test_indexing.py

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

from __future__ import print_function

from datetime import datetime, date, timedelta, time

from pandas.compat import map, zip, range, lrange, lzip, long
from pandas import compat

from numpy import nan
from numpy.random import randn
import numpy as np

import pandas.core.common as com
from pandas import (DataFrame, Index, Series, notnull, isnull,
                    MultiIndex, DatetimeIndex, Timestamp,
                    date_range)
import pandas as pd

from pandas.tseries.offsets import BDay
from pandas.types.common import (is_float_dtype,
                                 is_integer,
                                 is_scalar)
from pandas.util.testing import (assert_almost_equal,
                                 assert_numpy_array_equal,
                                 assert_series_equal,
                                 assert_frame_equal,
                                 assertRaisesRegexp,
                                 assertRaises)
from pandas.core.indexing import IndexingError

import pandas.util.testing as tm

from pandas.tests.frame.common import TestData


class TestDataFrameIndexing(tm.TestCase, TestData):

    _multiprocess_can_split_ = True

    def test_getitem(self):
        # slicing
        sl = self.frame[:20]
        self.assertEqual(20, len(sl.index))

        # column access

        for _, series in compat.iteritems(sl):
            self.assertEqual(20, len(series.index))
            self.assertTrue(tm.equalContents(series.index, sl.index))

        for key, _ in compat.iteritems(self.frame._series):
            self.assertIsNotNone(self.frame[key])

        self.assertNotIn('random', self.frame)
        with assertRaisesRegexp(KeyError, 'random'):
            self.frame['random']

        df = self.frame.copy()
        df['$10'] = randn(len(df))
        ad = randn(len(df))
        df['@awesome_domain'] = ad
        self.assertRaises(KeyError, df.__getitem__, 'df["$10"]')
        res = df['@awesome_domain']
        assert_numpy_array_equal(ad, res.values)

    def test_getitem_dupe_cols(self):
        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=['a', 'a', 'b'])
        try:
            df[['baf']]
        except KeyError:
            pass
        else:
            self.fail("Dataframe failed to raise KeyError")

    def test_get(self):
        b = self.frame.get('B')
        assert_series_equal(b, self.frame['B'])

        self.assertIsNone(self.frame.get('foo'))
        assert_series_equal(self.frame.get('foo', self.frame['B']),
                            self.frame['B'])
        # None
        # GH 5652
        for df in [DataFrame(), DataFrame(columns=list('AB')),
                   DataFrame(columns=list('AB'), index=range(3))]:
            result = df.get(None)
            self.assertIsNone(result)

    def test_getitem_iterator(self):
        idx = iter(['A', 'B', 'C'])
        result = self.frame.ix[:, idx]
        expected = self.frame.ix[:, ['A', 'B', 'C']]
        assert_frame_equal(result, expected)

    def test_getitem_list(self):
        self.frame.columns.name = 'foo'

        result = self.frame[['B', 'A']]
        result2 = self.frame[Index(['B', 'A'])]

        expected = self.frame.ix[:, ['B', 'A']]
        expected.columns.name = 'foo'

        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        self.assertEqual(result.columns.name, 'foo')

        with assertRaisesRegexp(KeyError, 'not in index'):
            self.frame[['B', 'A', 'food']]
        with assertRaisesRegexp(KeyError, 'not in index'):
            self.frame[Index(['B', 'A', 'foo'])]

        # tuples
        df = DataFrame(randn(8, 3),
                       columns=Index([('foo', 'bar'), ('baz', 'qux'),
                                      ('peek', 'aboo')], name=['sth', 'sth2']))

        result = df[[('foo', 'bar'), ('baz', 'qux')]]
        expected = df.ix[:, :2]
        assert_frame_equal(result, expected)
        self.assertEqual(result.columns.names, ['sth', 'sth2'])

    def test_getitem_callable(self):
        # GH 12533
        result = self.frame[lambda x: 'A']
        tm.assert_series_equal(result, self.frame.loc[:, 'A'])

        result = self.frame[lambda x: ['A', 'B']]
        tm.assert_frame_equal(result, self.frame.loc[:, ['A', 'B']])

        df = self.frame[:3]
        result = df[lambda x: [True, False, True]]
        tm.assert_frame_equal(result, self.frame.iloc[[0, 2], :])

    def test_setitem_list(self):

        self.frame['E'] = 'foo'
        data = self.frame[['A', 'B']]
        self.frame[['B', 'A']] = data

        assert_series_equal(self.frame['B'], data['A'], check_names=False)
        assert_series_equal(self.frame['A'], data['B'], check_names=False)

        with assertRaisesRegexp(ValueError,
                                'Columns must be same length as key'):
            data[['A']] = self.frame[['A', 'B']]

        with assertRaisesRegexp(ValueError, 'Length of values does not match '
                                'length of index'):
            data['A'] = range(len(data.index) - 1)

        df = DataFrame(0, lrange(3), ['tt1', 'tt2'], dtype=np.int_)
        df.ix[1, ['tt1', 'tt2']] = [1, 2]

        result = df.ix[1, ['tt1', 'tt2']]
        expected = Series([1, 2], df.columns, dtype=np.int_, name=1)
        assert_series_equal(result, expected)

        df['tt1'] = df['tt2'] = '0'
        df.ix[1, ['tt1', 'tt2']] = ['1', '2']
        result = df.ix[1, ['tt1', 'tt2']]
        expected = Series(['1', '2'], df.columns, name=1)
        assert_series_equal(result, expected)

    def test_setitem_list_not_dataframe(self):
        data = np.random.randn(len(self.frame), 2)
        self.frame[['A', 'B']] = data
        assert_almost_equal(self.frame[['A', 'B']].values, data)

    def test_setitem_list_of_tuples(self):
        tuples = lzip(self.frame['A'], self.frame['B'])
        self.frame['tuples'] = tuples

        result = self.frame['tuples']
        expected = Series(tuples, index=self.frame.index, name='tuples')
        assert_series_equal(result, expected)

    def test_setitem_mulit_index(self):
        # GH7655, test that assigning to a sub-frame of a frame
        # with multi-index columns aligns both rows and columns
        it = ['jim', 'joe', 'jolie'], ['first', 'last'], \
             ['left', 'center', 'right']

        cols = MultiIndex.from_product(it)
        index = pd.date_range('20141006', periods=20)
        vals = np.random.randint(1, 1000, (len(index), len(cols)))
        df = pd.DataFrame(vals, columns=cols, index=index)

        i, j = df.index.values.copy(), it[-1][:]

        np.random.shuffle(i)
        df['jim'] = df['jolie'].loc[i, ::-1]
        assert_frame_equal(df['jim'], df['jolie'])

        np.random.shuffle(j)
        df[('joe', 'first')] = df[('jolie', 'last')].loc[i, j]
        assert_frame_equal(df[('joe', 'first')], df[('jolie', 'last')])

        np.random.shuffle(j)
        df[('joe', 'last')] = df[('jolie', 'first')].loc[i, j]
        assert_frame_equal(df[('joe', 'last')], df[('jolie', 'first')])

    def test_setitem_callable(self):
        # GH 12533
        df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})
        df[lambda x: 'A'] = [11, 12, 13, 14]

        exp = pd.DataFrame({'A': [11, 12, 13, 14], 'B': [5, 6, 7, 8]})
        tm.assert_frame_equal(df, exp)

    def test_setitem_other_callable(self):
        # GH 13299
        inc = lambda x: x + 1

        df = pd.DataFrame([[-1, 1], [1, -1]])
        df[df > 0] = inc

        expected = pd.DataFrame([[-1, inc], [inc, -1]])
        tm.assert_frame_equal(df, expected)

    def test_getitem_boolean(self):
        # boolean indexing
        d = self.tsframe.index[10]
        indexer = self.tsframe.index > d
        indexer_obj = indexer.astype(object)

        subindex = self.tsframe.index[indexer]
        subframe = self.tsframe[indexer]

        self.assert_index_equal(subindex, subframe.index)
        with assertRaisesRegexp(ValueError, 'Item wrong length'):
            self.tsframe[indexer[:-1]]

        subframe_obj = self.tsframe[indexer_obj]
        assert_frame_equal(subframe_obj, subframe)

        with tm.assertRaisesRegexp(ValueError, 'boolean values only'):
            self.tsframe[self.tsframe]

        # test that Series work
        indexer_obj = Series(indexer_obj, self.tsframe.index)

        subframe_obj = self.tsframe[indexer_obj]
        assert_frame_equal(subframe_obj, subframe)

        # test that Series indexers reindex
        # we are producing a warning that since the passed boolean
        # key is not the same as the given index, we will reindex
        # not sure this is really necessary
        with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
            indexer_obj = indexer_obj.reindex(self.tsframe.index[::-1])
            subframe_obj = self.tsframe[indexer_obj]
            assert_frame_equal(subframe_obj, subframe)

        # test df[df > 0]
        for df in [self.tsframe, self.mixed_frame,
                   self.mixed_float, self.mixed_int]:

            data = df._get_numeric_data()
            bif = df[df > 0]
            bifw = DataFrame(dict([(c, np.where(data[c] > 0, data[c], np.nan))
                                   for c in data.columns]),
                             index=data.index, columns=data.columns)

            # add back other columns to compare
            for c in df.columns:
                if c not in bifw:
                    bifw[c] = df[c]
            bifw = bifw.reindex(columns=df.columns)

            assert_frame_equal(bif, bifw, check_dtype=False)
            for c in df.columns:
                if bif[c].dtype != bifw[c].dtype:
                    self.assertEqual(bif[c].dtype, df[c].dtype)

    def test_getitem_boolean_casting(self):

        # don't upcast if we don't need to
        df = self.tsframe.copy()
        df['E'] = 1
        df['E'] = df['E'].astype('int32')
        df['E1'] = df['E'].copy()
        df['F'] = 1
        df['F'] = df['F'].astype('int64')
        df['F1'] = df['F'].copy()

        casted = df[df > 0]
        result = casted.get_dtype_counts()
        expected = Series({'float64': 4, 'int32': 2, 'int64': 2})
        assert_series_equal(result, expected)

        # int block splitting
        df.ix[1:3, ['E1', 'F1']] = 0
        casted = df[df > 0]
        result = casted.get_dtype_counts()
        expected = Series({'float64': 6, 'int32': 1, 'int64': 1})
        assert_series_equal(result, expected)

        # where dtype conversions
        # GH 3733
        df = DataFrame(data=np.random.randn(100, 50))
        df = df.where(df > 0)  # create nans
        bools = df > 0
        mask = isnull(df)
        expected = bools.astype(float).mask(mask)
        result = bools.mask(mask)
        assert_frame_equal(result, expected)

    def test_getitem_boolean_list(self):
        df = DataFrame(np.arange(12).reshape(3, 4))

        def _checkit(lst):
            result = df[lst]
            expected = df.ix[df.index[lst]]
            assert_frame_equal(result, expected)

        _checkit([True, False, True])
        _checkit([True, True, True])
        _checkit([False, False, False])

    def test_getitem_boolean_iadd(self):
        arr = randn(5, 5)

        df = DataFrame(arr.copy(), columns=['A', 'B', 'C', 'D', 'E'])

        df[df < 0] += 1
        arr[arr < 0] += 1

        assert_almost_equal(df.values, arr)

    def test_boolean_index_empty_corner(self):
        # #2096
        blah = DataFrame(np.empty([0, 1]), columns=['A'],
                         index=DatetimeIndex([]))

        # both of these should succeed trivially
        k = np.array([], bool)

        blah[k]
        blah[k] = 0

    def test_getitem_ix_mixed_integer(self):
        df = DataFrame(np.random.randn(4, 3),
                       index=[1, 10, 'C', 'E'], columns=[1, 2, 3])

        result = df.ix[:-1]
        expected = df.ix[df.index[:-1]]
        assert_frame_equal(result, expected)

        result = df.ix[[1, 10]]
        expected = df.ix[Index([1, 10], dtype=object)]
        assert_frame_equal(result, expected)

        # 11320
        df = pd.DataFrame({"rna": (1.5, 2.2, 3.2, 4.5),
                           -1000: [11, 21, 36, 40],
                           0: [10, 22, 43, 34],
                           1000: [0, 10, 20, 30]},
                          columns=['rna', -1000, 0, 1000])
        result = df[[1000]]
        expected = df.iloc[:, [3]]
        assert_frame_equal(result, expected)
        result = df[[-1000]]
        expected = df.iloc[:, [1]]
        assert_frame_equal(result, expected)

    def test_getitem_setitem_ix_negative_integers(self):
        result = self.frame.ix[:, -1]
        assert_series_equal(result, self.frame['D'])

        result = self.frame.ix[:, [-1]]
        assert_frame_equal(result, self.frame[['D']])

        result = self.frame.ix[:, [-1, -2]]
        assert_frame_equal(result, self.frame[['D', 'C']])

        self.frame.ix[:, [-1]] = 0
        self.assertTrue((self.frame['D'] == 0).all())

        df = DataFrame(np.random.randn(8, 4))
        self.assertTrue(isnull(df.ix[:, [-1]].values).all())

        # #1942
        a = DataFrame(randn(20, 2), index=[chr(x + 65) for x in range(20)])
        a.ix[-1] = a.ix[-2]

        assert_series_equal(a.ix[-1], a.ix[-2], check_names=False)
        self.assertEqual(a.ix[-1].name, 'T')
        self.assertEqual(a.ix[-2].name, 'S')

    def test_getattr(self):
        assert_series_equal(self.frame.A, self.frame['A'])
        self.assertRaises(AttributeError, getattr, self.frame,
                          'NONEXISTENT_NAME')

    def test_setattr_column(self):
        df = DataFrame({'foobar': 1}, index=lrange(10))

        df.foobar = 5
        self.assertTrue((df.foobar == 5).all())

    def test_setitem(self):
        # not sure what else to do here
        series = self.frame['A'][::2]
        self.frame['col5'] = series
        self.assertIn('col5', self.frame)

        self.assertEqual(len(series), 15)
        self.assertEqual(len(self.frame), 30)

        exp = np.ravel(np.column_stack((series.values, [np.nan] * 15)))
        exp = Series(exp, index=self.frame.index, name='col5')
        tm.assert_series_equal(self.frame['col5'], exp)

        series = self.frame['A']
        self.frame['col6'] = series
        tm.assert_series_equal(series, self.frame['col6'], check_names=False)

        with tm.assertRaises(KeyError):
            self.frame[randn(len(self.frame) + 1)] = 1

        # set ndarray
        arr = randn(len(self.frame))
        self.frame['col9'] = arr
        self.assertTrue((self.frame['col9'] == arr).all())

        self.frame['col7'] = 5
        assert((self.frame['col7'] == 5).all())

        self.frame['col0'] = 3.14
        assert((self.frame['col0'] == 3.14).all())

        self.frame['col8'] = 'foo'
        assert((self.frame['col8'] == 'foo').all())

        # this is partially a view (e.g. some blocks are view)
        # so raise/warn
        smaller = self.frame[:2]

        def f():
            smaller['col10'] = ['1', '2']
        self.assertRaises(com.SettingWithCopyError, f)
        self.assertEqual(smaller['col10'].dtype, np.object_)
        self.assertTrue((smaller['col10'] == ['1', '2']).all())

        # with a dtype
        for dtype in ['int32', 'int64', 'float32', 'float64']:
            self.frame[dtype] = np.array(arr, dtype=dtype)
            self.assertEqual(self.frame[dtype].dtype.name, dtype)

        # dtype changing GH4204
        df = DataFrame([[0, 0]])
        df.iloc[0] = np.nan
        expected = DataFrame([[np.nan, np.nan]])
        assert_frame_equal(df, expected)

        df = DataFrame([[0, 0]])
        df.loc[0] = np.nan
        assert_frame_equal(df, expected)

    def test_setitem_tuple(self):
        self.frame['A', 'B'] = self.frame['A']
        assert_series_equal(self.frame['A', 'B'], self.frame[
                            'A'], check_names=False)

    def test_setitem_always_copy(self):
        s = self.frame['A'].copy()
        self.frame['E'] = s

        self.frame['E'][5:10] = nan
        self.assertTrue(notnull(s[5:10]).all())

    def test_setitem_boolean(self):
        df = self.frame.copy()
        values = self.frame.values

        df[df['A'] > 0] = 4
        values[values[:, 0] > 0] = 4
        assert_almost_equal(df.values, values)

        # test that column reindexing works
        series = df['A'] == 4
        series = series.reindex(df.index[::-1])
        df[series] = 1
        values[values[:, 0] == 4] = 1
        assert_almost_equal(df.values, values)

        df[df > 0] = 5
        values[values > 0] = 5
        assert_almost_equal(df.values, values)

        df[df == 5] = 0
        values[values == 5] = 0
        assert_almost_equal(df.values, values)

        # a df that needs alignment first
        df[df[:-1] < 0] = 2
        np.putmask(values[:-1], values[:-1] < 0, 2)
        assert_almost_equal(df.values, values)

        # indexed with same shape but rows-reversed df
        df[df[::-1] == 2] = 3
        values[values == 2] = 3
        assert_almost_equal(df.values, values)

        with assertRaisesRegexp(TypeError, 'Must pass DataFrame with boolean '
                                'values only'):
            df[df * 0] = 2

        # index with DataFrame
        mask = df > np.abs(df)
        expected = df.copy()
        df[df > np.abs(df)] = nan
        expected.values[mask.values] = nan
        assert_frame_equal(df, expected)

        # set from DataFrame
        expected = df.copy()
        df[df > np.abs(df)] = df * 2
        np.putmask(expected.values, mask.values, df.values * 2)
        assert_frame_equal(df, expected)

    def test_setitem_cast(self):
        self.frame['D'] = self.frame['D'].astype('i8')
        self.assertEqual(self.frame['D'].dtype, np.int64)

        # #669, should not cast?
        # this is now set to int64, which means a replacement of the column to
        # the value dtype (and nothing to do with the existing dtype)
        self.frame['B'] = 0
        self.assertEqual(self.frame['B'].dtype, np.int64)

        # cast if pass array of course
        self.frame['B'] = np.arange(len(self.frame))
        self.assertTrue(issubclass(self.frame['B'].dtype.type, np.integer))

        self.frame['foo'] = 'bar'
        self.frame['foo'] = 0
        self.assertEqual(self.frame['foo'].dtype, np.int64)

        self.frame['foo'] = 'bar'
        self.frame['foo'] = 2.5
        self.assertEqual(self.frame['foo'].dtype, np.float64)

        self.frame['something'] = 0
        self.assertEqual(self.frame['something'].dtype, np.int64)
        self.frame['something'] = 2
        self.assertEqual(self.frame['something'].dtype, np.int64)
        self.frame['something'] = 2.5
        self.assertEqual(self.frame['something'].dtype, np.float64)

        # GH 7704
        # dtype conversion on setting
        df = DataFrame(np.random.rand(30, 3), columns=tuple('ABC'))
        df['event'] = np.nan
        df.loc[10, 'event'] = 'foo'
        result = df.get_dtype_counts().sort_values()
        expected = Series({'float64': 3, 'object': 1}).sort_values()
        assert_series_equal(result, expected)

        # Test that data type is preserved . #5782
        df = DataFrame({'one': np.arange(6, dtype=np.int8)})
        df.loc[1, 'one'] = 6
        self.assertEqual(df.dtypes.one, np.dtype(np.int8))
        df.one = np.int8(7)
        self.assertEqual(df.dtypes.one, np.dtype(np.int8))

    def test_setitem_boolean_column(self):
        expected = self.frame.copy()
        mask = self.frame['A'] > 0

        self.frame.ix[mask, 'B'] = 0
        expected.values[mask.values, 1] = 0

        assert_frame_equal(self.frame, expected)

    def test_setitem_corner(self):
        # corner case
        df = DataFrame({'B': [1., 2., 3.],
                        'C': ['a', 'b', 'c']},
                       index=np.arange(3))
        del df['B']
        df['B'] = [1., 2., 3.]
        self.assertIn('B', df)
        self.assertEqual(len(df.columns), 2)

        df['A'] = 'beginning'
        df['E'] = 'foo'
        df['D'] = 'bar'
        df[datetime.now()] = 'date'
        df[datetime.now()] = 5.

        # what to do when empty frame with index
        dm = DataFrame(index=self.frame.index)
        dm['A'] = 'foo'
        dm['B'] = 'bar'
        self.assertEqual(len(dm.columns), 2)
        self.assertEqual(dm.values.dtype, np.object_)

        # upcast
        dm['C'] = 1
        self.assertEqual(dm['C'].dtype, np.int64)

        dm['E'] = 1.
        self.assertEqual(dm['E'].dtype, np.float64)

        # set existing column
        dm['A'] = 'bar'
        self.assertEqual('bar', dm['A'][0])

        dm = DataFrame(index=np.arange(3))
        dm['A'] = 1
        dm['foo'] = 'bar'
        del dm['foo']
        dm['foo'] = 'bar'
        self.assertEqual(dm['foo'].dtype, np.object_)

        dm['coercable'] = ['1', '2', '3']
        self.assertEqual(dm['coercable'].dtype, np.object_)

    def test_setitem_corner2(self):
        data = {"title": ['foobar', 'bar', 'foobar'] + ['foobar'] * 17,
                "cruft": np.random.random(20)}

        df = DataFrame(data)
        ix = df[df['title'] == 'bar'].index

        df.ix[ix, ['title']] = 'foobar'
        df.ix[ix, ['cruft']] = 0

        assert(df.ix[1, 'title'] == 'foobar')
        assert(df.ix[1, 'cruft'] == 0)

    def test_setitem_ambig(self):
        # difficulties with mixed-type data
        from decimal import Decimal

        # created as float type
        dm = DataFrame(index=lrange(3), columns=lrange(3))

        coercable_series = Series([Decimal(1) for _ in range(3)],
                                  index=lrange(3))
        uncoercable_series = Series(['foo', 'bzr', 'baz'], index=lrange(3))

        dm[0] = np.ones(3)
        self.assertEqual(len(dm.columns), 3)
        # self.assertIsNone(dm.objects)

        dm[1] = coercable_series
        self.assertEqual(len(dm.columns), 3)
        # self.assertIsNone(dm.objects)

        dm[2] = uncoercable_series
        self.assertEqual(len(dm.columns), 3)
        # self.assertIsNotNone(dm.objects)
        self.assertEqual(dm[2].dtype, np.object_)

    def test_setitem_clear_caches(self):
        # GH #304
        df = DataFrame({'x': [1.1, 2.1, 3.1, 4.1], 'y': [5.1, 6.1, 7.1, 8.1]},
                       index=[0, 1, 2, 3])
        df.insert(2, 'z', np.nan)

        # cache it
        foo = df['z']

        df.ix[2:, 'z'] = 42

        expected = Series([np.nan, np.nan, 42, 42], index=df.index, name='z')
        self.assertIsNot(df['z'], foo)
        assert_series_equal(df['z'], expected)

    def test_setitem_None(self):
        # GH #766
        self.frame[None] = self.frame['A']
        assert_series_equal(
            self.frame.iloc[:, -1], self.frame['A'], check_names=False)
        assert_series_equal(self.frame.loc[:, None], self.frame[
                            'A'], check_names=False)
        assert_series_equal(self.frame[None], self.frame[
                            'A'], check_names=False)
        repr(self.frame)

    def test_setitem_empty(self):
        # GH 9596
        df = pd.DataFrame({'a': ['1', '2', '3'],
                           'b': ['11', '22', '33'],
                           'c': ['111', '222', '333']})

        result = df.copy()
        result.loc[result.b.isnull(), 'a'] = result.a
        assert_frame_equal(result, df)

    def test_setitem_empty_frame_with_boolean(self):
        # Test for issue #10126

        for dtype in ('float', 'int64'):
            for df in [
                    pd.DataFrame(dtype=dtype),
                    pd.DataFrame(dtype=dtype, index=[1]),
                    pd.DataFrame(dtype=dtype, columns=['A']),
            ]:
                df2 = df.copy()
                df[df > df2] = 47
                assert_frame_equal(df, df2)

    def test_getitem_empty_frame_with_boolean(self):
        # Test for issue #11859

        df = pd.DataFrame()
        df2 = df[df > 0]
        assert_frame_equal(df, df2)

    def test_delitem_corner(self):
        f = self.frame.copy()
        del f['D']
        self.assertEqual(len(f.columns), 3)
        self.assertRaises(KeyError, f.__delitem__, 'D')
        del f['B']
        self.assertEqual(len(f.columns), 2)

    def test_getitem_fancy_2d(self):
        f = self.frame
        ix = f.ix

        assert_frame_equal(ix[:, ['B', 'A']], f.reindex(columns=['B', 'A']))

        subidx = self.frame.index[[5, 4, 1]]
        assert_frame_equal(ix[subidx, ['B', 'A']],
                           f.reindex(index=subidx, columns=['B', 'A']))

        # slicing rows, etc.
        assert_frame_equal(ix[5:10], f[5:10])
        assert_frame_equal(ix[5:10, :], f[5:10])
        assert_frame_equal(ix[:5, ['A', 'B']],
                           f.reindex(index=f.index[:5], columns=['A', 'B']))

        # slice rows with labels, inclusive!
        expected = ix[5:11]
        result = ix[f.index[5]:f.index[10]]
        assert_frame_equal(expected, result)

        # slice columns
        assert_frame_equal(ix[:, :2], f.reindex(columns=['A', 'B']))

        # get view
        exp = f.copy()
        ix[5:10].values[:] = 5
        exp.values[5:10] = 5
        assert_frame_equal(f, exp)

        self.assertRaises(ValueError, ix.__getitem__, f > 0.5)

    def test_slice_floats(self):
        index = [52195.504153, 52196.303147, 52198.369883]
        df = DataFrame(np.random.rand(3, 2), index=index)

        s1 = df.ix[52195.1:52196.5]
        self.assertEqual(len(s1), 2)

        s1 = df.ix[52195.1:52196.6]
        self.assertEqual(len(s1), 2)

        s1 = df.ix[52195.1:52198.9]
        self.assertEqual(len(s1), 3)

    def test_getitem_fancy_slice_integers_step(self):
        df = DataFrame(np.random.randn(10, 5))

        # this is OK
        result = df.ix[:8:2]  # noqa
        df.ix[:8:2] = np.nan
        self.assertTrue(isnull(df.ix[:8:2]).values.all())

    def test_getitem_setitem_integer_slice_keyerrors(self):
        df = DataFrame(np.random.randn(10, 5), index=lrange(0, 20, 2))

        # this is OK
        cp = df.copy()
        cp.ix[4:10] = 0
        self.assertTrue((cp.ix[4:10] == 0).values.all())

        # so is this
        cp = df.copy()
        cp.ix[3:11] = 0
        self.assertTrue((cp.ix[3:11] == 0).values.all())

        result = df.ix[4:10]
        result2 = df.ix[3:11]
        expected = df.reindex([4, 6, 8, 10])

        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        # non-monotonic, raise KeyError
        df2 = df.iloc[lrange(5) + lrange(5, 10)[::-1]]
        self.assertRaises(KeyError, df2.ix.__getitem__, slice(3, 11))
        self.assertRaises(KeyError, df2.ix.__setitem__, slice(3, 11), 0)

    def test_setitem_fancy_2d(self):
        f = self.frame

        ix = f.ix  # noqa

        # case 1
        frame = self.frame.copy()
        expected = frame.copy()
        frame.ix[:, ['B', 'A']] = 1
        expected['B'] = 1.
        expected['A'] = 1.
        assert_frame_equal(frame, expected)

        # case 2
        frame = self.frame.copy()
        frame2 = self.frame.copy()

        expected = frame.copy()

        subidx = self.frame.index[[5, 4, 1]]
        values = randn(3, 2)

        frame.ix[subidx, ['B', 'A']] = values
        frame2.ix[[5, 4, 1], ['B', 'A']] = values

        expected['B'].ix[subidx] = values[:, 0]
        expected['A'].ix[subidx] = values[:, 1]

        assert_frame_equal(frame, expected)
        assert_frame_equal(frame2, expected)

        # case 3: slicing rows, etc.
        frame = self.frame.copy()

        expected1 = self.frame.copy()
        frame.ix[5:10] = 1.
        expected1.values[5:10] = 1.
        assert_frame_equal(frame, expected1)

        expected2 = self.frame.copy()
        arr = randn(5, len(frame.columns))
        frame.ix[5:10] = arr
        expected2.values[5:10] = arr
        assert_frame_equal(frame, expected2)

        # case 4
        frame = self.frame.copy()
        frame.ix[5:10, :] = 1.
        assert_frame_equal(frame, expected1)
        frame.ix[5:10, :] = arr
        assert_frame_equal(frame, expected2)

        # case 5
        frame = self.frame.copy()
        frame2 = self.frame.copy()

        expected = self.frame.copy()
        values = randn(5, 2)

        frame.ix[:5, ['A', 'B']] = values
        expected['A'][:5] = values[:, 0]
        expected['B'][:5] = values[:, 1]
        assert_frame_equal(frame, expected)

        frame2.ix[:5, [0, 1]] = values
        assert_frame_equal(frame2, expected)

        # case 6: slice rows with labels, inclusive!
        frame = self.frame.copy()
        expected = self.frame.copy()

        frame.ix[frame.index[5]:frame.index[10]] = 5.
        expected.values[5:11] = 5
        assert_frame_equal(frame, expected)

        # case 7: slice columns
        frame = self.frame.copy()
        frame2 = self.frame.copy()
        expected = self.frame.copy()

        # slice indices
        frame.ix[:, 1:3] = 4.
        expected.values[:, 1:3] = 4.
        assert_frame_equal(frame, expected)

        # slice with labels
        frame.ix[:, 'B':'C'] = 4.
        assert_frame_equal(frame, expected)

        # new corner case of boolean slicing / setting
        frame = DataFrame(lzip([2, 3, 9, 6, 7], [np.nan] * 5),
                          columns=['a', 'b'])
        lst = [100]
        lst.extend([np.nan] * 4)
        expected = DataFrame(lzip([100, 3, 9, 6, 7], lst),
                             columns=['a', 'b'])
        frame[frame['a'] == 2] = 100
        assert_frame_equal(frame, expected)

    def test_fancy_getitem_slice_mixed(self):
        sliced = self.mixed_frame.ix[:, -3:]
        self.assertEqual(sliced['D'].dtype, np.float64)

        # get view with single block
        # setting it triggers setting with copy
        sliced = self.frame.ix[:, -3:]

        def f():
            sliced['C'] = 4.
        self.assertRaises(com.SettingWithCopyError, f)
        self.assertTrue((self.frame['C'] == 4).all())

    def test_fancy_setitem_int_labels(self):
        # integer index defers to label-based indexing

        df = DataFrame(np.random.randn(10, 5), index=np.arange(0, 20, 2))

        tmp = df.copy()
        exp = df.copy()
        tmp.ix[[0, 2, 4]] = 5
        exp.values[:3] = 5
        assert_frame_equal(tmp, exp)

        tmp = df.copy()
        exp = df.copy()
        tmp.ix[6] = 5
        exp.values[3] = 5
        assert_frame_equal(tmp, exp)

        tmp = df.copy()
        exp = df.copy()
        tmp.ix[:, 2] = 5

        # tmp correctly sets the dtype
        # so match the exp way
        exp[2] = 5
        assert_frame_equal(tmp, exp)

    def test_fancy_getitem_int_labels(self):
        df = DataFrame(np.random.randn(10, 5), index=np.arange(0, 20, 2))

        result = df.ix[[4, 2, 0], [2, 0]]
        expected = df.reindex(index=[4, 2, 0], columns=[2, 0])
        assert_frame_equal(result, expected)

        result = df.ix[[4, 2, 0]]
        expected = df.reindex(index=[4, 2, 0])
        assert_frame_equal(result, expected)

        result = df.ix[4]
        expected = df.xs(4)
        assert_series_equal(result, expected)

        result = df.ix[:, 3]
        expected = df[3]
        assert_series_equal(result, expected)

    def test_fancy_index_int_labels_exceptions(self):
        df = DataFrame(np.random.randn(10, 5), index=np.arange(0, 20, 2))

        # labels that aren't contained
        self.assertRaises(KeyError, df.ix.__setitem__,
                          ([0, 1, 2], [2, 3, 4]), 5)

        # try to set indices not contained in frame
        self.assertRaises(KeyError,
                          self.frame.ix.__setitem__,
                          ['foo', 'bar', 'baz'], 1)
        self.assertRaises(KeyError,
                          self.frame.ix.__setitem__,
                          (slice(None, None), ['E']), 1)

        # partial setting now allows this GH2578
        # self.assertRaises(KeyError,
        #                  self.frame.ix.__setitem__,
        #                  (slice(None, None), 'E'), 1)

    def test_setitem_fancy_mixed_2d(self):
        self.mixed_frame.ix[:5, ['C', 'B', 'A']] = 5
        result = self.mixed_frame.ix[:5, ['C', 'B', 'A']]
        self.assertTrue((result.values == 5).all())

        self.mixed_frame.ix[5] = np.nan
        self.assertTrue(isnull(self.mixed_frame.ix[5]).all())

        self.mixed_frame.ix[5] = self.mixed_frame.ix[6]
        assert_series_equal(self.mixed_frame.ix[5], self.mixed_frame.ix[6],
                            check_names=False)

        # #1432
        df = DataFrame({1: [1., 2., 3.],
                        2: [3, 4, 5]})
        self.assertTrue(df._is_mixed_type)

        df.ix[1] = [5, 10]

        expected = DataFrame({1: [1., 5., 3.],
                              2: [3, 10, 5]})

        assert_frame_equal(df, expected)

    def test_ix_align(self):
        b = Series(randn(10), name=0).sort_values()
        df_orig = DataFrame(randn(10, 4))
        df = df_orig.copy()

        df.ix[:, 0] = b
        assert_series_equal(df.ix[:, 0].reindex(b.index), b)

        dft = df_orig.T
        dft.ix[0, :] = b
        assert_series_equal(dft.ix[0, :].reindex(b.index), b)

        df = df_orig.copy()
        df.ix[:5, 0] = b
        s = df.ix[:5, 0]
        assert_series_equal(s, b.reindex(s.index))

        dft = df_orig.T
        dft.ix[0, :5] = b
        s = dft.ix[0, :5]
        assert_series_equal(s, b.reindex(s.index))

        df = df_orig.copy()
        idx = [0, 1, 3, 5]
        df.ix[idx, 0] = b
        s = df.ix[idx, 0]
        assert_series_equal(s, b.reindex(s.index))

        dft = df_orig.T
        dft.ix[0, idx] = b
        s = dft.ix[0, idx]
        assert_series_equal(s, b.reindex(s.index))

    def test_ix_frame_align(self):
        b = DataFrame(np.random.randn(3, 4))
        df_orig = DataFrame(randn(10, 4))
        df = df_orig.copy()

        df.ix[:3] = b
        out = b.ix[:3]
        assert_frame_equal(out, b)

        b.sort_index(inplace=True)

        df = df_orig.copy()
        df.ix[[0, 1, 2]] = b
        out = df.ix[[0, 1, 2]].reindex(b.index)
        assert_frame_equal(out, b)

        df = df_orig.copy()
        df.ix[:3] = b
        out = df.ix[:3]
        assert_frame_equal(out, b.reindex(out.index))

    def test_getitem_setitem_non_ix_labels(self):
        df = tm.makeTimeDataFrame()

        start, end = df.index[[5, 10]]

        result = df.ix[start:end]
        result2 = df[start:end]
        expected = df[5:11]
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        result = df.copy()
        result.ix[start:end] = 0
        result2 = df.copy()
        result2[start:end] = 0
        expected = df.copy()
        expected[5:11] = 0
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_ix_multi_take(self):
        df = DataFrame(np.random.randn(3, 2))
        rs = df.ix[df.index == 0, :]
        xp = df.reindex([0])
        assert_frame_equal(rs, xp)

        """ #1321
        df = DataFrame(np.random.randn(3, 2))
        rs = df.ix[df.index==0, df.columns==1]
        xp = df.reindex([0], [1])
        assert_frame_equal(rs, xp)
        """

    def test_ix_multi_take_nonint_index(self):
        df = DataFrame(np.random.randn(3, 2), index=['x', 'y', 'z'],
                       columns=['a', 'b'])
        rs = df.ix[[0], [0]]
        xp = df.reindex(['x'], columns=['a'])
        assert_frame_equal(rs, xp)

    def test_ix_multi_take_multiindex(self):
        df = DataFrame(np.random.randn(3, 2), index=['x', 'y', 'z'],
                       columns=[['a', 'b'], ['1', '2']])
        rs = df.ix[[0], [0]]
        xp = df.reindex(['x'], columns=[('a', '1')])
        assert_frame_equal(rs, xp)

    def test_ix_dup(self):
        idx = Index(['a', 'a', 'b', 'c', 'd', 'd'])
        df = DataFrame(np.random.randn(len(idx), 3), idx)

        sub = df.ix[:'d']
        assert_frame_equal(sub, df)

        sub = df.ix['a':'c']
        assert_frame_equal(sub, df.ix[0:4])

        sub = df.ix['b':'d']
        assert_frame_equal(sub, df.ix[2:])

    def test_getitem_fancy_1d(self):
        f = self.frame
        ix = f.ix

        # return self if no slicing...for now
        self.assertIs(ix[:, :], f)

        # low dimensional slice
        xs1 = ix[2, ['C', 'B', 'A']]
        xs2 = f.xs(f.index[2]).reindex(['C', 'B', 'A'])
        assert_series_equal(xs1, xs2)

        ts1 = ix[5:10, 2]
        ts2 = f[f.columns[2]][5:10]
        assert_series_equal(ts1, ts2)

        # positional xs
        xs1 = ix[0]
        xs2 = f.xs(f.index[0])
        assert_series_equal(xs1, xs2)

        xs1 = ix[f.index[5]]
        xs2 = f.xs(f.index[5])
        assert_series_equal(xs1, xs2)

        # single column
        assert_series_equal(ix[:, 'A'], f['A'])

        # return view
        exp = f.copy()
        exp.values[5] = 4
        ix[5][:] = 4
        assert_frame_equal(exp, f)

        exp.values[:, 1] = 6
        ix[:, 1][:] = 6
        assert_frame_equal(exp, f)

        # slice of mixed-frame
        xs = self.mixed_frame.ix[5]
        exp = self.mixed_frame.xs(self.mixed_frame.index[5])
        assert_series_equal(xs, exp)

    def test_setitem_fancy_1d(self):

        # case 1: set cross-section for indices
        frame = self.frame.copy()
        expected = self.frame.copy()

        frame.ix[2, ['C', 'B', 'A']] = [1., 2., 3.]
        expected['C'][2] = 1.
        expected['B'][2] = 2.
        expected['A'][2] = 3.
        assert_frame_equal(frame, expected)

        frame2 = self.frame.copy()
        frame2.ix[2, [3, 2, 1]] = [1., 2., 3.]
        assert_frame_equal(frame, expected)

        # case 2, set a section of a column
        frame = self.frame.copy()
        expected = self.frame.copy()

        vals = randn(5)
        expected.values[5:10, 2] = vals
        frame.ix[5:10, 2] = vals
        assert_frame_equal(frame, expected)

        frame2 = self.frame.copy()
        frame2.ix[5:10, 'B'] = vals
        assert_frame_equal(frame, expected)

        # case 3: full xs
        frame = self.frame.copy()
        expected = self.frame.copy()

        frame.ix[4] = 5.
        expected.values[4] = 5.
        assert_frame_equal(frame, expected)

        frame.ix[frame.index[4]] = 6.
        expected.values[4] = 6.
        assert_frame_equal(frame, expected)

        # single column
        frame = self.frame.copy()
        expected = self.frame.copy()

        frame.ix[:, 'A'] = 7.
        expected['A'] = 7.
        assert_frame_equal(frame, expected)

    def test_getitem_fancy_scalar(self):
        f = self.frame
        ix = f.ix
        # individual value
        for col in f.columns:
            ts = f[col]
            for idx in f.index[::5]:
                self.assertEqual(ix[idx, col], ts[idx])

    def test_setitem_fancy_scalar(self):
        f = self.frame
        expected = self.frame.copy()
        ix = f.ix
        # individual value
        for j, col in enumerate(f.columns):
            ts = f[col]  # noqa
            for idx in f.index[::5]:
                i = f.index.get_loc(idx)
                val = randn()
                expected.values[i, j] = val
                ix[idx, col] = val
                assert_frame_equal(f, expected)

    def test_getitem_fancy_boolean(self):
        f = self.frame
        ix = f.ix

        expected = f.reindex(columns=['B', 'D'])
        result = ix[:, [False, True, False, True]]
        assert_frame_equal(result, expected)

        expected = f.reindex(index=f.index[5:10], columns=['B', 'D'])
        result = ix[5:10, [False, True, False, True]]
        assert_frame_equal(result, expected)

        boolvec = f.index > f.index[7]
        expected = f.reindex(index=f.index[boolvec])
        result = ix[boolvec]
        assert_frame_equal(result, expected)
        result = ix[boolvec, :]
        assert_frame_equal(result, expected)

        result = ix[boolvec, 2:]
        expected = f.reindex(index=f.index[boolvec],
                             columns=['C', 'D'])
        assert_frame_equal(result, expected)

    def test_setitem_fancy_boolean(self):
        # from 2d, set with booleans
        frame = self.frame.copy()
        expected = self.frame.copy()

        mask = frame['A'] > 0
        frame.ix[mask] = 0.
        expected.values[mask.values] = 0.
        assert_frame_equal(frame, expected)

        frame = self.frame.copy()
        expected = self.frame.copy()
        frame.ix[mask, ['A', 'B']] = 0.
        expected.values[mask.values, :2] = 0.
        assert_frame_equal(frame, expected)

    def test_getitem_fancy_ints(self):
        result = self.frame.ix[[1, 4, 7]]
        expected = self.frame.ix[self.frame.index[[1, 4, 7]]]
        assert_frame_equal(result, expected)

        result = self.frame.ix[:, [2, 0, 1]]
        expected = self.frame.ix[:, self.frame.columns[[2, 0, 1]]]
        assert_frame_equal(result, expected)

    def test_getitem_setitem_fancy_exceptions(self):
        ix = self.frame.ix
        with assertRaisesRegexp(IndexingError, 'Too many indexers'):
            ix[:, :, :]

        with assertRaises(IndexingError):
            ix[:, :, :] = 1

    def test_getitem_setitem_boolean_misaligned(self):
        # boolean index misaligned labels
        mask = self.frame['A'][::-1] > 1

        result = self.frame.ix[mask]
        expected = self.frame.ix[mask[::-1]]
        assert_frame_equal(result, expected)

        cp = self.frame.copy()
        expected = self.frame.copy()
        cp.ix[mask] = 0
        expected.ix[mask] = 0
        assert_frame_equal(cp, expected)

    def test_getitem_setitem_boolean_multi(self):
        df = DataFrame(np.random.randn(3, 2))

        # get
        k1 = np.array([True, False, True])
        k2 = np.array([False, True])
        result = df.ix[k1, k2]
        expected = df.ix[[0, 2], [1]]
        assert_frame_equal(result, expected)

        expected = df.copy()
        df.ix[np.array([True, False, True]),
              np.array([False, True])] = 5
        expected.ix[[0, 2], [1]] = 5
        assert_frame_equal(df, expected)

    def test_getitem_setitem_float_labels(self):
        index = Index([1.5, 2, 3, 4, 5])
        df = DataFrame(np.random.randn(5, 5), index=index)

        result = df.ix[1.5:4]
        expected = df.reindex([1.5, 2, 3, 4])
        assert_frame_equal(result, expected)
        self.assertEqual(len(result), 4)

        result = df.ix[4:5]
        expected = df.reindex([4, 5])  # reindex with int
        assert_frame_equal(result, expected, check_index_type=False)
        self.assertEqual(len(result), 2)

        result = df.ix[4:5]
        expected = df.reindex([4.0, 5.0])  # reindex with float
        assert_frame_equal(result, expected)
        self.assertEqual(len(result), 2)

        # loc_float changes this to work properly
        result = df.ix[1:2]
        expected = df.iloc[0:2]
        assert_frame_equal(result, expected)

        df.ix[1:2] = 0
        result = df[1:2]
        self.assertTrue((result == 0).all().all())

        # #2727
        index = Index([1.0, 2.5, 3.5, 4.5, 5.0])
        df = DataFrame(np.random.randn(5, 5), index=index)

        # positional slicing only via iloc!
        self.assertRaises(TypeError, lambda: df.iloc[1.0:5])

        result = df.iloc[4:5]
        expected = df.reindex([5.0])
        assert_frame_equal(result, expected)
        self.assertEqual(len(result), 1)

        cp = df.copy()

        def f():
            cp.iloc[1.0:5] = 0
        self.assertRaises(TypeError, f)

        def f():
            result = cp.iloc[1.0:5] == 0  # noqa

        self.assertRaises(TypeError, f)
        self.assertTrue(result.values.all())
        self.assertTrue((cp.iloc[0:1] == df.iloc[0:1]).values.all())

        cp = df.copy()
        cp.iloc[4:5] = 0
        self.assertTrue((cp.iloc[4:5] == 0).values.all())
        self.assertTrue((cp.iloc[0:4] == df.iloc[0:4]).values.all())

        # float slicing
        result = df.ix[1.0:5]
        expected = df
        assert_frame_equal(result, expected)
        self.assertEqual(len(result), 5)

        result = df.ix[1.1:5]
        expected = df.reindex([2.5, 3.5, 4.5, 5.0])
        assert_frame_equal(result, expected)
        self.assertEqual(len(result), 4)

        result = df.ix[4.51:5]
        expected = df.reindex([5.0])
        assert_frame_equal(result, expected)
        self.assertEqual(len(result), 1)

        result = df.ix[1.0:5.0]
        expected = df.reindex([1.0, 2.5, 3.5, 4.5, 5.0])
        assert_frame_equal(result, expected)
        self.assertEqual(len(result), 5)

        cp = df.copy()
        cp.ix[1.0:5.0] = 0
        result = cp.ix[1.0:5.0]
        self.assertTrue((result == 0).values.all())

    def test_setitem_single_column_mixed(self):
        df = DataFrame(randn(5, 3), index=['a', 'b', 'c', 'd', 'e'],
                       columns=['foo', 'bar', 'baz'])
        df['str'] = 'qux'
        df.ix[::2, 'str'] = nan
        expected = np.array([nan, 'qux', nan, 'qux', nan], dtype=object)
        assert_almost_equal(df['str'].values, expected)

    def test_setitem_single_column_mixed_datetime(self):
        df = DataFrame(randn(5, 3), index=['a', 'b', 'c', 'd', 'e'],
                       columns=['foo', 'bar', 'baz'])

        df['timestamp'] = Timestamp('20010102')

        # check our dtypes
        result = df.get_dtype_counts()
        expected = Series({'float64': 3, 'datetime64[ns]': 1})
        assert_series_equal(result, expected)

        # set an allowable datetime64 type
        from pandas import tslib
        df.ix['b', 'timestamp'] = tslib.iNaT
        self.assertTrue(isnull(df.ix['b', 'timestamp']))

        # allow this syntax
        df.ix['c', 'timestamp'] = nan
        self.assertTrue(isnull(df.ix['c', 'timestamp']))

        # allow this syntax
        df.ix['d', :] = nan
        self.assertTrue(isnull(df.ix['c', :]).all() == False)  # noqa

        # as of GH 3216 this will now work!
        # try to set with a list like item
        # self.assertRaises(
        #    Exception, df.ix.__setitem__, ('d', 'timestamp'), [nan])

    def test_setitem_frame(self):
        piece = self.frame.ix[:2, ['A', 'B']]
        self.frame.ix[-2:, ['A', 'B']] = piece.values
        assert_almost_equal(self.frame.ix[-2:, ['A', 'B']].values,
                            piece.values)

        # GH 3216

        # already aligned
        f = self.mixed_frame.copy()
        piece = DataFrame([[1., 2.], [3., 4.]],
                          index=f.index[0:2], columns=['A', 'B'])
        key = (slice(None, 2), ['A', 'B'])
        f.ix[key] = piece
        assert_almost_equal(f.ix[0:2, ['A', 'B']].values,
                            piece.values)

        # rows unaligned
        f = self.mixed_frame.copy()
        piece = DataFrame([[1., 2.], [3., 4.], [5., 6.], [7., 8.]],
                          index=list(f.index[0:2]) + ['foo', 'bar'],
                          columns=['A', 'B'])
        key = (slice(None, 2), ['A', 'B'])
        f.ix[key] = piece
        assert_almost_equal(f.ix[0:2:, ['A', 'B']].values,
                            piece.values[0:2])

        # key is unaligned with values
        f = self.mixed_frame.copy()
        piece = f.ix[:2, ['A']]
        piece.index = f.index[-2:]
        key = (slice(-2, None), ['A', 'B'])
        f.ix[key] = piece
        piece['B'] = np.nan
        assert_almost_equal(f.ix[-2:, ['A', 'B']].values,
                            piece.values)

        # ndarray
        f = self.mixed_frame.copy()
        piece = self.mixed_frame.ix[:2, ['A', 'B']]
        key = (slice(-2, None), ['A', 'B'])
        f.ix[key] = piece.values
        assert_almost_equal(f.ix[-2:, ['A', 'B']].values,
                            piece.values)

        # needs upcasting
        df = DataFrame([[1, 2, 'foo'], [3, 4, 'bar']], columns=['A', 'B', 'C'])
        df2 = df.copy()
        df2.ix[:, ['A', 'B']] = df.ix[:, ['A', 'B']] + 0.5
        expected = df.reindex(columns=['A', 'B'])
        expected += 0.5
        expected['C'] = df['C']
        assert_frame_equal(df2, expected)

    def test_setitem_frame_align(self):
        piece = self.frame.ix[:2, ['A', 'B']]
        piece.index = self.frame.index[-2:]
        piece.columns = ['A', 'B']
        self.frame.ix[-2:, ['A', 'B']] = piece
        assert_almost_equal(self.frame.ix[-2:, ['A', 'B']].values,
                            piece.values)

    def test_getitem_setitem_ix_duplicates(self):
        # #1201
        df = DataFrame(np.random.randn(5, 3),
                       index=['foo', 'foo', 'bar', 'baz', 'bar'])

        result = df.ix['foo']
        expected = df[:2]
        assert_frame_equal(result, expected)

        result = df.ix['bar']
        expected = df.ix[[2, 4]]
        assert_frame_equal(result, expected)

        result = df.ix['baz']
        expected = df.ix[3]
        assert_series_equal(result, expected)

    def test_getitem_ix_boolean_duplicates_multiple(self):
        # #1201
        df = DataFrame(np.random.randn(5, 3),
                       index=['foo', 'foo', 'bar', 'baz', 'bar'])

        result = df.ix[['bar']]
        exp = df.ix[[2, 4]]
        assert_frame_equal(result, exp)

        result = df.ix[df[1] > 0]
        exp = df[df[1] > 0]
        assert_frame_equal(result, exp)

        result = df.ix[df[0] > 0]
        exp = df[df[0] > 0]
        assert_frame_equal(result, exp)

    def test_getitem_setitem_ix_bool_keyerror(self):
        # #2199
        df = DataFrame({'a': [1, 2, 3]})

        self.assertRaises(KeyError, df.ix.__getitem__, False)
        self.assertRaises(KeyError, df.ix.__getitem__, True)

        self.assertRaises(KeyError, df.ix.__setitem__, False, 0)
        self.assertRaises(KeyError, df.ix.__setitem__, True, 0)

    def test_getitem_list_duplicates(self):
        # #1943
        df = DataFrame(np.random.randn(4, 4), columns=list('AABC'))
        df.columns.name = 'foo'

        result = df[['B', 'C']]
        self.assertEqual(result.columns.name, 'foo')

        expected = df.ix[:, 2:]
        assert_frame_equal(result, expected)

    def test_get_value(self):
        for idx in self.frame.index:
            for col in self.frame.columns:
                result = self.frame.get_value(idx, col)
                expected = self.frame[col][idx]
                self.assertEqual(result, expected)

    def test_lookup(self):
        def alt(df, rows, cols, dtype):
            result = []
            for r, c in zip(rows, cols):
                result.append(df.get_value(r, c))
            return np.array(result, dtype=dtype)

        def testit(df):
            rows = list(df.index) * len(df.columns)
            cols = list(df.columns) * len(df.index)
            result = df.lookup(rows, cols)
            expected = alt(df, rows, cols, dtype=np.object_)
            tm.assert_almost_equal(result, expected, check_dtype=False)

        testit(self.mixed_frame)
        testit(self.frame)

        df = DataFrame({'label': ['a', 'b', 'a', 'c'],
                        'mask_a': [True, True, False, True],
                        'mask_b': [True, False, False, False],
                        'mask_c': [False, True, False, True]})
        df['mask'] = df.lookup(df.index, 'mask_' + df['label'])
        exp_mask = alt(df, df.index, 'mask_' + df['label'], dtype=np.bool_)
        tm.assert_series_equal(df['mask'], pd.Series(exp_mask, name='mask'))
        self.assertEqual(df['mask'].dtype, np.bool_)

        with tm.assertRaises(KeyError):
            self.frame.lookup(['xyz'], ['A'])

        with tm.assertRaises(KeyError):
            self.frame.lookup([self.frame.index[0]], ['xyz'])

        with tm.assertRaisesRegexp(ValueError, 'same size'):
            self.frame.lookup(['a', 'b', 'c'], ['a'])

    def test_set_value(self):
        for idx in self.frame.index:
            for col in self.frame.columns:
                self.frame.set_value(idx, col, 1)
                self.assertEqual(self.frame[col][idx], 1)

    def test_set_value_resize(self):

        res = self.frame.set_value('foobar', 'B', 0)
        self.assertIs(res, self.frame)
        self.assertEqual(res.index[-1], 'foobar')
        self.assertEqual(res.get_value('foobar', 'B'), 0)

        self.frame.loc['foobar', 'qux'] = 0
        self.assertEqual(self.frame.get_value('foobar', 'qux'), 0)

        res = self.frame.copy()
        res3 = res.set_value('foobar', 'baz', 'sam')
        self.assertEqual(res3['baz'].dtype, np.object_)

        res = self.frame.copy()
        res3 = res.set_value('foobar', 'baz', True)
        self.assertEqual(res3['baz'].dtype, np.object_)

        res = self.frame.copy()
        res3 = res.set_value('foobar', 'baz', 5)
        self.assertTrue(is_float_dtype(res3['baz']))
        self.assertTrue(isnull(res3['baz'].drop(['foobar'])).all())
        self.assertRaises(ValueError, res3.set_value, 'foobar', 'baz', 'sam')

    def test_set_value_with_index_dtype_change(self):
        df_orig = DataFrame(randn(3, 3), index=lrange(3), columns=list('ABC'))

        # this is actually ambiguous as the 2 is interpreted as a positional
        # so column is not created
        df = df_orig.copy()
        df.set_value('C', 2, 1.0)
        self.assertEqual(list(df.index), list(df_orig.index) + ['C'])
        # self.assertEqual(list(df.columns), list(df_orig.columns) + [2])

        df = df_orig.copy()
        df.loc['C', 2] = 1.0
        self.assertEqual(list(df.index), list(df_orig.index) + ['C'])
        # self.assertEqual(list(df.columns), list(df_orig.columns) + [2])

        # create both new
        df = df_orig.copy()
        df.set_value('C', 'D', 1.0)
        self.assertEqual(list(df.index), list(df_orig.index) + ['C'])
        self.assertEqual(list(df.columns), list(df_orig.columns) + ['D'])

        df = df_orig.copy()
        df.loc['C', 'D'] = 1.0
        self.assertEqual(list(df.index), list(df_orig.index) + ['C'])
        self.assertEqual(list(df.columns), list(df_orig.columns) + ['D'])

    def test_get_set_value_no_partial_indexing(self):
        # partial w/ MultiIndex raise exception
        index = MultiIndex.from_tuples([(0, 1), (0, 2), (1, 1), (1, 2)])
        df = DataFrame(index=index, columns=lrange(4))
        self.assertRaises(KeyError, df.get_value, 0, 1)
        # self.assertRaises(KeyError, df.set_value, 0, 1, 0)

    def test_single_element_ix_dont_upcast(self):
        self.frame['E'] = 1
        self.assertTrue(issubclass(self.frame['E'].dtype.type,
                                   (int, np.integer)))

        result = self.frame.ix[self.frame.index[5], 'E']
        self.assertTrue(is_integer(result))

    def test_irow(self):
        df = DataFrame(np.random.randn(10, 4), index=lrange(0, 20, 2))

        # 10711, deprecated
        with tm.assert_produces_warning(FutureWarning):
            df.irow(1)

        result = df.iloc[1]
        exp = df.ix[2]
        assert_series_equal(result, exp)

        result = df.iloc[2]
        exp = df.ix[4]
        assert_series_equal(result, exp)

        # slice
        result = df.iloc[slice(4, 8)]
        expected = df.ix[8:14]
        assert_frame_equal(result, expected)

        # verify slice is view
        # setting it makes it raise/warn
        def f():
            result[2] = 0.
        self.assertRaises(com.SettingWithCopyError, f)
        exp_col = df[2].copy()
        exp_col[4:8] = 0.
        assert_series_equal(df[2], exp_col)

        # list of integers
        result = df.iloc[[1, 2, 4, 6]]
        expected = df.reindex(df.index[[1, 2, 4, 6]])
        assert_frame_equal(result, expected)

    def test_icol(self):

        df = DataFrame(np.random.randn(4, 10), columns=lrange(0, 20, 2))

        # 10711, deprecated
        with tm.assert_produces_warning(FutureWarning):
            df.icol(1)

        result = df.iloc[:, 1]
        exp = df.ix[:, 2]
        assert_series_equal(result, exp)

        result = df.iloc[:, 2]
        exp = df.ix[:, 4]
        assert_series_equal(result, exp)

        # slice
        result = df.iloc[:, slice(4, 8)]
        expected = df.ix[:, 8:14]
        assert_frame_equal(result, expected)

        # verify slice is view
        # and that we are setting a copy
        def f():
            result[8] = 0.
        self.assertRaises(com.SettingWithCopyError, f)
        self.assertTrue((df[8] == 0).all())

        # list of integers
        result = df.iloc[:, [1, 2, 4, 6]]
        expected = df.reindex(columns=df.columns[[1, 2, 4, 6]])
        assert_frame_equal(result, expected)

    def test_irow_icol_duplicates(self):
        # 10711, deprecated

        df = DataFrame(np.random.rand(3, 3), columns=list('ABC'),
                       index=list('aab'))

        result = df.iloc[0]
        result2 = df.ix[0]
        tm.assertIsInstance(result, Series)
        assert_almost_equal(result.values, df.values[0])
        assert_series_equal(result, result2)

        result = df.T.iloc[:, 0]
        result2 = df.T.ix[:, 0]
        tm.assertIsInstance(result, Series)
        assert_almost_equal(result.values, df.values[0])
        assert_series_equal(result, result2)

        # multiindex
        df = DataFrame(np.random.randn(3, 3),
                       columns=[['i', 'i', 'j'], ['A', 'A', 'B']],
                       index=[['i', 'i', 'j'], ['X', 'X', 'Y']])

        rs = df.iloc[0]
        xp = df.ix[0]
        assert_series_equal(rs, xp)

        rs = df.iloc[:, 0]
        xp = df.T.ix[0]
        assert_series_equal(rs, xp)

        rs = df.iloc[:, [0]]
        xp = df.ix[:, [0]]
        assert_frame_equal(rs, xp)

        # #2259
        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1, 1, 2])
        result = df.iloc[:, [0]]
        expected = df.take([0], axis=1)
        assert_frame_equal(result, expected)

    def test_icol_sparse_propegate_fill_value(self):
        from pandas.sparse.api import SparseDataFrame
        df = SparseDataFrame({'A': [999, 1]}, default_fill_value=999)
        self.assertTrue(len(df['A'].sp_values) == len(df.iloc[:, 0].sp_values))

    def test_iget_value(self):
        # 10711 deprecated

        with tm.assert_produces_warning(FutureWarning):
            self.frame.iget_value(0, 0)

        for i, row in enumerate(self.frame.index):
            for j, col in enumerate(self.frame.columns):
                result = self.frame.iat[i, j]
                expected = self.frame.at[row, col]
                self.assertEqual(result, expected)

    def test_nested_exception(self):
        # Ignore the strange way of triggering the problem
        # (which may get fixed), it's just a way to trigger
        # the issue or reraising an outer exception without
        # a named argument
        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6],
                        "c": [7, 8, 9]}).set_index(["a", "b"])
        l = list(df.index)
        l[0] = ["a", "b"]
        df.index = l

        try:
            repr(df)
        except Exception as e:
            self.assertNotEqual(type(e), UnboundLocalError)

    def test_reindex_methods(self):
        df = pd.DataFrame({'x': list(range(5))})
        target = np.array([-0.1, 0.9, 1.1, 1.5])

        for method, expected_values in [('nearest', [0, 1, 1, 2]),
                                        ('pad', [np.nan, 0, 1, 1]),
                                        ('backfill', [0, 1, 2, 2])]:
            expected = pd.DataFrame({'x': expected_values}, index=target)
            actual = df.reindex(target, method=method)
            assert_frame_equal(expected, actual)

            actual = df.reindex_like(df, method=method, tolerance=0)
            assert_frame_equal(df, actual)

            actual = df.reindex(target, method=method, tolerance=1)
            assert_frame_equal(expected, actual)

            e2 = expected[::-1]
            actual = df.reindex(target[::-1], method=method)
            assert_frame_equal(e2, actual)

            new_order = [3, 0, 2, 1]
            e2 = expected.iloc[new_order]
            actual = df.reindex(target[new_order], method=method)
            assert_frame_equal(e2, actual)

            switched_method = ('pad' if method == 'backfill'
                               else 'backfill' if method == 'pad'
                               else method)
            actual = df[::-1].reindex(target, method=switched_method)
            assert_frame_equal(expected, actual)

        expected = pd.DataFrame({'x': [0, 1, 1, np.nan]}, index=target)
        actual = df.reindex(target, method='nearest', tolerance=0.2)
        assert_frame_equal(expected, actual)

    def test_non_monotonic_reindex_methods(self):
        dr = pd.date_range('2013-08-01', periods=6, freq='B')
        data = np.random.randn(6, 1)
        df = pd.DataFrame(data, index=dr, columns=list('A'))
        df_rev = pd.DataFrame(data, index=dr[[3, 4, 5] + [0, 1, 2]],
                              columns=list('A'))
        # index is not monotonic increasing or decreasing
        self.assertRaises(ValueError, df_rev.reindex, df.index, method='pad')
        self.assertRaises(ValueError, df_rev.reindex, df.index, method='ffill')
        self.assertRaises(ValueError, df_rev.reindex, df.index, method='bfill')
        self.assertRaises(ValueError, df_rev.reindex,
                          df.index, method='nearest')

    def test_reindex_level(self):
        from itertools import permutations
        icol = ['jim', 'joe', 'jolie']

        def verify_first_level(df, level, idx, check_index_type=True):
            f = lambda val: np.nonzero(df[level] == val)[0]
            i = np.concatenate(list(map(f, idx)))
            left = df.set_index(icol).reindex(idx, level=level)
            right = df.iloc[i].set_index(icol)
            assert_frame_equal(left, right, check_index_type=check_index_type)

        def verify(df, level, idx, indexer, check_index_type=True):
            left = df.set_index(icol).reindex(idx, level=level)
            right = df.iloc[indexer].set_index(icol)
            assert_frame_equal(left, right, check_index_type=check_index_type)

        df = pd.DataFrame({'jim': list('B' * 4 + 'A' * 2 + 'C' * 3),
                           'joe': list('abcdeabcd')[::-1],
                           'jolie': [10, 20, 30] * 3,
                           'joline': np.random.randint(0, 1000, 9)})

        target = [['C', 'B', 'A'], ['F', 'C', 'A', 'D'], ['A'],
                  ['A', 'B', 'C'], ['C', 'A', 'B'], ['C', 'B'], ['C', 'A'],
                  ['A', 'B'], ['B', 'A', 'C']]

        for idx in target:
            verify_first_level(df, 'jim', idx)

        # reindex by these causes different MultiIndex levels
        for idx in [['D', 'F'], ['A', 'C', 'B']]:
            verify_first_level(df, 'jim', idx, check_index_type=False)

        verify(df, 'joe', list('abcde'), [3, 2, 1, 0, 5, 4, 8, 7, 6])
        verify(df, 'joe', list('abcd'), [3, 2, 1, 0, 5, 8, 7, 6])
        verify(df, 'joe', list('abc'), [3, 2, 1, 8, 7, 6])
        verify(df, 'joe', list('eca'), [1, 3, 4, 6, 8])
        verify(df, 'joe', list('edc'), [0, 1, 4, 5, 6])
        verify(df, 'joe', list('eadbc'), [3, 0, 2, 1, 4, 5, 8, 7, 6])
        verify(df, 'joe', list('edwq'), [0, 4, 5])
        verify(df, 'joe', list('wq'), [], check_index_type=False)

        df = DataFrame({'jim': ['mid'] * 5 + ['btm'] * 8 + ['top'] * 7,
                        'joe': ['3rd'] * 2 + ['1st'] * 3 + ['2nd'] * 3 +
                        ['1st'] * 2 + ['3rd'] * 3 + ['1st'] * 2 +
                        ['3rd'] * 3 + ['2nd'] * 2,
                        # this needs to be jointly unique with jim and joe or
                        # reindexing will fail ~1.5% of the time, this works
                        # out to needing unique groups of same size as joe
                        'jolie': np.concatenate([
                            np.random.choice(1000, x, replace=False)
                            for x in [2, 3, 3, 2, 3, 2, 3, 2]]),
                        'joline': np.random.randn(20).round(3) * 10})

        for idx in permutations(df['jim'].unique()):
            for i in range(3):
                verify_first_level(df, 'jim', idx[:i + 1])

        i = [2, 3, 4, 0, 1, 8, 9, 5, 6, 7, 10,
             11, 12, 13, 14, 18, 19, 15, 16, 17]
        verify(df, 'joe', ['1st', '2nd', '3rd'], i)

        i = [0, 1, 2, 3, 4, 10, 11, 12, 5, 6,
             7, 8, 9, 15, 16, 17, 18, 19, 13, 14]
        verify(df, 'joe', ['3rd', '2nd', '1st'], i)

        i = [0, 1, 5, 6, 7, 10, 11, 12, 18, 19, 15, 16, 17]
        verify(df, 'joe', ['2nd', '3rd'], i)

        i = [0, 1, 2, 3, 4, 10, 11, 12, 8, 9, 15, 16, 17, 13, 14]
        verify(df, 'joe', ['3rd', '1st'], i)

    def test_getitem_ix_float_duplicates(self):
        df = pd.DataFrame(np.random.randn(3, 3),
                          index=[0.1, 0.2, 0.2], columns=list('abc'))
        expect = df.iloc[1:]
        assert_frame_equal(df.loc[0.2], expect)
        assert_frame_equal(df.ix[0.2], expect)

        expect = df.iloc[1:, 0]
        assert_series_equal(df.loc[0.2, 'a'], expect)

        df.index = [1, 0.2, 0.2]
        expect = df.iloc[1:]
        assert_frame_equal(df.loc[0.2], expect)
        assert_frame_equal(df.ix[0.2], expect)

        expect = df.iloc[1:, 0]
        assert_series_equal(df.loc[0.2, 'a'], expect)

        df = pd.DataFrame(np.random.randn(4, 3),
                          index=[1, 0.2, 0.2, 1], columns=list('abc'))
        expect = df.iloc[1:-1]
        assert_frame_equal(df.loc[0.2], expect)
        assert_frame_equal(df.ix[0.2], expect)

        expect = df.iloc[1:-1, 0]
        assert_series_equal(df.loc[0.2, 'a'], expect)

        df.index = [0.1, 0.2, 2, 0.2]
        expect = df.iloc[[1, -1]]
        assert_frame_equal(df.loc[0.2], expect)
        assert_frame_equal(df.ix[0.2], expect)

        expect = df.iloc[[1, -1], 0]
        assert_series_equal(df.loc[0.2, 'a'], expect)

    def test_setitem_with_sparse_value(self):
        # GH8131
        df = pd.DataFrame({'c_1': ['a', 'b', 'c'], 'n_1': [1., 2., 3.]})
        sp_series = pd.Series([0, 0, 1]).to_sparse(fill_value=0)
        df['new_column'] = sp_series
        assert_series_equal(df['new_column'], sp_series, check_names=False)

    def test_setitem_with_unaligned_sparse_value(self):
        df = pd.DataFrame({'c_1': ['a', 'b', 'c'], 'n_1': [1., 2., 3.]})
        sp_series = (pd.Series([0, 0, 1], index=[2, 1, 0])
                     .to_sparse(fill_value=0))
        df['new_column'] = sp_series
        exp = pd.Series([1, 0, 0], name='new_column')
        assert_series_equal(df['new_column'], exp)

    def test_setitem_with_unaligned_tz_aware_datetime_column(self):
        # GH 12981
        # Assignment of unaligned offset-aware datetime series.
        # Make sure timezone isn't lost
        column = pd.Series(pd.date_range('2015-01-01', periods=3, tz='utc'),
                           name='dates')
        df = pd.DataFrame({'dates': column})
        df['dates'] = column[[1, 0, 2]]
        assert_series_equal(df['dates'], column)

        df = pd.DataFrame({'dates': column})
        df.loc[[0, 1, 2], 'dates'] = column[[1, 0, 2]]
        assert_series_equal(df['dates'], column)

    def test_setitem_datetime_coercion(self):
        # GH 1048
        df = pd.DataFrame({'c': [pd.Timestamp('2010-10-01')] * 3})
        df.loc[0:1, 'c'] = np.datetime64('2008-08-08')
        self.assertEqual(pd.Timestamp('2008-08-08'), df.loc[0, 'c'])
        self.assertEqual(pd.Timestamp('2008-08-08'), df.loc[1, 'c'])
        df.loc[2, 'c'] = date(2005, 5, 5)
        self.assertEqual(pd.Timestamp('2005-05-05'), df.loc[2, 'c'])

    def test_setitem_datetimelike_with_inference(self):
        # GH 7592
        # assignment of timedeltas with NaT

        one_hour = timedelta(hours=1)
        df = DataFrame(index=date_range('20130101', periods=4))
        df['A'] = np.array([1 * one_hour] * 4, dtype='m8[ns]')
        df.loc[:, 'B'] = np.array([2 * one_hour] * 4, dtype='m8[ns]')
        df.loc[:3, 'C'] = np.array([3 * one_hour] * 3, dtype='m8[ns]')
        df.ix[:, 'D'] = np.array([4 * one_hour] * 4, dtype='m8[ns]')
        df.ix[:3, 'E'] = np.array([5 * one_hour] * 3, dtype='m8[ns]')
        df['F'] = np.timedelta64('NaT')
        df.ix[:-1, 'F'] = np.array([6 * one_hour] * 3, dtype='m8[ns]')
        df.ix[-3:, 'G'] = date_range('20130101', periods=3)
        df['H'] = np.datetime64('NaT')
        result = df.dtypes
        expected = Series([np.dtype('timedelta64[ns]')] * 6 +
                          [np.dtype('datetime64[ns]')] * 2,
                          index=list('ABCDEFGH'))
        assert_series_equal(result, expected)

    def test_at_time_between_time_datetimeindex(self):
        index = date_range("2012-01-01", "2012-01-05", freq='30min')
        df = DataFrame(randn(len(index), 5), index=index)
        akey = time(12, 0, 0)
        bkey = slice(time(13, 0, 0), time(14, 0, 0))
        ainds = [24, 72, 120, 168]
        binds = [26, 27, 28, 74, 75, 76, 122, 123, 124, 170, 171, 172]

        result = df.at_time(akey)
        expected = df.ix[akey]
        expected2 = df.ix[ainds]
        assert_frame_equal(result, expected)
        assert_frame_equal(result, expected2)
        self.assertEqual(len(result), 4)

        result = df.between_time(bkey.start, bkey.stop)
        expected = df.ix[bkey]
        expected2 = df.ix[binds]
        assert_frame_equal(result, expected)
        assert_frame_equal(result, expected2)
        self.assertEqual(len(result), 12)

        result = df.copy()
        result.ix[akey] = 0
        result = result.ix[akey]
        expected = df.ix[akey].copy()
        expected.ix[:] = 0
        assert_frame_equal(result, expected)

        result = df.copy()
        result.ix[akey] = 0
        result.ix[akey] = df.ix[ainds]
        assert_frame_equal(result, df)

        result = df.copy()
        result.ix[bkey] = 0
        result = result.ix[bkey]
        expected = df.ix[bkey].copy()
        expected.ix[:] = 0
        assert_frame_equal(result, expected)

        result = df.copy()
        result.ix[bkey] = 0
        result.ix[bkey] = df.ix[binds]
        assert_frame_equal(result, df)

    def test_xs(self):
        idx = self.frame.index[5]
        xs = self.frame.xs(idx)
        for item, value in compat.iteritems(xs):
            if np.isnan(value):
                self.assertTrue(np.isnan(self.frame[item][idx]))
            else:
                self.assertEqual(value, self.frame[item][idx])

        # mixed-type xs
        test_data = {
            'A': {'1': 1, '2': 2},
            'B': {'1': '1', '2': '2', '3': '3'},
        }
        frame = DataFrame(test_data)
        xs = frame.xs('1')
        self.assertEqual(xs.dtype, np.object_)
        self.assertEqual(xs['A'], 1)
        self.assertEqual(xs['B'], '1')

        with tm.assertRaises(KeyError):
            self.tsframe.xs(self.tsframe.index[0] - BDay())

        # xs get column
        series = self.frame.xs('A', axis=1)
        expected = self.frame['A']
        assert_series_equal(series, expected)

        # view is returned if possible
        series = self.frame.xs('A', axis=1)
        series[:] = 5
        self.assertTrue((expected == 5).all())

    def test_xs_corner(self):
        # pathological mixed-type reordering case
        df = DataFrame(index=[0])
        df['A'] = 1.
        df['B'] = 'foo'
        df['C'] = 2.
        df['D'] = 'bar'
        df['E'] = 3.

        xs = df.xs(0)
        exp = pd.Series([1., 'foo', 2., 'bar', 3.],
                        index=list('ABCDE'), name=0)
        tm.assert_series_equal(xs, exp)

        # no columns but Index(dtype=object)
        df = DataFrame(index=['a', 'b', 'c'])
        result = df.xs('a')
        expected = Series([], name='a', index=pd.Index([], dtype=object))
        assert_series_equal(result, expected)

    def test_xs_duplicates(self):
        df = DataFrame(randn(5, 2), index=['b', 'b', 'c', 'b', 'a'])

        cross = df.xs('c')
        exp = df.iloc[2]
        assert_series_equal(cross, exp)

    def test_xs_keep_level(self):
        df = (DataFrame({'day': {0: 'sat', 1: 'sun'},
                         'flavour': {0: 'strawberry', 1: 'strawberry'},
                         'sales': {0: 10, 1: 12},
                         'year': {0: 2008, 1: 2008}})
              .set_index(['year', 'flavour', 'day']))
        result = df.xs('sat', level='day', drop_level=False)
        expected = df[:1]
        assert_frame_equal(result, expected)

        result = df.xs([2008, 'sat'], level=['year', 'day'], drop_level=False)
        assert_frame_equal(result, expected)

    def test_xs_view(self):
        # in 0.14 this will return a view if possible a copy otherwise, but
        # this is numpy dependent

        dm = DataFrame(np.arange(20.).reshape(4, 5),
                       index=lrange(4), columns=lrange(5))

        dm.xs(2)[:] = 10
        self.assertTrue((dm.xs(2) == 10).all())

    def test_index_namedtuple(self):
        from collections import namedtuple
        IndexType = namedtuple("IndexType", ["a", "b"])
        idx1 = IndexType("foo", "bar")
        idx2 = IndexType("baz", "bof")
        index = Index([idx1, idx2],
                      name="composite_index", tupleize_cols=False)
        df = DataFrame([(1, 2), (3, 4)], index=index, columns=["A", "B"])
        result = df.ix[IndexType("foo", "bar")]["A"]
        self.assertEqual(result, 1)

    def test_boolean_indexing(self):
        idx = lrange(3)
        cols = ['A', 'B', 'C']
        df1 = DataFrame(index=idx, columns=cols,
                        data=np.array([[0.0, 0.5, 1.0],
                                       [1.5, 2.0, 2.5],
                                       [3.0, 3.5, 4.0]],
                                      dtype=float))
        df2 = DataFrame(index=idx, columns=cols,
                        data=np.ones((len(idx), len(cols))))

        expected = DataFrame(index=idx, columns=cols,
                             data=np.array([[0.0, 0.5, 1.0],
                                            [1.5, 2.0, -1],
                                            [-1, -1, -1]], dtype=float))

        df1[df1 > 2.0 * df2] = -1
        assert_frame_equal(df1, expected)
        with assertRaisesRegexp(ValueError, 'Item wrong length'):
            df1[df1.index[:-1] > 2] = -1

    def test_boolean_indexing_mixed(self):
        df = DataFrame({
            long(0): {35: np.nan, 40: np.nan, 43: np.nan,
                      49: np.nan, 50: np.nan},
            long(1): {35: np.nan,
                      40: 0.32632316859446198,
                      43: np.nan,
                      49: 0.32632316859446198,
                      50: 0.39114724480578139},
            long(2): {35: np.nan, 40: np.nan, 43: 0.29012581014105987,
                      49: np.nan, 50: np.nan},
            long(3): {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan,
                      50: np.nan},
            long(4): {35: 0.34215328467153283, 40: np.nan, 43: np.nan,
                      49: np.nan, 50: np.nan},
            'y': {35: 0, 40: 0, 43: 0, 49: 0, 50: 1}})

        # mixed int/float ok
        df2 = df.copy()
        df2[df2 > 0.3] = 1
        expected = df.copy()
        expected.loc[40, 1] = 1
        expected.loc[49, 1] = 1
        expected.loc[50, 1] = 1
        expected.loc[35, 4] = 1
        assert_frame_equal(df2, expected)

        df['foo'] = 'test'
        with tm.assertRaisesRegexp(TypeError, 'boolean setting on mixed-type'):
            df[df > 0.3] = 1

    def test_where(self):
        default_frame = DataFrame(np.random.randn(5, 3),
                                  columns=['A', 'B', 'C'])

        def _safe_add(df):
            # only add to the numeric items
            def is_ok(s):
                return (issubclass(s.dtype.type, (np.integer, np.floating)) and
                        s.dtype != 'uint8')

            return DataFrame(dict([(c, s + 1) if is_ok(s) else (c, s)
                                   for c, s in compat.iteritems(df)]))

        def _check_get(df, cond, check_dtypes=True):
            other1 = _safe_add(df)
            rs = df.where(cond, other1)
            rs2 = df.where(cond.values, other1)
            for k, v in rs.iteritems():
                exp = Series(
                    np.where(cond[k], df[k], other1[k]), index=v.index)
                assert_series_equal(v, exp, check_names=False)
            assert_frame_equal(rs, rs2)

            # dtypes
            if check_dtypes:
                self.assertTrue((rs.dtypes == df.dtypes).all())

        # check getting
        for df in [default_frame, self.mixed_frame,
                   self.mixed_float, self.mixed_int]:
            cond = df > 0
            _check_get(df, cond)

        # upcasting case (GH # 2794)
        df = DataFrame(dict([(c, Series([1] * 3, dtype=c))
                             for c in ['int64', 'int32',
                                       'float32', 'float64']]))
        df.ix[1, :] = 0
        result = df.where(df >= 0).get_dtype_counts()

        # when we don't preserve boolean casts
        #
        # expected = Series({ 'float32' : 1, 'float64' : 3 })

        expected = Series({'float32': 1, 'float64': 1, 'int32': 1, 'int64': 1})
        assert_series_equal(result, expected)

        # aligning
        def _check_align(df, cond, other, check_dtypes=True):
            rs = df.where(cond, other)
            for i, k in enumerate(rs.columns):
                result = rs[k]
                d = df[k].values
                c = cond[k].reindex(df[k].index).fillna(False).values

                if is_scalar(other):
                    o = other
                else:
                    if isinstance(other, np.ndarray):
                        o = Series(other[:, i], index=result.index).values
                    else:
                        o = other[k].values

                new_values = d if c.all() else np.where(c, d, o)
                expected = Series(new_values, index=result.index, name=k)

                # since we can't always have the correct numpy dtype
                # as numpy doesn't know how to downcast, don't check
                assert_series_equal(result, expected, check_dtype=False)

            # dtypes
            # can't check dtype when other is an ndarray

            if check_dtypes and not isinstance(other, np.ndarray):
                self.assertTrue((rs.dtypes == df.dtypes).all())

        for df in [self.mixed_frame, self.mixed_float, self.mixed_int]:

            # other is a frame
            cond = (df > 0)[1:]
            _check_align(df, cond, _safe_add(df))

            # check other is ndarray
            cond = df > 0
            _check_align(df, cond, (_safe_add(df).values))

            # integers are upcast, so don't check the dtypes
            cond = df > 0
            check_dtypes = all([not issubclass(s.type, np.integer)
                                for s in df.dtypes])
            _check_align(df, cond, np.nan, check_dtypes=check_dtypes)

        # invalid conditions
        df = default_frame
        err1 = (df + 1).values[0:2, :]
        self.assertRaises(ValueError, df.where, cond, err1)

        err2 = cond.ix[:2, :].values
        other1 = _safe_add(df)
        self.assertRaises(ValueError, df.where, err2, other1)

        self.assertRaises(ValueError, df.mask, True)
        self.assertRaises(ValueError, df.mask, 0)

        # where inplace
        def _check_set(df, cond, check_dtypes=True):
            dfi = df.copy()
            econd = cond.reindex_like(df).fillna(True)
            expected = dfi.mask(~econd)

            dfi.where(cond, np.nan, inplace=True)
            assert_frame_equal(dfi, expected)

            # dtypes (and confirm upcasts)x
            if check_dtypes:
                for k, v in compat.iteritems(df.dtypes):
                    if issubclass(v.type, np.integer) and not cond[k].all():
                        v = np.dtype('float64')
                    self.assertEqual(dfi[k].dtype, v)

        for df in [default_frame, self.mixed_frame, self.mixed_float,
                   self.mixed_int]:

            cond = df > 0
            _check_set(df, cond)

            cond = df >= 0
            _check_set(df, cond)

            # aligining
            cond = (df >= 0)[1:]
            _check_set(df, cond)

        # GH 10218
        # test DataFrame.where with Series slicing
        df = DataFrame({'a': range(3), 'b': range(4, 7)})
        result = df.where(df['a'] == 1)
        expected = df[df['a'] == 1].reindex(df.index)
        assert_frame_equal(result, expected)

    def test_where_bug(self):

        # GH 2793

        df = DataFrame({'a': [1.0, 2.0, 3.0, 4.0], 'b': [
                       4.0, 3.0, 2.0, 1.0]}, dtype='float64')
        expected = DataFrame({'a': [np.nan, np.nan, 3.0, 4.0], 'b': [
                             4.0, 3.0, np.nan, np.nan]}, dtype='float64')
        result = df.where(df > 2, np.nan)
        assert_frame_equal(result, expected)

        result = df.copy()
        result.where(result > 2, np.nan, inplace=True)
        assert_frame_equal(result, expected)

        # mixed
        for dtype in ['int16', 'int8', 'int32', 'int64']:
            df = DataFrame({'a': np.array([1, 2, 3, 4], dtype=dtype),
                            'b': np.array([4.0, 3.0, 2.0, 1.0],
                                          dtype='float64')})

            expected = DataFrame({'a': [np.nan, np.nan, 3.0, 4.0],
                                  'b': [4.0, 3.0, np.nan, np.nan]},
                                 dtype='float64')

            result = df.where(df > 2, np.nan)
            assert_frame_equal(result, expected)

            result = df.copy()
            result.where(result > 2, np.nan, inplace=True)
            assert_frame_equal(result, expected)

        # transpositional issue
        # GH7506
        a = DataFrame({0: [1, 2], 1: [3, 4], 2: [5, 6]})
        b = DataFrame({0: [np.nan, 8], 1: [9, np.nan], 2: [np.nan, np.nan]})
        do_not_replace = b.isnull() | (a > b)

        expected = a.copy()
        expected[~do_not_replace] = b

        result = a.where(do_not_replace, b)
        assert_frame_equal(result, expected)

        a = DataFrame({0: [4, 6], 1: [1, 0]})
        b = DataFrame({0: [np.nan, 3], 1: [3, np.nan]})
        do_not_replace = b.isnull() | (a > b)

        expected = a.copy()
        expected[~do_not_replace] = b

        result = a.where(do_not_replace, b)
        assert_frame_equal(result, expected)

    def test_where_datetime(self):

        # GH 3311
        df = DataFrame(dict(A=date_range('20130102', periods=5),
                            B=date_range('20130104', periods=5),
                            C=np.random.randn(5)))

        stamp = datetime(2013, 1, 3)
        result = df[df > stamp]
        expected = df.copy()
        expected.loc[[0, 1], 'A'] = np.nan
        assert_frame_equal(result, expected)

    def test_where_none(self):
        # GH 4667
        # setting with None changes dtype
        df = DataFrame({'series': Series(range(10))}).astype(float)
        df[df > 7] = None
        expected = DataFrame(
            {'series': Series([0, 1, 2, 3, 4, 5, 6, 7, np.nan, np.nan])})
        assert_frame_equal(df, expected)

        # GH 7656
        df = DataFrame([{'A': 1, 'B': np.nan, 'C': 'Test'}, {
                       'A': np.nan, 'B': 'Test', 'C': np.nan}])
        expected = df.where(~isnull(df), None)
        with tm.assertRaisesRegexp(TypeError, 'boolean setting on mixed-type'):
            df.where(~isnull(df), None, inplace=True)

    def test_where_align(self):

        def create():
            df = DataFrame(np.random.randn(10, 3))
            df.iloc[3:5, 0] = np.nan
            df.iloc[4:6, 1] = np.nan
            df.iloc[5:8, 2] = np.nan
            return df

        # series
        df = create()
        expected = df.fillna(df.mean())
        result = df.where(pd.notnull(df), df.mean(), axis='columns')
        assert_frame_equal(result, expected)

        df.where(pd.notnull(df), df.mean(), inplace=True, axis='columns')
        assert_frame_equal(df, expected)

        df = create().fillna(0)
        expected = df.apply(lambda x, y: x.where(x > 0, y), y=df[0])
        result = df.where(df > 0, df[0], axis='index')
        assert_frame_equal(result, expected)
        result = df.where(df > 0, df[0], axis='rows')
        assert_frame_equal(result, expected)

        # frame
        df = create()
        expected = df.fillna(1)
        result = df.where(pd.notnull(df), DataFrame(
            1, index=df.index, columns=df.columns))
        assert_frame_equal(result, expected)

    def test_where_complex(self):
        # GH 6345
        expected = DataFrame(
            [[1 + 1j, 2], [np.nan, 4 + 1j]], columns=['a', 'b'])
        df = DataFrame([[1 + 1j, 2], [5 + 1j, 4 + 1j]], columns=['a', 'b'])
        df[df.abs() >= 5] = np.nan
        assert_frame_equal(df, expected)

    def test_where_axis(self):
        # GH 9736
        df = DataFrame(np.random.randn(2, 2))
        mask = DataFrame([[False, False], [False, False]])
        s = Series([0, 1])

        expected = DataFrame([[0, 0], [1, 1]], dtype='float64')
        result = df.where(mask, s, axis='index')
        assert_frame_equal(result, expected)

        result = df.copy()
        result.where(mask, s, axis='index', inplace=True)
        assert_frame_equal(result, expected)

        expected = DataFrame([[0, 1], [0, 1]], dtype='float64')
        result = df.where(mask, s, axis='columns')
        assert_frame_equal(result, expected)

        result = df.copy()
        result.where(mask, s, axis='columns', inplace=True)
        assert_frame_equal(result, expected)

        # Upcast needed
        df = DataFrame([[1, 2], [3, 4]], dtype='int64')
        mask = DataFrame([[False, False], [False, False]])
        s = Series([0, np.nan])

        expected = DataFrame([[0, 0], [np.nan, np.nan]], dtype='float64')
        result = df.where(mask, s, axis='index')
        assert_frame_equal(result, expected)

        result = df.copy()
        result.where(mask, s, axis='index', inplace=True)
        assert_frame_equal(result, expected)

        expected = DataFrame([[0, np.nan], [0, np.nan]], dtype='float64')
        result = df.where(mask, s, axis='columns')
        assert_frame_equal(result, expected)

        expected = DataFrame({0: np.array([0, 0], dtype='int64'),
                              1: np.array([np.nan, np.nan], dtype='float64')})
        result = df.copy()
        result.where(mask, s, axis='columns', inplace=True)
        assert_frame_equal(result, expected)

        # Multiple dtypes (=> multiple Blocks)
        df = pd.concat([DataFrame(np.random.randn(10, 2)),
                        DataFrame(np.random.randint(0, 10, size=(10, 2)))],
                       ignore_index=True, axis=1)
        mask = DataFrame(False, columns=df.columns, index=df.index)
        s1 = Series(1, index=df.columns)
        s2 = Series(2, index=df.index)

        result = df.where(mask, s1, axis='columns')
        expected = DataFrame(1.0, columns=df.columns, index=df.index)
        expected[2] = expected[2].astype(int)
        expected[3] = expected[3].astype(int)
        assert_frame_equal(result, expected)

        result = df.copy()
        result.where(mask, s1, axis='columns', inplace=True)
        assert_frame_equal(result, expected)

        result = df.where(mask, s2, axis='index')
        expected = DataFrame(2.0, columns=df.columns, index=df.index)
        expected[2] = expected[2].astype(int)
        expected[3] = expected[3].astype(int)
        assert_frame_equal(result, expected)

        result = df.copy()
        result.where(mask, s2, axis='index', inplace=True)
        assert_frame_equal(result, expected)

        # DataFrame vs DataFrame
        d1 = df.copy().drop(1, axis=0)
        expected = df.copy()
        expected.loc[1, :] = np.nan

        result = df.where(mask, d1)
        assert_frame_equal(result, expected)
        result = df.where(mask, d1, axis='index')
        assert_frame_equal(result, expected)
        result = df.copy()
        result.where(mask, d1, inplace=True)
        assert_frame_equal(result, expected)
        result = df.copy()
        result.where(mask, d1, inplace=True, axis='index')
        assert_frame_equal(result, expected)

        d2 = df.copy().drop(1, axis=1)
        expected = df.copy()
        expected.loc[:, 1] = np.nan

        result = df.where(mask, d2)
        assert_frame_equal(result, expected)
        result = df.where(mask, d2, axis='columns')
        assert_frame_equal(result, expected)
        result = df.copy()
        result.where(mask, d2, inplace=True)
        assert_frame_equal(result, expected)
        result = df.copy()
        result.where(mask, d2, inplace=True, axis='columns')
        assert_frame_equal(result, expected)

    def test_where_callable(self):
        # GH 12533
        df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        result = df.where(lambda x: x > 4, lambda x: x + 1)
        exp = DataFrame([[2, 3, 4], [5, 5, 6], [7, 8, 9]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result, df.where(df > 4, df + 1))

        # return ndarray and scalar
        result = df.where(lambda x: (x % 2 == 0).values, lambda x: 99)
        exp = DataFrame([[99, 2, 99], [4, 99, 6], [99, 8, 99]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result, df.where(df % 2 == 0, 99))

        # chain
        result = (df + 2).where(lambda x: x > 8, lambda x: x + 10)
        exp = DataFrame([[13, 14, 15], [16, 17, 18], [9, 10, 11]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result,
                              (df + 2).where((df + 2) > 8, (df + 2) + 10))

    def test_mask(self):
        df = DataFrame(np.random.randn(5, 3))
        cond = df > 0

        rs = df.where(cond, np.nan)
        assert_frame_equal(rs, df.mask(df <= 0))
        assert_frame_equal(rs, df.mask(~cond))

        other = DataFrame(np.random.randn(5, 3))
        rs = df.where(cond, other)
        assert_frame_equal(rs, df.mask(df <= 0, other))
        assert_frame_equal(rs, df.mask(~cond, other))

    def test_mask_inplace(self):
        # GH8801
        df = DataFrame(np.random.randn(5, 3))
        cond = df > 0

        rdf = df.copy()

        rdf.where(cond, inplace=True)
        assert_frame_equal(rdf, df.where(cond))
        assert_frame_equal(rdf, df.mask(~cond))

        rdf = df.copy()
        rdf.where(cond, -df, inplace=True)
        assert_frame_equal(rdf, df.where(cond, -df))
        assert_frame_equal(rdf, df.mask(~cond, -df))

    def test_mask_edge_case_1xN_frame(self):
        # GH4071
        df = DataFrame([[1, 2]])
        res = df.mask(DataFrame([[True, False]]))
        expec = DataFrame([[nan, 2]])
        assert_frame_equal(res, expec)

    def test_mask_callable(self):
        # GH 12533
        df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        result = df.mask(lambda x: x > 4, lambda x: x + 1)
        exp = DataFrame([[1, 2, 3], [4, 6, 7], [8, 9, 10]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result, df.mask(df > 4, df + 1))

        # return ndarray and scalar
        result = df.mask(lambda x: (x % 2 == 0).values, lambda x: 99)
        exp = DataFrame([[1, 99, 3], [99, 5, 99], [7, 99, 9]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result, df.mask(df % 2 == 0, 99))

        # chain
        result = (df + 2).mask(lambda x: x > 8, lambda x: x + 10)
        exp = DataFrame([[3, 4, 5], [6, 7, 8], [19, 20, 21]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result,
                              (df + 2).mask((df + 2) > 8, (df + 2) + 10))

    def test_head_tail(self):
        assert_frame_equal(self.frame.head(), self.frame[:5])
        assert_frame_equal(self.frame.tail(), self.frame[-5:])

        assert_frame_equal(self.frame.head(0), self.frame[0:0])
        assert_frame_equal(self.frame.tail(0), self.frame[0:0])

        assert_frame_equal(self.frame.head(-1), self.frame[:-1])
        assert_frame_equal(self.frame.tail(-1), self.frame[1:])
        assert_frame_equal(self.frame.head(1), self.frame[:1])
        assert_frame_equal(self.frame.tail(1), self.frame[-1:])
        # with a float index
        df = self.frame.copy()
        df.index = np.arange(len(self.frame)) + 0.1
        assert_frame_equal(df.head(), df.iloc[:5])
        assert_frame_equal(df.tail(), df.iloc[-5:])
        assert_frame_equal(df.head(0), df[0:0])
        assert_frame_equal(df.tail(0), df[0:0])
        assert_frame_equal(df.head(-1), df.iloc[:-1])
        assert_frame_equal(df.tail(-1), df.iloc[1:])
        # test empty dataframe
        empty_df = DataFrame()
        assert_frame_equal(empty_df.tail(), empty_df)
        assert_frame_equal(empty_df.head(), empty_df)

    def test_type_error_multiindex(self):
        # See gh-12218
        df = DataFrame(columns=['i', 'c', 'x', 'y'],
                       data=[[0, 0, 1, 2], [1, 0, 3, 4],
                             [0, 1, 1, 2], [1, 1, 3, 4]])
        dg = df.pivot_table(index='i', columns='c',
                            values=['x', 'y'])

        with assertRaisesRegexp(TypeError, "is an invalid key"):
            str(dg[:, 0])

        index = Index(range(2), name='i')
        columns = MultiIndex(levels=[['x', 'y'], [0, 1]],
                             labels=[[0, 1], [0, 0]],
                             names=[None, 'c'])
        expected = DataFrame([[1, 2], [3, 4]], columns=columns, index=index)

        result = dg.loc[:, (slice(None), 0)]
        assert_frame_equal(result, expected)

        name = ('x', 0)
        index = Index(range(2), name='i')
        expected = Series([1, 3], index=index, name=name)

        result = dg['x', 0]
        assert_series_equal(result, expected)


class TestDataFrameIndexingDatetimeWithTZ(tm.TestCase, TestData):

    _multiprocess_can_split_ = True

    def setUp(self):
        self.idx = Index(date_range('20130101', periods=3, tz='US/Eastern'),
                         name='foo')
        self.dr = date_range('20130110', periods=3)
        self.df = DataFrame({'A': self.idx, 'B': self.dr})

    def test_setitem(self):

        df = self.df
        idx = self.idx

        # setitem
        df['C'] = idx
        assert_series_equal(df['C'], Series(idx, name='C'))

        df['D'] = 'foo'
        df['D'] = idx
        assert_series_equal(df['D'], Series(idx, name='D'))
        del df['D']

        # assert that A & C are not sharing the same base (e.g. they
        # are copies)
        b1 = df._data.blocks[1]
        b2 = df._data.blocks[2]
        self.assertTrue(b1.values.equals(b2.values))
        self.assertFalse(id(b1.values.values.base) ==
                         id(b2.values.values.base))

        # with nan
        df2 = df.copy()
        df2.iloc[1, 1] = pd.NaT
        df2.iloc[1, 2] = pd.NaT
        result = df2['B']
        assert_series_equal(notnull(result), Series(
            [True, False, True], name='B'))
        assert_series_equal(df2.dtypes, df.dtypes)

    def test_set_reset(self):

        idx = self.idx

        # set/reset
        df = DataFrame({'A': [0, 1, 2]}, index=idx)
        result = df.reset_index()
        self.assertTrue(result['foo'].dtype, 'M8[ns, US/Eastern')

        result = result.set_index('foo')
        tm.assert_index_equal(df.index, idx)

    def test_transpose(self):

        result = self.df.T
        expected = DataFrame(self.df.values.T)
        expected.index = ['A', 'B']
        assert_frame_equal(result, expected)


if __name__ == '__main__':
    import nose
    nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                   exit=False)