ymd['A'].groupby(level='month').transform(np.sum)
        tm.assert_series_equal(result, expected, check_names=False)

        # axis=1
        month_sums = self.ymd.T.sum(axis=1, level='month')
        result = month_sums.reindex(columns=self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum).T
        tm.assert_frame_equal(result, expected)

    def test_binops_level(self):
        def _check_op(opname):
            op = getattr(DataFrame, opname)
            month_sums = self.ymd.sum(level='month')
            result = op(self.ymd, month_sums, level='month')

            broadcasted = self.ymd.groupby(level='month').transform(np.sum)
            expected = op(self.ymd, broadcasted)
            tm.assert_frame_equal(result, expected)

            # Series
            op = getattr(Series, opname)
            result = op(self.ymd['A'], month_sums['A'], level='month')
            broadcasted = self.ymd['A'].groupby(level='month').transform(
                np.sum)
            expected = op(self.ymd['A'], broadcasted)
            expected.name = 'A'
            tm.assert_series_equal(result, expected)

        _check_op('sub')
        _check_op('add')
        _check_op('mul')
        _check_op('div')

    def test_pickle(self):
        def _test_roundtrip(frame):
            unpickled = tm.round_trip_pickle(frame)
            tm.assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        expected = self.frame.iloc[[0, 3]]
        reindexed = self.frame.loc[[('foo', 'one'), ('bar', 'one')]]
        tm.assert_frame_equal(reindexed, expected)

        with catch_warnings(record=True):
            simplefilter("ignore", DeprecationWarning)
            reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
        tm.assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        assert chunk.index is new_index

        chunk = self.ymd.loc[new_index]
        assert chunk.index is new_index

        with catch_warnings(record=True):
            simplefilter("ignore", DeprecationWarning)
            chunk = self.ymd.ix[new_index]
        assert chunk.index is new_index

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        assert chunk.columns is new_index

        chunk = ymdT.loc[:, new_index]
        assert chunk.columns is new_index

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_repr_name_coincide(self):
        index = MultiIndex.from_tuples([('a', 0, 'foo'), ('b', 1, 'bar')],
                                       names=['a', 'b', 'c'])

        df = DataFrame({'value': [0, 1]}, index=index)

        lines = repr(df).split('\n')
        assert lines[2].startswith('a 0 foo')

    def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2'])

    def test_reset_index_with_drop(self):
        deleveled = self.ymd.reset_index(drop=True)
        assert len(deleveled.columns) == len(self.ymd.columns)
        assert deleveled.index.name == self.ymd.index.name

        deleveled = self.series.reset_index()
        assert isinstance(deleveled, DataFrame)
        assert len(deleveled.columns) == len(self.series.index.levels) + 1
        assert deleveled.index.name == self.series.index.name

        deleveled = self.series.reset_index(drop=True)
        assert isinstance(deleveled, Series)
        assert deleveled.index.name == self.series.index.name

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count()
                expected = expected.reindex_like(result).astype('i8')
                tm.assert_frame_equal(result, expected)

        self.frame.iloc[1, [1, 2]] = np.nan
        self.frame.iloc[7, [0, 1]] = np.nan
        self.ymd.iloc[1, [1, 2]] = np.nan
        self.ymd.iloc[7, [0, 1]] = np.nan

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        with pytest.raises(TypeError, match='hierarchical'):
            df.count(level=0)

        self.frame['D'] = 'foo'
        result = self.frame.count(level=0, numeric_only=True)
        tm.assert_index_equal(result.columns, Index(list('ABC'), name='exp'))

    def test_count_level_series(self):
        index = MultiIndex(levels=[['foo', 'bar', 'baz'], ['one', 'two',
                                                           'three', 'four']],
                           codes=[[0, 0, 0, 2, 2], [2, 0, 1, 1, 2]])

        s = Series(np.random.randn(len(index)), index=index)

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        tm.assert_series_equal(
            result.astype('f8'), expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        tm.assert_series_equal(
            result.astype('f8'), expected.reindex(result.index).fillna(0))

    def test_count_level_corner(self):
        s = self.frame['A'][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0], name='A')
        tm.assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0],
                             columns=df.columns).fillna(0).astype(np.int64)
        tm.assert_frame_equal(result, expected)

    def test_get_level_number_out_of_bounds(self):
        with pytest.raises(IndexError, match="Too many levels"):
            self.frame.index._get_level_number(2)
        with pytest.raises(IndexError, match="not a valid level number"):
            self.frame.index._get_level_number(-3)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked.unstack()

        # test that ints work
        self.ymd.astype(int).unstack()

        # test that int32 work
        self.ymd.astype(np.int32).unstack()

    def test_unstack_multiple_no_empty_columns(self):
        index = MultiIndex.from_tuples([(0, 'foo', 0), (0, 'bar', 0), (
            1, 'baz', 1), (1, 'qux', 1)])

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

        unstacked = s.unstack([1, 2])
        expected = unstacked.dropna(axis=1, how='all')
        tm.assert_frame_equal(unstacked, expected)

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        tm.assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sort_index(level=2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        tm.assert_frame_equal(restacked.sort_index(level=0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort_index(axis=1, ascending=False)
        restacked = unstacked.stack()
        tm.assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        tm.assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        tm.assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        tm.assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).loc[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        tm.assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

        # stack with negative number
        result = self.ymd.unstack(0).stack(-2)
        expected = self.ymd.unstack(0).stack(0)

        # GH10417
        def check(left, right):
            tm.assert_series_equal(left, right)
            assert left.index.is_unique is False
            li, ri = left.index, right.index
            tm.assert_index_equal(li, ri)

        df = DataFrame(np.arange(12).reshape(4, 3),
                       index=list('abab'),
                       columns=['1st', '2nd', '3rd'])

        mi = MultiIndex(levels=[['a', 'b'], ['1st', '2nd', '3rd']],
                        codes=[np.tile(
                            np.arange(2).repeat(3), 2), np.tile(
                                np.arange(3), 4)])

        left, right = df.stack(), Series(np.arange(12), index=mi)
        check(left, right)

        df.columns = ['1st', '2nd', '1st']
        mi = MultiIndex(levels=[['a', 'b'], ['1st', '2nd']], codes=[np.tile(
            np.arange(2).repeat(3), 2), np.tile(
                [0, 1, 0], 4)])

        left, right = df.stack(), Series(np.arange(12), index=mi)
        check(left, right)

        tpls = ('a', 2), ('b', 1), ('a', 1), ('b', 2)
        df.index = MultiIndex.from_tuples(tpls)
        mi = MultiIndex(levels=[['a', 'b'], [1, 2], ['1st', '2nd']],
                        codes=[np.tile(
                            np.arange(2).repeat(3), 2), np.repeat(
                                [1, 0, 1], [3, 6, 3]), np.tile(
                                    [0, 1, 0], 4)])

        left, right = df.stack(), Series(np.arange(12), index=mi)
        check(left, right)

    def test_unstack_odd_failure(self):
        data = """day,time,smoker,sum,len
Fri,Dinner,No,8.25,3.
Fri,Dinner,Yes,27.03,9
Fri,Lunch,No,3.0,1
Fri,Lunch,Yes,13.68,6
Sat,Dinner,No,139.63,45
Sat,Dinner,Yes,120.77,42
Sun,Dinner,No,180.57,57
Sun,Dinner,Yes,66.82,19
Thur,Dinner,No,3.0,1
Thur,Lunch,No,117.32,44
Thur,Lunch,Yes,51.51,17"""

        df = pd.read_csv(StringIO(data)).set_index(['day', 'time', 'smoker'])

        # it works, #2100
        result = df.unstack(2)

        recons = result.stack()
        tm.assert_frame_equal(recons, df)

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'
        df = df.sort_index(level=1, axis=1)

        stacked = df.stack()
        result = df['foo'].stack().sort_index()
        tm.assert_series_equal(stacked['foo'], result, check_names=False)
        assert result.name is None
        assert stacked['bar'].dtype == np.float_

    def test_unstack_bug(self):
        df = DataFrame({'state': ['naive', 'naive', 'naive', 'activ', 'activ',
                                  'activ'],
                        'exp': ['a', 'b', 'b', 'b', 'a', 'a'],
                        'barcode': [1, 2, 3, 4, 1, 3],
                        'v': ['hi', 'hi', 'bye', 'bye', 'bye', 'peace'],
                        'extra': np.arange(6.)})

        result = df.groupby(['state', 'exp', 'barcode', 'v']).apply(len)

        unstacked = result.unstack()
        restacked = unstacked.stack()
        tm.assert_series_equal(
            restacked, result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        assert unstacked.index.name == 'first'
        assert unstacked.columns.names == ['exp', 'second']

        restacked = unstacked.stack()
        assert restacked.index.names == self.frame.index.names

    def test_unstack_level_name(self):
        result = self.frame.unstack('second')
        expected = self.frame.unstack(level=1)
        tm.assert_frame_equal(result, expected)

    def test_stack_level_name(self):
        unstacked = self.frame.unstack('second')
        result = unstacked.stack('exp')
        expected = self.frame.unstack().stack(0)
        tm.assert_frame_equal(result, expected)

        result = self.frame.stack('exp')
        expected = self.frame.stack()
        tm.assert_series_equal(result, expected)

    def test_stack_unstack_multiple(self):
        unstacked = self.ymd.unstack(['year', 'month'])
        expected = self.ymd.unstack('year').unstack('month')
        tm.assert_frame_equal(unstacked, expected)
        assert unstacked.columns.names == expected.columns.names

        # series
        s = self.ymd['A']
        s_unstacked = s.unstack(['year', 'month'])
        tm.assert_frame_equal(s_unstacked, expected['A'])

        restacked = unstacked.stack(['year', 'month'])
        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
        restacked = restacked.sort_index(level=0)

        tm.assert_frame_equal(restacked, self.ymd)
        assert restacked.index.names == self.ymd.index.names

        # GH #451
        unstacked = self.ymd.unstack([1, 2])
        expected = self.ymd.unstack(1).unstack(1).dropna(axis=1, how='all')
        tm.assert_frame_equal(unstacked, expected)

        unstacked = self.ymd.unstack([2, 1])
        expected = self.ymd.unstack(2).unstack(1).dropna(axis=1, how='all')
        tm.assert_frame_equal(unstacked, expected.loc[:, unstacked.columns])

    def test_stack_names_and_numbers(self):
        unstacked = self.ymd.unstack(['year', 'month'])

        # Can't use mixture of names and numbers to stack
        with pytest.raises(ValueError, match="level should contain"):
            unstacked.stack([0, 'month'])

    def test_stack_multiple_out_of_bounds(self):
        # nlevels == 3
        unstacked = self.ymd.unstack(['year', 'month'])

        with pytest.raises(IndexError, match="Too many levels"):
            unstacked.stack([2, 3])
        with pytest.raises(IndexError, match="not a valid level number"):
            unstacked.stack([-4, -3])

    def test_unstack_period_series(self):
        # GH 4342
        idx1 = pd.PeriodIndex(['2013-01', '2013-01', '2013-02', '2013-02',
                               '2013-03', '2013-03'], freq='M', name='period')
        idx2 = Index(['A', 'B'] * 3, name='str')
        value = [1, 2, 3, 4, 5, 6]

        idx = MultiIndex.from_arrays([idx1, idx2])
        s = Series(value, index=idx)

        result1 = s.unstack()
        result2 = s.unstack(level=1)
        result3 = s.unstack(level=0)

        e_idx = pd.PeriodIndex(
            ['2013-01', '2013-02', '2013-03'], freq='M', name='period')
        expected = DataFrame({'A': [1, 3, 5], 'B': [2, 4, 6]}, index=e_idx,
                             columns=['A', 'B'])
        expected.columns.name = 'str'

        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)
        tm.assert_frame_equal(result3, expected.T)

        idx1 = pd.PeriodIndex(['2013-01', '2013-01', '2013-02', '2013-02',
                               '2013-03', '2013-03'], freq='M', name='period1')

        idx2 = pd.PeriodIndex(['2013-12', '2013-11', '2013-10', '2013-09',
                               '2013-08', '2013-07'], freq='M', name='period2')
        idx = MultiIndex.from_arrays([idx1, idx2])
        s = Series(value, index=idx)

        result1 = s.unstack()
        result2 = s.unstack(level=1)
        result3 = s.unstack(level=0)

        e_idx = pd.PeriodIndex(
            ['2013-01', '2013-02', '2013-03'], freq='M', name='period1')
        e_cols = pd.PeriodIndex(['2013-07', '2013-08', '2013-09', '2013-10',
                                 '2013-11', '2013-12'],
                                freq='M', name='period2')
        expected = DataFrame([[np.nan, np.nan, np.nan, np.nan, 2, 1],
                              [np.nan, np.nan, 4, 3, np.nan, np.nan],
                              [6, 5, np.nan, np.nan, np.nan, np.nan]],
                             index=e_idx, columns=e_cols)

        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)
        tm.assert_frame_equal(result3, expected.T)

    def test_unstack_period_frame(self):
        # GH 4342
        idx1 = pd.PeriodIndex(['2014-01', '2014-02', '2014-02', '2014-02',
                               '2014-01', '2014-01'],
                              freq='M', name='period1')
        idx2 = pd.PeriodIndex(['2013-12', '2013-12', '2014-02', '2013-10',
                               '2013-10', '2014-02'],
                              freq='M', name='period2')
        value = {'A': [1, 2, 3, 4, 5, 6], 'B': [6, 5, 4, 3, 2, 1]}
        idx = MultiIndex.from_arrays([idx1, idx2])
        df = DataFrame(value, index=idx)

        result1 = df.unstack()
        result2 = df.unstack(level=1)
        result3 = df.unstack(level=0)

        e_1 = pd.PeriodIndex(['2014-01', '2014-02'], freq='M', name='period1')
        e_2 = pd.PeriodIndex(['2013-10', '2013-12', '2014-02', '2013-10',
                              '2013-12', '2014-02'], freq='M', name='period2')
        e_cols = MultiIndex.from_arrays(['A A A B B B'.split(), e_2])
        expected = DataFrame([[5, 1, 6, 2, 6, 1], [4, 2, 3, 3, 5, 4]],
                             index=e_1, columns=e_cols)

        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)

        e_1 = pd.PeriodIndex(['2014-01', '2014-02', '2014-01',
                              '2014-02'], freq='M', name='period1')
        e_2 = pd.PeriodIndex(
            ['2013-10', '2013-12', '2014-02'], freq='M', name='period2')
        e_cols = MultiIndex.from_arrays(['A A B B'.split(), e_1])
        expected = DataFrame([[5, 4, 2, 3], [1, 2, 6, 5], [6, 3, 1, 4]],
                             index=e_2, columns=e_cols)

        tm.assert_frame_equal(result3, expected)

    def test_stack_multiple_bug(self):
        """ bug when some uniques are not present in the data #3170"""
        id_col = ([1] * 3) + ([2] * 3)
        name = (['a'] * 3) + (['b'] * 3)
        date = pd.to_datetime(['2013-01-03', '2013-01-04', '2013-01-05'] * 2)
        var1 = np.random.randint(0, 100, 6)
        df = DataFrame(dict(ID=id_col, NAME=name, DATE=date, VAR1=var1))

        multi = df.set_index(['DATE', 'ID'])
        multi.columns.name = 'Params'
        unst = multi.unstack('ID')
        down = unst.resample('W-THU').mean()

        rs = down.stack('ID')
        xp = unst.loc[:, ['VAR1']].resample('W-THU').mean().stack('ID')
        xp.columns.name = 'Params'
        tm.assert_frame_equal(rs, xp)

    def test_stack_dropna(self):
        # GH #3997
        df = DataFrame({'A': ['a1', 'a2'], 'B': ['b1', 'b2'], 'C': [1, 1]})
        df = df.set_index(['A', 'B'])

        stacked = df.unstack().stack(dropna=False)
        assert len(stacked) > len(stacked.dropna())

        stacked = df.unstack().stack(dropna=True)
        tm.assert_frame_equal(stacked, stacked.dropna())

    def test_unstack_multiple_hierarchical(self):
        df = DataFrame(index=[[0, 0, 0, 0, 1, 1, 1, 1],
                              [0, 0, 1, 1, 0, 0, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1
                                                         ]],
                       columns=[[0, 0, 1, 1], [0, 1, 0, 1]])

        df.index.names = ['a', 'b', 'c']
        df.columns.names = ['d', 'e']

        # it works!
        df.unstack(['b', 'c'])

    def test_groupby_transform(self):
        s = self.frame['A']
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        result = applied.reindex(expected.index)
        tm.assert_series_equal(result, expected, check_names=False)

    def test_unstack_sparse_keyspace(self):
        # memory problems with naive impl #2278
        # Generate Long File & Test Pivot
        NUM_ROWS = 1000

        df = DataFrame({'A': np.random.randint(100, size=NUM_ROWS),
                        'B': np.random.randint(300, size=NUM_ROWS),
                        'C': np.random.randint(-7, 7, size=NUM_ROWS),
                        'D': np.random.randint(-19, 19, size=NUM_ROWS),
                        'E': np.random.randint(3000, size=NUM_ROWS),
                        'F': np.random.randn(NUM_ROWS)})

        idf = df.set_index(['A', 'B', 'C', 'D', 'E'])

        # it works! is sufficient
        idf.unstack('E')

    def test_unstack_unobserved_keys(self):
        # related to #2278 refactoring
        levels = [[0, 1], [0, 1, 2, 3]]
        codes = [[0, 0, 1, 1], [0, 2, 0, 2]]

        index = MultiIndex(levels, codes)

        df = DataFrame(np.random.randn(4, 2), index=index)

        result = df.unstack()
        assert len(result.columns) == 4

        recons = result.stack()
        tm.assert_frame_equal(recons, df)

    @pytest.mark.slow
    def test_unstack_number_of_levels_larger_than_int32(self):
        # GH 20601
        df = DataFrame(np.random.randn(2 ** 16, 2),
                       index=[np.arange(2 ** 16), np.arange(2 ** 16)])
        with pytest.raises(ValueError, match='int32 overflow'):
            df.unstack()

    def test_stack_order_with_unsorted_levels(self):
        # GH 16323

        def manual_compare_stacked(df, df_stacked, lev0, lev1):
            assert all(df.loc[row, col] ==
                       df_stacked.loc[(row, col[lev0]), col[lev1]]
                       for row in df.index for col in df.columns)

        # deep check for 1-row case
        for width in [2, 3]:
            levels_poss = itertools.product(
                itertools.permutations([0, 1, 2], width),
                repeat=2)

            for levels in levels_poss:
                columns = MultiIndex(levels=levels,
                                     codes=[[0, 0, 1, 1],
                                            [0, 1, 0, 1]])
                df = DataFrame(columns=columns, data=[range(4)])
                for stack_lev in range(2):
                    df_stacked = df.stack(stack_lev)
                    manual_compare_stacked(df, df_stacked,
                                           stack_lev, 1 - stack_lev)

        # check multi-row case
        mi = MultiIndex(levels=[["A", "C", "B"], ["B", "A", "C"]],
                        codes=[np.repeat(range(3), 3), np.tile(range(3), 3)])
        df = DataFrame(columns=mi, index=range(5),
                       data=np.arange(5 * len(mi)).reshape(5, -1))
        manual_compare_stacked(df, df.stack(0), 0, 1)

    def test_groupby_corner(self):
        midx = MultiIndex(levels=[['foo'], ['bar'], ['baz']],
                          codes=[[0], [0], [0]],
                          names=['one', 'two', 'three'])
        df = DataFrame([np.random.rand(4)], columns=['a', 'b', 'c', 'd'],
                       index=midx)
        # should work
        df.groupby(level='three')

    def test_groupby_level_no_obs(self):
        # #1697
        midx = MultiIndex.from_tuples([('f1', 's1'), ('f1', 's2'), (
            'f2', 's1'), ('f2', 's2'), ('f3', 's1'), ('f3', 's2')])
        df = DataFrame(
            [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], columns=midx)
        df1 = df.loc(axis=1)[df.columns.map(
            lambda u: u[0] in ['f2', 'f3'])]

        grouped = df1.groupby(axis=1, level=0)
        result = grouped.sum()
        assert (result.columns == ['f2', 'f3']).all()

    def test_join(self):
        a = self.frame.loc[self.frame.index[:5], ['A']]
        b = self.frame.loc[self.frame.index[2:], ['B', 'C']]

        joined = a.join(b, how='outer').reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        assert not np.isnan(joined.values).all()

        # TODO what should join do with names ?
        tm.assert_frame_equal(joined, expected, check_names=False)

    def test_swaplevel(self):
        swapped = self.frame['A'].swaplevel()
        swapped2 = self.frame['A'].swaplevel(0)
        swapped3 = self.frame['A'].swaplevel(0, 1)
        swapped4 = self.frame['A'].swaplevel('first', 'second')
        assert not swapped.index.equals(self.frame.index)
        tm.assert_series_equal(swapped, swapped2)
        tm.assert_series_equal(swapped, swapped3)
        tm.assert_series_equal(swapped, swapped4)

        back = swapped.swaplevel()
        back2 = swapped.swaplevel(0)
        back3 = swapped.swaplevel(0, 1)
        back4 = swapped.swaplevel('second', 'first')
        assert back.index.equals(self.frame.index)
        tm.assert_series_equal(back, back2)
        tm.assert_series_equal(back, back3)
        tm.assert_series_equal(back, back4)

        ft = self.frame.T
        swapped = ft.swaplevel('first', 'second', axis=1)
        exp = self.frame.swaplevel('first', 'second').T
        tm.assert_frame_equal(swapped, exp)

    def test_swaplevel_panel(self):
        with catch_warnings(record=True):
            simplefilter("ignore", FutureWarning)
            panel = Panel({'ItemA': self.frame, 'ItemB': self.frame * 2})
            expected = panel.copy()
            expected.major_axis = expected.major_axis.swaplevel(0, 1)

            for result in (panel.swaplevel(axis='major'),
                           panel.swaplevel(0, axis='major'),
                           panel.swaplevel(0, 1, axis='major')):
                tm.assert_panel_equal(result, expected)

    def test_reorder_levels(self):
        result = self.ymd.reorder_levels(['month', 'day', 'year'])
        expected = self.ymd.swaplevel(0, 1).swaplevel(1, 2)
        tm.assert_frame_equal(result, expected)

        result = self.ymd['A'].reorder_levels(['month', 'day', 'year'])
        expected = self.ymd['A'].swaplevel(0, 1).swaplevel(1, 2)
        tm.assert_series_equal(result, expected)

        result = self.ymd.T.reorder_levels(['month', 'day', 'year'], axis=1)
        expected = self.ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
        tm.assert_frame_equal(result, expected)

        with pytest.raises(TypeError, match='hierarchical axis'):
            self.ymd.reorder_levels([1, 2], axis=1)

        with pytest.raises(IndexError, match='Too many levels'):
            self.ymd.index.reorder_levels([1, 2, 3])

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        assert isinstance(df.columns, MultiIndex)
        assert (df[2000, 1, 10] == df[2000, 1, 7]).all()

    def test_alignment(self):
        x = Series(data=[1, 2, 3], index=MultiIndex.from_tuples([("A", 1), (
            "A", 2), ("B", 3)]))

        y = Series(data=[4, 5, 6], index=MultiIndex.from_tuples([("Z", 1), (
            "Z", 2), ("B", 3)]))

        res = x - y
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        tm.assert_series_equal(res, exp)

        # hit non-monotonic code path
        res = x[::-1] - y[::-1]
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        tm.assert_series_equal(res, exp)

    def test_count(self):
        frame = self.frame.copy()
        frame.index.names = ['a', 'b']

        result = frame.count(level='b')
        expect = self.frame.count(level=1)
        tm.assert_frame_equal(result, expect, check_names=False)

        result = frame.count(level='a')
        expect = self.frame.count(level=0)
        tm.assert_frame_equal(result, expect, check_names=False)

        series = self.series.copy()
        series.index.names = ['a', 'b']

        result = series.count(level='b')
        expect = self.series.count(level=1)
        tm.assert_series_equal(result, expect, check_names=False)
        assert result.index.name == 'b'

        result = series.count(level='a')
        expect = self.series.count(level=0)
        tm.assert_series_equal(result, expect, check_names=False)
        assert result.index.name == 'a'

        pytest.raises(KeyError, series.count, 'x')
        pytest.raises(KeyError, frame.count, level='x')

    @pytest.mark.parametrize('op', AGG_FUNCTIONS)
    @pytest.mark.parametrize('level', [0, 1])
    @pytest.mark.parametrize('skipna', [True, False])
    @pytest.mark.parametrize('sort', [True, False])
    def test_series_group_min_max(self, op, level, skipna, sort):
        # GH 17537
        grouped = self.series.groupby(level=level, sort=sort)
        # skipna=True
        leftside = grouped.agg(lambda x: getattr(x, op)(skipna=skipna))
        rightside = getattr(self.series, op)(level=level, skipna=skipna)
        if sort:
            rightside = rightside.sort_index(level=level)
        tm.assert_series_equal(leftside, rightside)

    @pytest.mark.parametrize('op', AGG_FUNCTIONS)
    @pytest.mark.parametrize('level', [0, 1])
    @pytest.mark.parametrize('axis', [0, 1])
    @pytest.mark.parametrize('skipna', [True, False])
    @pytest.mark.parametrize('sort', [True, False])
    def test_frame_group_ops(self, op, level, axis, skipna, sort):
        # GH 17537
        self.frame.iloc[1, [1, 2]] = np.nan
        self.frame.iloc[7, [0, 1]] = np.nan

        if axis == 0:
            frame = self.frame
        else:
            frame = self.frame.T

        grouped = frame.groupby(level=level, axis=axis, sort=sort)

        pieces = []

        def aggf(x):
            pieces.append(x)
            return getattr(x, op)(skipna=skipna, axis=axis)

        leftside = grouped.agg(aggf)
        rightside = getattr(frame, op)(level=level, axis=axis,
                                       skipna=skipna)
        if sort:
            rightside = rightside.sort_index(level=level, axis=axis)
            frame = frame.sort_index(level=level, axis=axis)

        # for good measure, groupby detail
        level_index = frame._get_axis(axis).levels[level]

        tm.assert_index_equal(leftside._get_axis(axis), level_index)
        tm.assert_index_equal(rightside._get_axis(axis), level_index)

        tm.assert_frame_equal(leftside, rightside)

    def test_stat_op_corner(self):
        obj = Series([10.0], index=MultiIndex.from_tuples([(2, 3)]))

        result = obj.sum(level=0)
        expected = Series([10.0], index=[2])
        tm.assert_series_equal(result, expected)

    def test_frame_any_all_group(self):
        df = DataFrame(
            {'data': [False, False, True, False, True, False, True]},
            index=[
                ['one', 'one', 'two', 'one', 'two', 'two', 'two'],
                [0, 1, 0, 2, 1, 2, 3]])

        result = df.any(level=0)
        ex = DataFrame({'data': [False, True]}, index=['one', 'two'])
        tm.assert_frame_equal(result, ex)

        result = df.all(level=0)
        ex = DataFrame({'data': [False, False]}, index=['one', 'two'])
        tm.assert_frame_equal(result, ex)

    def test_std_var_pass_ddof(self):
        index = MultiIndex.from_arrays([np.arange(5).repeat(10), np.tile(
            np.arange(10), 5)])
        df = DataFrame(np.random.randn(len(index), 5), index=index)

        for meth in ['var', 'std']:
            ddof = 4
            alt = lambda x: getattr(x, meth)(ddof=ddof)

            result = getattr(df[0], meth)(level=0, ddof=ddof)
            expected = df[0].groupby(level=0).agg(alt)
            tm.assert_series_equal(result, expected)

            result = getattr(df, meth)(level=0, ddof=ddof)
            expected = df.groupby(level=0).agg(alt)
            tm.assert_frame_equal(result, expected)

    def test_frame_series_agg_multiple_levels(self):
        result = self.ymd.sum(level=['year', 'month'])
        expected = self.ymd.groupby(level=['year', 'month']).sum()
        tm.assert_frame_equal(result, expected)

        result = self.ymd['A'].sum(level=['year', 'month'])
        expected = self.ymd['A'].groupby(level=['year', 'month']).sum()
        tm.assert_series_equal(result, expected)

    def test_groupby_multilevel(self):
        result = self.ymd.groupby(level=[0, 1]).mean()

        k1 = self.ymd.index.get_level_values(0)
        k2 = self.ymd.index.get_level_values(1)

        expected = self.ymd.groupby([k1, k2]).mean()

        # TODO groupby with level_values drops names
        tm.assert_frame_equal(result, expected, check_names=False)
        assert result.index.names == self.ymd.index.names[:2]

        result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
        tm.assert_frame_equal(result, result2)

    def test_groupby_multilevel_with_transform(self):
        pass

    def test_multilevel_consolidate(self):
        index = MultiIndex.from_tuples([('foo', 'one'), ('foo', 'two'), (
            'bar', 'one'), ('bar', 'two')])
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df['Totals', ''] = df.sum(1)
        df = df._consolidate()

    def test_ix_preserve_names(self):
        result = self.ymd.loc[2000]
        result2 = self.ymd['A'].loc[2000]
        assert result.index.names == self.ymd.index.names[1:]
        assert result2.index.names == self.ymd.index.names[1:]

        result = self.ymd.loc[2000, 2]
        result2 = self.ymd['A'].loc[2000, 2]
        assert result.index.name == self.ymd.index.names[2]
        assert result2.index.name == self.ymd.index.names[2]

    def test_unstack_preserve_types(self):
        # GH #403
        self.ymd['E'] = 'foo'
        self.ymd['F'] = 2

        unstacked = self.ymd.unstack('month')
        assert unstacked['A', 1].dtype == np.float64
        assert unstacked['E', 1].dtype == np.object_
        assert unstacked['F', 1].dtype == np.float64

    def test_unstack_group_index_overflow(self):
        codes = np.tile(np.arange(500), 2)
        level = np.arange(500)

        index = MultiIndex(levels=[level] * 8 + [[0, 1]],
                           codes=[codes] * 8 + [np.arange(2).repeat(500)])

        s = Series(np.arange(1000), index=index)
        result = s.unstack()
        assert result.shape == (500, 2)

        # test roundtrip
        stacked = result.stack()
        tm.assert_series_equal(s, stacked.reindex(s.index))

        # put it at beginning
        index = MultiIndex(levels=[[0, 1]] + [level] * 8,
                           codes=[np.arange(2).repeat(500)] + [codes] * 8)

        s = Series(np.arange(1000), index=index)
        result = s.unstack(0)
        assert result.shape == (500, 2)

        # put it in middle
        index = MultiIndex(levels=[level] * 4 + [[0, 1]] + [level] * 4,
                           codes=([codes] * 4 + [np.arange(2).repeat(500)] +
                                  [codes] * 4))

        s = Series(np.arange(1000), index=index)
        result = s.unstack(4)
        assert result.shape == (500, 2)

    def test_pyint_engine(self):
        # GH 18519 : when combinations of codes cannot be represented in 64
        # bits, the index underlying the MultiIndex engine works with Python
        # integers, rather than uint64.
        N = 5
        keys = [tuple(l) for l in [[0] * 10 * N,
                                   [1] * 10 * N,
                                   [2] * 10 * N,
                                   [np.nan] * N + [2] * 9 * N,
                                   [0] * N + [2] * 9 * N,
                                   [np.nan] * N + [2] * 8 * N + [0] * N]]
        # Each level contains 4 elements (including NaN), so it is represented
        # in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a
        # 64 bit engine and truncating the first levels, the fourth and fifth
        # keys would collide; if truncating the last levels, the fifth and
        # sixth; if rotating bits rather than shifting, the third and fifth.

        for idx in range(len(keys)):
            index = MultiIndex.from_tuples(keys)
            assert index.get_loc(keys[idx]) == idx

            expected = np.arange(idx + 1, dtype=np.intp)
            result = index.get_indexer([keys[i] for i in expected])
            tm.assert_numpy_array_equal(result, expected)

        # With missing key:
        idces = range(len(keys))
        expected = np.array([-1] + list(idces), dtype=np.intp)
        missing = tuple([0, 1] * 5 * N)
        result = index.get_indexer([missing] + [keys[i] for i in idces])
        tm.assert_numpy_array_equal(result, expected)

    def test_to_html(self):
        self.ymd.columns.name = 'foo'
        self.ymd.to_html()
        self.ymd.T.to_html()

    def test_level_with_tuples(self):
        index = MultiIndex(levels=[[('foo', 'bar', 0), ('foo', 'baz', 0), (
            'foo', 'qux', 0)], [0, 1]],
            codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[('foo', 'bar', 0)]
        result2 = series.loc[('foo', 'bar', 0)]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result2, expected)

        pytest.raises(KeyError, series.__getitem__, (('foo', 'bar', 0), 2))

        result = frame.loc[('foo', 'bar', 0)]
        result2 = frame.xs(('foo', 'bar', 0))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result2, expected)

        index = MultiIndex(levels=[[('foo', 'bar'), ('foo', 'baz'), (
            'foo', 'qux')], [0, 1]],
            codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[('foo', 'bar')]
        result2 = series.loc[('foo', 'bar')]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result2, expected)

        result = frame.loc[('foo', 'bar')]
        result2 = frame.xs(('foo', 'bar'))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result2, expected)

    def test_mixed_depth_drop(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = sorted(zip(*arrays))
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        result = df.drop('a', axis=1)
        expected = df.drop([('a', '', '')], axis=1)
        tm.assert_frame_equal(expected, result)

        result = df.drop(['top'], axis=1)
        expected = df.drop([('top', 'OD', 'wx')], axis=1)
        expected = expected.drop([('top', 'OD', 'wy')], axis=1)
        tm.assert_frame_equal(expected, result)

        result = df.drop(('top', 'OD', 'wx'), axis=1)
        expected = df.drop([('top', 'OD', 'wx')], axis=1)
        tm.assert_frame_equal(expected, result)

        expected = df.drop([('top', 'OD', 'wy')], axis=1)
        expected = df.drop('top', axis=1)

        result = df.drop('result1', level=1, axis=1)
        expected = df.drop([('routine1', 'result1', ''),
                            ('routine2', 'result1', '')], axis=1)
        tm.assert_frame_equal(expected, result)

    def test_drop_nonunique(self):
        df = DataFrame([["x-a", "x", "a", 1.5], ["x-a", "x", "a", 1.2],
                        ["z-c", "z", "c", 3.1], ["x-a", "x", "a", 4.1],
                        ["x-b", "x", "b", 5.1], ["x-b", "x", "b", 4.1],
                        ["x-b", "x", "b", 2.2],
                        ["y-a", "y", "a", 1.2], ["z-b", "z", "b", 2.1]],
                       columns=["var1", "var2", "var3", "var4"])

        grp_size = df.groupby("var1").size()
        drop_idx = grp_size.loc[grp_size == 1]

        idf = df.set_index(["var1", "var2", "var3"])

        # it works! #2101
        result = idf.drop(drop_idx.index, level=0).reset_index()
        expected = df[-df.var1.isin(drop_idx.index)]

        result.index = expected.index

        tm.assert_frame_equal(result, expected)

    def test_mixed_depth_pop(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = sorted(zip(*arrays))
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        df1 = df.copy()
        df2 = df.copy()
        result = df1.pop('a')
        expected = df2.pop(('a', '', ''))
        tm.assert_series_equal(expected, result, check_names=False)
        tm.assert_frame_equal(df1, df2)
        assert result.name == 'a'

        expected = df1['top']
        df1 = df1.drop(['top'], axis=1)
        result = df2.pop('top')
        tm.assert_frame_equal(expected, result)
        tm.assert_frame_equal(df1, df2)

    def test_reindex_level_partial_selection(self):
        result = self.frame.reindex(['foo', 'qux'], level=0)
        expected = self.frame.iloc[[0, 1, 2, 7, 8, 9]]
        tm.assert_frame_equal(result, expected)

        result = self.frame.T.reindex(['foo', 'qux'], axis=1, level=0)
        tm.assert_frame_equal(result, expected.T)

        result = self.frame.loc[['foo', 'qux']]
        tm.assert_frame_equal(result, expected)

        result = self.frame['A'].loc[['foo', 'qux']]
        tm.assert_series_equal(result, expected['A'])

        result = self.frame.T.loc[:, ['foo', 'qux']]
        tm.assert_frame_equal(result, expected.T)

    def test_drop_level(self):
        result = self.frame.drop(['bar', 'qux'], level='first')
        expected = self.frame.iloc[[0, 1, 2, 5, 6]]
        tm.assert_frame_equal(result, expected)

        result = self.frame.drop(['two'], level='second')
        expected = self.frame.iloc[[0, 2, 3, 6, 7, 9]]
        tm.assert_frame_equal(result, expected)

        result = self.frame.T.drop(['bar', 'qux'], axis=1, level='first')
        expected = self.frame.iloc[[0, 1, 2, 5, 6]].T
        tm.assert_frame_equal(result, expected)

        result = self.frame.T.drop(['two'], axis=1, level='second')
        expected = self.frame.iloc[[0, 2, 3, 6, 7, 9]].T
        tm.assert_frame_equal(result, expected)

    def test_drop_level_nonunique_datetime(self):
        # GH 12701
        idx = Index([2, 3, 4, 4, 5], name='id')
        idxdt = pd.to_datetime(['201603231400',
                                '201603231500',
                                '201603231600',
                                '201603231600',
                                '201603231700'])
        df = DataFrame(np.arange(10).reshape(5, 2),
                       columns=list('ab'), index=idx)
        df['tstamp'] = idxdt
        df = df.set_index('tstamp', append=True)
        ts = Timestamp('201603231600')
        assert df.index.is_unique is False

        result = df.drop(ts, level='tstamp')
        expected = df.loc[idx != 4]
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize('box', [Series, DataFrame])
    def test_drop_tz_aware_timestamp_across_dst(self, box):
        # GH 21761
        start = Timestamp('2017-10-29', tz='Europe/Berlin')
        end = Timestamp('2017-10-29 04:00:00', tz='Europe/Berlin')
        index = pd.date_range(start, end, freq='15min')
        data = box(data=[1] * len(index), index=index)
        result = data.drop(start)
        expected_start = Timestamp('2017-10-29 00:15:00', tz='Europe/Berlin')
        expected_idx = pd.date_range(expected_start, end, freq='15min')
        expected = box(data=[1] * len(expected_idx), index=expected_idx)
        tm.assert_equal(result, expected)

    def test_drop_preserve_names(self):
        index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
                                        [1, 2, 3, 1, 2, 3]],
                                       names=['one', 'two'])

        df = DataFrame(np.random.randn(6, 3), index=index)

        result = df.drop([(0, 2)])
        assert result.index.names == ('one', 'two')

    def test_unicode_repr_issues(self):
        levels = [Index([u('a/\u03c3'), u('b/\u03c3'), u('c/\u03c3')]),
                  Index([0, 1])]
        codes = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)]
        index = MultiIndex(levels=levels, codes=codes)

        repr(index.levels)

        # NumPy bug
        # repr(index.get_level_values(1))

    def test_unicode_repr_level_names(self):
        index = MultiIndex.from_tuples([(0, 0), (1, 1)],
                                       names=[u('\u0394'), 'i1'])

        s = Series(lrange(2), index=index)
        df = DataFrame(np.random.randn(2, 4), index=index)
        repr(s)
        repr(df)

    def test_join_segfault(self):
        # 1532
        df1 = DataFrame({'a': [1, 1], 'b': [1, 2], 'x': [1, 2]})
        df2 = DataFrame({'a': [2, 2], 'b': [1, 2], 'y': [1, 2]})
        df1 = df1.set_index(['a', 'b'])
        df2 = df2.set_index(['a', 'b'])
        # it works!
        for how in ['left', 'right', 'outer']:
            df1.join(df2, how=how)

    def test_frame_dict_constructor_empty_series(self):
        s1 = Series([
            1, 2, 3, 4
        ], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)]))
        s2 = Series([
            1, 2, 3, 4
        ], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)]))
        s3 = Series()

        # it works!
        DataFrame({'foo': s1, 'bar': s2, 'baz': s3})
        DataFrame.from_dict({'foo': s1, 'baz': s3, 'bar': s2})

    def test_multiindex_na_repr(self):
        # only an issue with long columns

        from numpy import nan
        df3 = DataFrame({
            'A' * 30: {('A', 'A0006000', 'nuit'): 'A0006000'},
            'B' * 30: {('A', 'A0006000', 'nuit'): nan},
            'C' * 30: {('A', 'A0006000', 'nuit'): nan},
            'D' * 30: {('A', 'A0006000', 'nuit'): nan},
            'E' * 30: {('A', 'A0006000', 'nuit'): 'A'},
            'F' * 30: {('A', 'A0006000', 'nuit'): nan},
        })

        idf = df3.set_index(['A' * 30, 'C' * 30])
        repr(idf)

    def test_assign_index_sequences(self):
        # #2200
        df = DataFrame({"a": [1, 2, 3],
                        "b": [4, 5, 6],
                        "c": [7, 8, 9]}).set_index(["a", "b"])
        index = list(df.index)
        index[0] = ("faz", "boo")
        df.index = index
        repr(df)

        # this travels an improper code path
        index[0] = ["faz", "boo"]
        df.index = index
        repr(df)

    def test_tuples_have_na(self):
        index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
                           codes=[[1, 1, 1, 1, -1, 0, 0, 0],
                                  [0, 1, 2, 3, 0, 1, 2, 3]])

        assert isna(index[4][0])
        assert isna(index.values[4][0])

    def test_duplicate_groupby_issues(self):
        idx_tp = [('600809', '20061231'), ('600809', '20070331'),
                  ('600809', '20070630'), ('600809', '20070331')]
        dt = ['demo', 'demo', 'demo', 'demo']

        idx = MultiIndex.from_tuples(idx_tp, names=['STK_ID', 'RPT_Date'])
        s = Series(dt, index=idx)

        result = s.groupby(s.index).first()
        assert len(result) == 3

    def test_duplicate_mi(self):
        # GH 4516
        df = DataFrame([['foo', 'bar', 1.0, 1], ['foo', 'bar', 2.0, 2],
                        ['bah', 'bam', 3.0, 3],
                        ['bah', 'bam', 4.0, 4], ['foo', 'bar', 5.0, 5],
                        ['bah', 'bam', 6.0, 6]],
                       columns=list('ABCD'))
        df = df.set_index(['A', 'B'])
        df = df.sort_index(level=0)
        expected = DataFrame([['foo', 'bar', 1.0, 1], ['foo', 'bar', 2.0, 2],
                              ['foo', 'bar', 5.0, 5]],
                             columns=list('ABCD')).set_index(['A', 'B'])
        result = df.loc[('foo', 'bar')]
        tm.assert_frame_equal(result, expected)

    def test_duplicated_drop_duplicates(self):
        # GH 4060
        idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))

        expected = np.array(
            [False, False, False, True, False, False], dtype=bool)
        duplicated = idx.duplicated()
        tm.assert_numpy_array_equal(duplicated, expected)
        assert duplicated.dtype == bool
        expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
        tm.assert_index_equal(idx.drop_duplicates(), expected)

        expected = np.array([True, False, False, False, False, False])
        duplicated = idx.duplicated(keep='last')
        tm.assert_numpy_array_equal(duplicated, expected)
        assert duplicated.dtype == bool
        expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
        tm.assert_index_equal(idx.drop_duplicates(keep='last'), expected)

        expected = np.array([True, False, False, True, False, False])
        duplicated = idx.duplicated(keep=False)
        tm.assert_numpy_array_equal(duplicated, expected)
        assert duplicated.dtype == bool
        expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
        tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)

    def test_multiindex_set_index(self):
        # segfault in #3308
        d = {'t1': [2, 2.5, 3], 't2': [4, 5, 6]}
        df = DataFrame(d)
        tuples = [(0, 1), (0, 2), (1, 2)]
        df['tuples'] = tuples

        index = MultiIndex.from_tuples(df['tuples'])
        # it works!
        df.set_index(index)

    def test_datetimeindex(self):
        idx1 = pd.DatetimeIndex(
            ['2013-04-01 9:00', '2013-04-02 9:00', '2013-04-03 9:00'
             ] * 2, tz='Asia/Tokyo')
        idx2 = pd.date_range('2010/01/01', periods=6, freq='M',
                             tz='US/Eastern')
        idx = MultiIndex.from_arrays([idx1, idx2])

        expected1 = pd.DatetimeIndex(['2013-04-01 9:00', '2013-04-02 9:00',
                                      '2013-04-03 9:00'], tz='Asia/Tokyo')

        tm.assert_index_equal(idx.levels[0], expected1)
        tm.assert_index_equal(idx.levels[1], idx2)

        # from datetime combos
        # GH 7888
        date1 = datetime.date.today()
        date2 = datetime.datetime.today()
        date3 = Timestamp.today()

        for d1, d2 in itertools.product(
                [date1, date2, date3], [date1, date2, date3]):
            index = MultiIndex.from_product([[d1], [d2]])
            assert isinstance(index.levels[0], pd.DatetimeIndex)
            assert isinstance(index.levels[1], pd.DatetimeIndex)

    def test_constructor_with_tz(self):

        index = pd.DatetimeIndex(['2013/01/01 09:00', '2013/01/02 09:00'],
                                 name='dt1', tz='US/Pacific')
        columns = pd.DatetimeIndex(['2014/01/01 09:00', '2014/01/02 09:00'],
                                   name='dt2', tz='Asia/Tokyo')

        result = MultiIndex.from_arrays([index, columns])
        tm.assert_index_equal(result.levels[0], index)
        tm.assert_index_equal(result.levels[1], columns)

        result = MultiIndex.from_arrays([Series(index), Series(columns)])
        tm.assert_index_equal(result.levels[0], index)
        tm.assert_index_equal(result.levels[1], columns)

    def test_set_index_datetime(self):
        # GH 3950
        df = DataFrame(
            {'label': ['a', 'a', 'a', 'b', 'b', 'b'],
             'datetime': ['2011-07-19 07:00:00', '2011-07-19 08:00:00',
                          '2011-07-19 09:00:00', '2011-07-19 07:00:00',
                          '2011-07-19 08:00:00', '2011-07-19 09:00:00'],
             'value': range(6)})
        df.index = pd.to_datetime(df.pop('datetime'), utc=True)
        df.index = df.index.tz_convert('US/Pacific')

        expected = pd.DatetimeIndex(['2011-07-19 07:00:00',
                                     '2011-07-19 08:00:00',
                                     '2011-07-19 09:00:00'], name='datetime')
        expected = expected.tz_localize('UTC').tz_convert('US/Pacific')

        df = df.set_index('label', append=True)
        tm.assert_index_equal(df.index.levels[0], expected)
        tm.assert_index_equal(df.index.levels[1],
                              Index(['a', 'b'], name='label'))

        df = df.swaplevel(0, 1)
        tm.assert_index_equal(df.index.levels[0],
                              Index(['a', 'b'], name='label'))
        tm.assert_index_equal(df.index.levels[1], expected)

        df = DataFrame(np.random.random(6))
        idx1 = pd.DatetimeIndex(['2011-07-19 07:00:00', '2011-07-19 08:00:00',
                                 '2011-07-19 09:00:00', '2011-07-19 07:00:00',
                                 '2011-07-19 08:00:00', '2011-07-19 09:00:00'],
                                tz='US/Eastern')
        idx2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-01 09:00',
                                 '2012-04-01 09:00', '2012-04-02 09:00',
                                 '2012-04-02 09:00', '2012-04-02 09:00'],
                                tz='US/Eastern')
        idx3 = pd.date_range('2011-01-01 09:00', periods=6, tz='Asia/Tokyo')

        df = df.set_index(idx1)
        df = df.set_index(idx2, append=True)
        df = df.set_index(idx3, append=True)

        expected1 = pd.DatetimeIndex(['2011-07-19 07:00:00',
                                      '2011-07-19 08:00:00',
                                      '2011-07-19 09:00:00'], tz='US/Eastern')
        expected2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-02 09:00'],
                                     tz='US/Eastern')

        tm.assert_index_equal(df.index.levels[0], expected1)
        tm.assert_index_equal(df.index.levels[1], expected2)
        tm.assert_index_equal(df.index.levels[2], idx3)

        # GH 7092
        tm.assert_index_equal(df.index.get_level_values(0), idx1)
        tm.assert_index_equal(df.index.get_level_values(1), idx2)
        tm.assert_index_equal(df.index.get_level_values(2), idx3)

    def test_reset_index_datetime(self):
        # GH 3950
        for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern']:
            idx1 = pd.date_range('1/1/2011', periods=5, freq='D', tz=tz,
                                 name='idx1')
            idx2 = Index(range(5), name='idx2', dtype='int64')
            idx = MultiIndex.from_arrays([idx1, idx2])
            df = DataFrame(
                {'a': np.arange(5, dtype='int64'),
                 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

            expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
                                           datetime.datetime(2011, 1, 2),
                                           datetime.datetime(2011, 1, 3),
                                           datetime.datetime(2011, 1, 4),
                                           datetime.datetime(2011, 1, 5)],
                                  'idx2': np.arange(5, dtype='int64'),
                                  'a': np.arange(5, dtype='int64'),
                                  'b': ['A', 'B', 'C', 'D', 'E']},
                                 columns=['idx1', 'idx2', 'a', 'b'])
            expected['idx1'] = expected['idx1'].apply(
                lambda d: Timestamp(d, tz=tz))

            tm.assert_frame_equal(df.reset_index(), expected)

            idx3 = pd.date_range('1/1/2012', periods=5, freq='MS',
                                 tz='Europe/Paris', name='idx3')
            idx = MultiIndex.from_arrays([idx1, idx2, idx3])
            df = DataFrame(
                {'a': np.arange(5, dtype='int64'),
                 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

            expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
                                           datetime.datetime(2011, 1, 2),
                                           datetime.datetime(2011, 1, 3),
                                           datetime.datetime(2011, 1, 4),
                                           datetime.datetime(2011, 1, 5)],
                                  'idx2': np.arange(5, dtype='int64'),
                                  'idx3': [datetime.datetime(2012, 1, 1),
                                           datetime.datetime(2012, 2, 1),
                                           datetime.datetime(2012, 3, 1),
                                           datetime.datetime(2012, 4, 1),
                                           datetime.datetime(2012, 5, 1)],
                                  'a': np.arange(5, dtype='int64'),
                                  'b': ['A', 'B', 'C', 'D', 'E']},
                                 columns=['idx1', 'idx2', 'idx3', 'a', 'b'])
            expected['idx1'] = expected['idx1'].apply(
                lambda d: Timestamp(d, tz=tz))
            expected['idx3'] = expected['idx3'].apply(
                lambda d: Timestamp(d, tz='Europe/Paris'))
            tm.assert_frame_equal(df.reset_index(), expected)

            # GH 7793
            idx = MultiIndex.from_product([['a', 'b'], pd.date_range(
                '20130101', periods=3, tz=tz)])
            df = DataFrame(
                np.arange(6, dtype='int64').reshape(
                    6, 1), columns=['a'], index=idx)

            expected = DataFrame({'level_0': 'a a a b b b'.split(),
                                  'level_1': [
                                  datetime.datetime(2013, 1, 1),
                                  datetime.datetime(2013, 1, 2),
                                  datetime.datetime(2013, 1, 3)] * 2,
                                  'a': np.arange(6, dtype='int64')},
                                 columns=['level_0', 'level_1', 'a'])
            expected['level_1'] = expected['level_1'].apply(
                lambda d: Timestamp(d, freq='D', tz=tz))
            tm.assert_frame_equal(df.reset_index(), expected)

    def test_reset_index_period(self):
        # GH 7746
        idx = MultiIndex.from_product(
            [pd.period_range('20130101', periods=3, freq='M'), list('abc')],
            names=['month', 'feature'])

        df = DataFrame(np.arange(9, dtype='int64').reshape(-1, 1),
                       index=idx, columns=['a'])
        expected = DataFrame({
            'month': ([pd.Period('2013-01', freq='M')] * 3 +
                      [pd.Period('2013-02', freq='M')] * 3 +
                      [pd.Period('2013-03', freq='M')] * 3),
            'feature': ['a', 'b', 'c'] * 3,
            'a': np.arange(9, dtype='int64')
        }, columns=['month', 'feature', 'a'])
        tm.assert_frame_equal(df.reset_index(), expected)

    def test_reset_index_multiindex_columns(self):
        levels = [['A', ''], ['B', 'b']]
        df = DataFrame([[0, 2], [1, 3]],
                       columns=MultiIndex.from_tuples(levels))
        result = df[['B']].rename_axis('A').reset_index()
        tm.assert_frame_equal(result, df)

        # gh-16120: already existing column
        with pytest.raises(ValueError,
                           match=(r"cannot insert \('A', ''\), "
                                  "already exists")):
            df.rename_axis('A').reset_index()

        # gh-16164: multiindex (tuple) full key
        result = df.set_index([('A', '')]).reset_index()
        tm.assert_frame_equal(result, df)

        # with additional (unnamed) index level
        idx_col = DataFrame([[0], [1]],
                            columns=MultiIndex.from_tuples([('level_0', '')]))
        expected = pd.concat([idx_col, df[[('B', 'b'), ('A', '')]]], axis=1)
        result = df.set_index([('B', 'b')], append=True).reset_index()
        tm.assert_frame_equal(result, expected)

        # with index name which is a too long tuple...
        with pytest.raises(ValueError,
                           match=("Item must have length equal "
                                  "to number of levels.")):
            df.rename_axis([('C', 'c', 'i')]).reset_index()

        # or too short...
        levels = [['A', 'a', ''], ['B', 'b', 'i']]
        df2 = DataFrame([[0, 2], [1, 3]],
                        columns=MultiIndex.from_tuples(levels))
        idx_col = DataFrame([[0], [1]],
                            columns=MultiIndex.from_tuples([('C', 'c', 'ii')]))
        expected = pd.concat([idx_col, df2], axis=1)
        result = df2.rename_axis([('C', 'c')]).reset_index(col_fill='ii')
        tm.assert_frame_equal(result, expected)

        # ... which is incompatible with col_fill=None
        with pytest.raises(ValueError,
                           match=("col_fill=None is incompatible with "
                                  r"incomplete column name \('C', 'c'\)")):
            df2.rename_axis([('C', 'c')]).reset_index(col_fill=None)

        # with col_level != 0
        result = df2.rename_axis([('c', 'ii')]).reset_index(col_level=1,
                                                            col_fill='C')
        tm.assert_frame_equal(result, expected)

    def test_set_index_period(self):
        # GH 6631
        df = DataFrame(np.random.random(6))
        idx1 = pd.period_range('2011-01-01', periods=3, freq='M')
        idx1 = idx1.append(idx1)
        idx2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')
        idx2 = idx2.append(idx2).append(idx2)
        idx3 = pd.period_range('2005', periods=6, freq='A')

        df = df.set_index(idx1)
        df = df.set_index(idx2, append=True)
        df = df.set_index(idx3, append=True)

        expected1 = pd.period_range('2011-01-01', periods=3, freq='M')
        expected2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')

        tm.assert_index_equal(df.index.levels[0], expected1)
        tm.assert_index_equal(df.index.levels[1], expected2)
        tm.assert_index_equal(df.index.levels[2], idx3)

        tm.assert_index_equal(df.index.get_level_values(0), idx1)
        tm.assert_index_equal(df.index.get_level_values(1), idx2)
        tm.assert_index_equal(df.index.get_level_values(2), idx3)

    def test_repeat(self):
        # GH 9361
        # fixed by # GH 7891
        m_idx = MultiIndex.from_tuples([(1, 2), (3, 4), (5, 6), (7, 8)])
        data = ['a', 'b', 'c', 'd']
        m_df = Series(data, index=m_idx)
        assert m_df.repeat(3).shape == (3 * len(data), )


class TestSorted(Base):
    """ everything you wanted to test about sorting """

    def test_sort_index_preserve_levels(self):
        result = self.frame.sort_index()
        assert result.index.names == self.frame.index.names

    def test_sorting_repr_8017(self):

        np.random.seed(0)
        data = np.random.randn(3, 4)

        for gen, extra in [([1., 3., 2., 5.], 4.), ([1, 3, 2, 5], 4),
                           ([Timestamp('20130101'), Timestamp('20130103'),
                             Timestamp('20130102'), Timestamp('20130105')],
                            Timestamp('20130104')),
                           (['1one', '3one', '2one', '5one'], '4one')]:
            columns = MultiIndex.from_tuples([('red', i) for i in gen])
            df = DataFrame(data, index=list('def'), columns=columns)
            df2 = pd.concat([df,
                             DataFrame('world', index=list('def'),
                                       columns=MultiIndex.from_tuples(
                                           [('red', extra)]))], axis=1)

            # check that the repr is good
            # make sure that we have a correct sparsified repr
            # e.g. only 1 header of read
            assert str(df2).splitlines()[0].split() == ['red']

            # GH 8017
            # sorting fails after columns added

            # construct single-dtype then sort
            result = df.copy().sort_index(axis=1)
            expected = df.iloc[:, [0, 2, 1, 3]]
            tm.assert_frame_equal(result, expected)

            result = df2.sort_index(axis=1)
            expected = df2.iloc[:, [0, 2, 1, 4, 3]]
            tm.assert_frame_equal(result, expected)

            # setitem then sort
            result = df.copy()
            result[('red', extra)] = 'world'

            result = result.sort_index(axis=1)
            tm.assert_frame_equal(result, expected)

    def test_sort_index_level(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))

        # axis=1

        # series
        a_sorted = self.frame['A'].sort_index(level=0)

        # preserve names
        assert a_sorted.index.names == self.frame.index.names

        # inplace
        rs = self.frame.copy()
        rs.sort_index(level=0, inplace=True)
        tm.assert_frame_equal(rs, self.frame.sort_index(level=0))

    def test_sort_index_level_large_cardinality(self):

        # #2684 (int64)
        index = MultiIndex.from_arrays([np.arange(4000)] * 3)
        df = DataFrame(np.random.randn(4000), index=index, dtype=np.int64)

        # it works!
        result = df.sort_index(level=0)
        assert result.index.lexsort_depth == 3

        # #2684 (int32)
        index = MultiIndex.from_arrays([np.arange(4000)] * 3)
        df = DataFrame(np.random.randn(4000), index=index, dtype=np.int32)

        # it works!
        result = df.sort_index(level=0)
        assert (result.dtypes.values == df.dtypes.values).all()
        assert result.index.lexsort_depth == 3

    def test_sort_index_level_by_name(self):
        self.frame.index.names = ['first', 'second']
        result = self.frame.sort_index(level='second')
        expected = self.frame.sort_index(level=1)
        tm.assert_frame_equal(result, expected)

    def test_sort_index_level_mixed(self):
        sorted_before = self.frame.sort_index(level=1)

        df = self.frame.copy()
        df['foo'] = 'bar'
        sorted_after = df.sort_index(level=1)
        tm.assert_frame_equal(sorted_before,
                              sorted_after.drop(['foo'], axis=1))

        dft = self.frame.T
        sorted_before = dft.sort_index(level=1, axis=1)
        dft['foo', 'three'] = 'bar'

        sorted_after = dft.sort_index(level=1, axis=1)
        tm.assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
                              sorted_after.drop([('foo', 'three')], axis=1))

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels,
                           codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        assert index.is_lexsorted()

        index = MultiIndex(levels=levels,
                           codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
        assert not index.is_lexsorted()

        index = MultiIndex(levels=levels,
                           codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
        assert not index.is_lexsorted()
        assert index.lexsort_depth == 0

    def test_sort_index_and_reconstruction(self):

        # 15622
        # lexsortedness should be identical
        # across MultiIndex consruction methods

        df = DataFrame([[1, 1], [2, 2]], index=list('ab'))
        expected = DataFrame([[1, 1], [2, 2], [1, 1], [2, 2]],
                             index=MultiIndex.from_tuples([(0.5, 'a'),
                                                           (0.5, 'b'),
                                                           (0.8, 'a'),
                                                           (0.8, 'b')]))
        assert expected.index.is_lexsorted()

        result = DataFrame(
            [[1, 1], [2, 2], [1, 1], [2, 2]],
            index=MultiIndex.from_product([[0.5, 0.8], list('ab')]))
        result = result.sort_index()
        assert result.index.is_lexsorted()
        assert result.index.is_monotonic

        tm.assert_frame_equal(result, expected)

        result = DataFrame(
            [[1, 1], [2, 2], [1, 1], [2, 2]],
            index=MultiIndex(levels=[[0.5, 0.8], ['a', 'b']],
                             codes=[[0, 0, 1, 1], [0, 1, 0, 1]]))
        result = result.sort_index()
        assert result.index.is_lexsorted()

        tm.assert_frame_equal(result, expected)

        concatted = pd.concat([df, df], keys=[0.8, 0.5])
        result = concatted.sort_index()

        assert result.index.is_lexsorted()
        assert result.index.is_monotonic

        tm.assert_frame_equal(result, expected)

        # 14015
        df = DataFrame([[1, 2], [6, 7]],
                       columns=MultiIndex.from_tuples(
                           [(0, '20160811 12:00:00'),
                            (0, '20160809 12:00:00')],
                           names=['l1', 'Date']))

        df.columns.set_levels(pd.to_datetime(df.columns.levels[1]),
                              level=1,
                              inplace=True)
        assert not df.columns.is_lexsorted()
        assert not df.columns.is_monotonic
        result = df.sort_index(axis=1)
        assert result.columns.is_lexsorted()
        assert result.columns.is_monotonic
        result = df.sort_index(axis=1, level=1)
        assert result.columns.is_lexsorted()
        assert result.columns.is_monotonic

    def test_sort_index_and_reconstruction_doc_example(self):
        # doc example
        df = DataFrame({'value': [1, 2, 3, 4]},
                       index=MultiIndex(
                           levels=[['a', 'b'], ['bb', 'aa']],
                           codes=[[0, 0, 1, 1], [0, 1, 0, 1]]))
        assert df.index.is_lexsorted()
        assert not df.index.is_monotonic

        # sort it
        expected = DataFrame({'value': [2, 1, 4, 3]},
                             index=MultiIndex(
                                 levels=[['a', 'b'], ['aa', 'bb']],
                                 codes=[[0, 0, 1, 1], [0, 1, 0, 1]]))
        result = df.sort_index()
        assert result.index.is_lexsorted()
        assert result.index.is_monotonic

        tm.assert_frame_equal(result, expected)

        # reconstruct
        result = df.sort_index().copy()
        result.index = result.index._sort_levels_monotonic()
        assert result.index.is_lexsorted()
        assert result.index.is_monotonic

        tm.assert_frame_equal(result, expected)

    def test_sort_index_reorder_on_ops(self):
        # 15687
        df = DataFrame(
            np.random.randn(8, 2),
            index=MultiIndex.from_product(
                [['a', 'b'], ['big', 'small'], ['red', 'blu']],
                names=['letter', 'size', 'color']),
            columns=['near', 'far'])
        df = df.sort_index()

        def my_func(group):
            group.index = ['newz', 'newa']
            return group

        result = df.groupby(level=['letter', 'size']).apply(
            my_func).sort_index()
        expected = MultiIndex.from_product(
            [['a', 'b'], ['big', 'small'], ['newa', 'newz']],
            names=['letter', 'size', None])

        tm.assert_index_equal(result.index, expected)

    def test_sort_non_lexsorted(self):
        # degenerate case where we sort but don't
        # have a satisfying result :<
        # GH 15797
        idx = MultiIndex([['A', 'B', 'C'],
                          ['c', 'b', 'a']],
                         [[0, 1, 2, 0, 1, 2],
                          [0, 2, 1, 1, 0, 2]])

        df = DataFrame({'col': range(len(idx))},
                       index=idx,
                       dtype='int64')
        assert df.index.is_lexsorted() is False
        assert df.index.is_monotonic is False

        sorted = df.sort_index()
        assert sorted.index.is_lexsorted() is True
        assert sorted.index.is_monotonic is True

        expected = DataFrame(
            {'col': [1, 4, 5, 2]},
            index=MultiIndex.from_tuples([('B', 'a'), ('B', 'c'),
                                          ('C', 'a'), ('C', 'b')]),
            dtype='int64')
        result = sorted.loc[pd.IndexSlice['B':'C', 'a':'c'], :]
        tm.assert_frame_equal(result, expected)

    def test_sort_index_nan(self):
        # GH 14784
        # incorrect sorting w.r.t. nans
        tuples = [[12, 13], [np.nan, np.nan], [np.nan, 3], [1, 2]]
        mi = MultiIndex.from_tuples(tuples)

        df = DataFrame(np.arange(16).reshape(4, 4),
                       index=mi, columns=list('ABCD'))
        s = Series(np.arange(4), index=mi)

        df2 = DataFrame({
            'date': pd.to_datetime([
                '20121002', '20121007', '20130130', '20130202', '20130305',
                '20121002', '20121207', '20130130', '20130202', '20130305',
                '20130202', '20130305'
            ]),
            'user_id': [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
            'whole_cost': [1790, np.nan, 280, 259, np.nan, 623, 90, 312,
                           np.nan, 301, 359, 801],
            'cost': [12, 15, 10, 24, 39, 1, 0, np.nan, 45, 34, 1, 12]
        }).set_index(['date', 'user_id'])

        # sorting frame, default nan position is last
        result = df.sort_index()
        expected = df.iloc[[3, 0, 2, 1], :]
        tm.assert_frame_equal(result, expected)

        # sorting frame, nan position last
        result = df.sort_index(na_position='last')
        expected = df.iloc[[3, 0, 2, 1], :]
        tm.assert_frame_equal(result, expected)

        # sorting frame, nan position first
        result = df.sort_index(na_position='first')
        expected = df.iloc[[1, 2, 3, 0], :]
        tm.assert_frame_equal(result, expected)

        # sorting frame with removed rows
        result = df2.dropna().sort_index()
        expected = df2.sort_index().dropna()
        tm.assert_frame_equal(result, expected)

        # sorting series, default nan position is last
        result = s.sort_index()
        expected = s.iloc[[3, 0, 2, 1]]
        tm.assert_series_equal(result, expected)

        # sorting series, nan position last
        result = s.sort_index(na_position='last')
        expected = s.iloc[[3, 0, 2, 1]]
        tm.assert_series_equal(result, expected)

        # sorting series, nan position first
        result = s.sort_index(na_position='first')
        expected = s.iloc[[1, 2, 3, 0]]
        tm.assert_series_equal(result, expected)

    def test_sort_ascending_list(self):
        # GH: 16934

        # Set up a Series with a three level MultiIndex
        arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'],
                  [4, 3, 2, 1, 4, 3, 2, 1]]
        tuples = lzip(*arrays)
        mi = MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
        s = Series(range(8), index=mi)

        # Sort with boolean ascending
        result = s.sort_index(level=['third', 'first'], ascending=False)
        expected = s.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]
        tm.assert_series_equal(result, expected)

        # Sort with list of boolean ascending
        result = s.sort_index(level=['third', 'first'],
                              ascending=[False, True])
        expected = s.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
        tm.assert_series_equal(result, expected)