test_iloc.py

""" test positional based indexing with iloc """

from warnings import catch_warnings, filterwarnings, simplefilter

import numpy as np
import pytest

from pandas.compat import lmap, lrange

import pandas as pd
from pandas import DataFrame, Series, concat, date_range, isna
from pandas.api.types import is_scalar
from pandas.tests.indexing.common import Base
from pandas.util import testing as tm


class TestiLoc(Base):

    def test_iloc_exceeds_bounds(self):

        # GH6296
        # iloc should allow indexers that exceed the bounds
        df = DataFrame(np.random.random_sample((20, 5)), columns=list('ABCDE'))

        # lists of positions should raise IndexErrror!
        msg = 'positional indexers are out-of-bounds'
        with pytest.raises(IndexError, match=msg):
            df.iloc[:, [0, 1, 2, 3, 4, 5]]
        pytest.raises(IndexError, lambda: df.iloc[[1, 30]])
        pytest.raises(IndexError, lambda: df.iloc[[1, -30]])
        pytest.raises(IndexError, lambda: df.iloc[[100]])

        s = df['A']
        pytest.raises(IndexError, lambda: s.iloc[[100]])
        pytest.raises(IndexError, lambda: s.iloc[[-100]])

        # still raise on a single indexer
        msg = 'single positional indexer is out-of-bounds'
        with pytest.raises(IndexError, match=msg):
            df.iloc[30]
        pytest.raises(IndexError, lambda: df.iloc[-30])

        # GH10779
        # single positive/negative indexer exceeding Series bounds should raise
        # an IndexError
        with pytest.raises(IndexError, match=msg):
            s.iloc[30]
        pytest.raises(IndexError, lambda: s.iloc[-30])

        # slices are ok
        result = df.iloc[:, 4:10]  # 0 < start < len < stop
        expected = df.iloc[:, 4:]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, -4:-10]  # stop < 0 < start < len
        expected = df.iloc[:, :0]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, 10:4:-1]  # 0 < stop < len < start (down)
        expected = df.iloc[:, :4:-1]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, 4:-10:-1]  # stop < 0 < start < len (down)
        expected = df.iloc[:, 4::-1]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, -10:4]  # start < 0 < stop < len
        expected = df.iloc[:, :4]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, 10:4]  # 0 < stop < len < start
        expected = df.iloc[:, :0]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, -10:-11:-1]  # stop < start < 0 < len (down)
        expected = df.iloc[:, :0]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, 10:11]  # 0 < len < start < stop
        expected = df.iloc[:, :0]
        tm.assert_frame_equal(result, expected)

        # slice bounds exceeding is ok
        result = s.iloc[18:30]
        expected = s.iloc[18:]
        tm.assert_series_equal(result, expected)

        result = s.iloc[30:]
        expected = s.iloc[:0]
        tm.assert_series_equal(result, expected)

        result = s.iloc[30::-1]
        expected = s.iloc[::-1]
        tm.assert_series_equal(result, expected)

        # doc example
        def check(result, expected):
            str(result)
            result.dtypes
            tm.assert_frame_equal(result, expected)

        dfl = DataFrame(np.random.randn(5, 2), columns=list('AB'))
        check(dfl.iloc[:, 2:3], DataFrame(index=dfl.index))
        check(dfl.iloc[:, 1:3], dfl.iloc[:, [1]])
        check(dfl.iloc[4:6], dfl.iloc[[4]])

        pytest.raises(IndexError, lambda: dfl.iloc[[4, 5, 6]])
        pytest.raises(IndexError, lambda: dfl.iloc[:, 4])

    def test_iloc_getitem_int(self):

        # integer
        self.check_result('integer', 'iloc', 2, 'ix',
                          {0: 4, 1: 6, 2: 8}, typs=['ints', 'uints'])
        self.check_result('integer', 'iloc', 2, 'indexer', 2,
                          typs=['labels', 'mixed', 'ts', 'floats', 'empty'],
                          fails=IndexError)

    def test_iloc_getitem_neg_int(self):

        # neg integer
        self.check_result('neg int', 'iloc', -1, 'ix',
                          {0: 6, 1: 9, 2: 12}, typs=['ints', 'uints'])
        self.check_result('neg int', 'iloc', -1, 'indexer', -1,
                          typs=['labels', 'mixed', 'ts', 'floats', 'empty'],
                          fails=IndexError)

    @pytest.mark.parametrize('dims', [1, 2])
    def test_iloc_getitem_invalid_scalar(self, dims):
        # GH 21982

        if dims == 1:
            s = Series(np.arange(10))
        else:
            s = DataFrame(np.arange(100).reshape(10, 10))

        with pytest.raises(TypeError, match='Cannot index by location index'):
            s.iloc['a']

    def test_iloc_array_not_mutating_negative_indices(self):

        # GH 21867
        array_with_neg_numbers = np.array([1, 2, -1])
        array_copy = array_with_neg_numbers.copy()
        df = pd.DataFrame({
            'A': [100, 101, 102],
            'B': [103, 104, 105],
            'C': [106, 107, 108]},
            index=[1, 2, 3])
        df.iloc[array_with_neg_numbers]
        tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy)
        df.iloc[:, array_with_neg_numbers]
        tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy)

    def test_iloc_getitem_list_int(self):

        # list of ints
        self.check_result('list int', 'iloc', [0, 1, 2], 'ix',
                          {0: [0, 2, 4], 1: [0, 3, 6], 2: [0, 4, 8]},
                          typs=['ints', 'uints'])
        self.check_result('list int', 'iloc', [2], 'ix',
                          {0: [4], 1: [6], 2: [8]}, typs=['ints', 'uints'])
        self.check_result('list int', 'iloc', [0, 1, 2], 'indexer', [0, 1, 2],
                          typs=['labels', 'mixed', 'ts', 'floats', 'empty'],
                          fails=IndexError)

        # array of ints (GH5006), make sure that a single indexer is returning
        # the correct type
        self.check_result('array int', 'iloc', np.array([0, 1, 2]), 'ix',
                          {0: [0, 2, 4],
                           1: [0, 3, 6],
                           2: [0, 4, 8]}, typs=['ints', 'uints'])
        self.check_result('array int', 'iloc', np.array([2]), 'ix',
                          {0: [4], 1: [6], 2: [8]}, typs=['ints', 'uints'])
        self.check_result('array int', 'iloc', np.array([0, 1, 2]), 'indexer',
                          [0, 1, 2],
                          typs=['labels', 'mixed', 'ts', 'floats', 'empty'],
                          fails=IndexError)

    def test_iloc_getitem_neg_int_can_reach_first_index(self):
        # GH10547 and GH10779
        # negative integers should be able to reach index 0
        df = DataFrame({'A': [2, 3, 5], 'B': [7, 11, 13]})
        s = df['A']

        expected = df.iloc[0]
        result = df.iloc[-3]
        tm.assert_series_equal(result, expected)

        expected = df.iloc[[0]]
        result = df.iloc[[-3]]
        tm.assert_frame_equal(result, expected)

        expected = s.iloc[0]
        result = s.iloc[-3]
        assert result == expected

        expected = s.iloc[[0]]
        result = s.iloc[[-3]]
        tm.assert_series_equal(result, expected)

        # check the length 1 Series case highlighted in GH10547
        expected = Series(['a'], index=['A'])
        result = expected.iloc[[-1]]
        tm.assert_series_equal(result, expected)

    def test_iloc_getitem_dups(self):

        # no dups in panel (bug?)
        self.check_result('list int (dups)', 'iloc', [0, 1, 1, 3], 'ix',
                          {0: [0, 2, 2, 6], 1: [0, 3, 3, 9]},
                          objs=['series', 'frame'], typs=['ints', 'uints'])

        # GH 6766
        df1 = DataFrame([{'A': None, 'B': 1}, {'A': 2, 'B': 2}])
        df2 = DataFrame([{'A': 3, 'B': 3}, {'A': 4, 'B': 4}])
        df = concat([df1, df2], axis=1)

        # cross-sectional indexing
        result = df.iloc[0, 0]
        assert isna(result)

        result = df.iloc[0, :]
        expected = Series([np.nan, 1, 3, 3], index=['A', 'B', 'A', 'B'],
                          name=0)
        tm.assert_series_equal(result, expected)

    def test_iloc_getitem_array(self):

        # array like
        s = Series(index=lrange(1, 4))
        self.check_result('array like', 'iloc', s.index, 'ix',
                          {0: [2, 4, 6], 1: [3, 6, 9], 2: [4, 8, 12]},
                          typs=['ints', 'uints'])

    def test_iloc_getitem_bool(self):

        # boolean indexers
        b = [True, False, True, False, ]
        self.check_result('bool', 'iloc', b, 'ix', b, typs=['ints', 'uints'])
        self.check_result('bool', 'iloc', b, 'ix', b,
                          typs=['labels', 'mixed', 'ts', 'floats', 'empty'],
                          fails=IndexError)

    def test_iloc_getitem_slice(self):

        # slices
        self.check_result('slice', 'iloc', slice(1, 3), 'ix',
                          {0: [2, 4], 1: [3, 6], 2: [4, 8]},
                          typs=['ints', 'uints'])
        self.check_result('slice', 'iloc', slice(1, 3), 'indexer',
                          slice(1, 3),
                          typs=['labels', 'mixed', 'ts', 'floats', 'empty'],
                          fails=IndexError)

    def test_iloc_getitem_slice_dups(self):

        df1 = DataFrame(np.random.randn(10, 4), columns=['A', 'A', 'B', 'B'])
        df2 = DataFrame(np.random.randint(0, 10, size=20).reshape(10, 2),
                        columns=['A', 'C'])

        # axis=1
        df = concat([df1, df2], axis=1)
        tm.assert_frame_equal(df.iloc[:, :4], df1)
        tm.assert_frame_equal(df.iloc[:, 4:], df2)

        df = concat([df2, df1], axis=1)
        tm.assert_frame_equal(df.iloc[:, :2], df2)
        tm.assert_frame_equal(df.iloc[:, 2:], df1)

        exp = concat([df2, df1.iloc[:, [0]]], axis=1)
        tm.assert_frame_equal(df.iloc[:, 0:3], exp)

        # axis=0
        df = concat([df, df], axis=0)
        tm.assert_frame_equal(df.iloc[0:10, :2], df2)
        tm.assert_frame_equal(df.iloc[0:10, 2:], df1)
        tm.assert_frame_equal(df.iloc[10:, :2], df2)
        tm.assert_frame_equal(df.iloc[10:, 2:], df1)

    def test_iloc_setitem(self):
        df = self.frame_ints

        df.iloc[1, 1] = 1
        result = df.iloc[1, 1]
        assert result == 1

        df.iloc[:, 2:3] = 0
        expected = df.iloc[:, 2:3]
        result = df.iloc[:, 2:3]
        tm.assert_frame_equal(result, expected)

        # GH5771
        s = Series(0, index=[4, 5, 6])
        s.iloc[1:2] += 1
        expected = Series([0, 1, 0], index=[4, 5, 6])
        tm.assert_series_equal(s, expected)

    def test_iloc_setitem_list(self):

        # setitem with an iloc list
        df = DataFrame(np.arange(9).reshape((3, 3)), index=["A", "B", "C"],
                       columns=["A", "B", "C"])
        df.iloc[[0, 1], [1, 2]]
        df.iloc[[0, 1], [1, 2]] += 100

        expected = DataFrame(
            np.array([0, 101, 102, 3, 104, 105, 6, 7, 8]).reshape((3, 3)),
            index=["A", "B", "C"], columns=["A", "B", "C"])
        tm.assert_frame_equal(df, expected)

    def test_iloc_setitem_pandas_object(self):
        # GH 17193
        s_orig = Series([0, 1, 2, 3])
        expected = Series([0, -1, -2, 3])

        s = s_orig.copy()
        s.iloc[Series([1, 2])] = [-1, -2]
        tm.assert_series_equal(s, expected)

        s = s_orig.copy()
        s.iloc[pd.Index([1, 2])] = [-1, -2]
        tm.assert_series_equal(s, expected)

    def test_iloc_setitem_dups(self):

        # GH 6766
        # iloc with a mask aligning from another iloc
        df1 = DataFrame([{'A': None, 'B': 1}, {'A': 2, 'B': 2}])
        df2 = DataFrame([{'A': 3, 'B': 3}, {'A': 4, 'B': 4}])
        df = concat([df1, df2], axis=1)

        expected = df.fillna(3)
        expected['A'] = expected['A'].astype('float64')
        inds = np.isnan(df.iloc[:, 0])
        mask = inds[inds].index
        df.iloc[mask, 0] = df.iloc[mask, 2]
        tm.assert_frame_equal(df, expected)

        # del a dup column across blocks
        expected = DataFrame({0: [1, 2], 1: [3, 4]})
        expected.columns = ['B', 'B']
        del df['A']
        tm.assert_frame_equal(df, expected)

        # assign back to self
        df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]]
        tm.assert_frame_equal(df, expected)

        # reversed x 2
        df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(
            drop=True)
        df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(
            drop=True)
        tm.assert_frame_equal(df, expected)

    def test_iloc_getitem_frame(self):
        df = DataFrame(np.random.randn(10, 4), index=lrange(0, 20, 2),
                       columns=lrange(0, 8, 2))

        result = df.iloc[2]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            exp = df.ix[4]
        tm.assert_series_equal(result, exp)

        result = df.iloc[2, 2]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            exp = df.ix[4, 4]
        assert result == exp

        # slice
        result = df.iloc[4:8]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            expected = df.ix[8:14]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:, 2:3]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            expected = df.ix[:, 4:5]
        tm.assert_frame_equal(result, expected)

        # list of integers
        result = df.iloc[[0, 1, 3]]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            expected = df.ix[[0, 2, 6]]
        tm.assert_frame_equal(result, expected)

        result = df.iloc[[0, 1, 3], [0, 1]]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            expected = df.ix[[0, 2, 6], [0, 2]]
        tm.assert_frame_equal(result, expected)

        # neg indices
        result = df.iloc[[-1, 1, 3], [-1, 1]]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            expected = df.ix[[18, 2, 6], [6, 2]]
        tm.assert_frame_equal(result, expected)

        # dups indices
        result = df.iloc[[-1, -1, 1, 3], [-1, 1]]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            expected = df.ix[[18, 18, 2, 6], [6, 2]]
        tm.assert_frame_equal(result, expected)

        # with index-like
        s = Series(index=lrange(1, 5))
        result = df.iloc[s.index]
        with catch_warnings(record=True):
            filterwarnings("ignore", "\\n.ix", DeprecationWarning)
            expected = df.ix[[2, 4, 6, 8]]
        tm.assert_frame_equal(result, expected)

    def test_iloc_getitem_labelled_frame(self):
        # try with labelled frame
        df = DataFrame(np.random.randn(10, 4),
                       index=list('abcdefghij'), columns=list('ABCD'))

        result = df.iloc[1, 1]
        exp = df.loc['b', 'B']
        assert result == exp

        result = df.iloc[:, 2:3]
        expected = df.loc[:, ['C']]
        tm.assert_frame_equal(result, expected)

        # negative indexing
        result = df.iloc[-1, -1]
        exp = df.loc['j', 'D']
        assert result == exp

        # out-of-bounds exception
        pytest.raises(IndexError, df.iloc.__getitem__, tuple([10, 5]))

        # trying to use a label
        pytest.raises(ValueError, df.iloc.__getitem__, tuple(['j', 'D']))

    def test_iloc_getitem_doc_issue(self):

        # multi axis slicing issue with single block
        # surfaced in GH 6059

        arr = np.random.randn(6, 4)
        index = date_range('20130101', periods=6)
        columns = list('ABCD')
        df = DataFrame(arr, index=index, columns=columns)

        # defines ref_locs
        df.describe()

        result = df.iloc[3:5, 0:2]
        str(result)
        result.dtypes

        expected = DataFrame(arr[3:5, 0:2], index=index[3:5],
                             columns=columns[0:2])
        tm.assert_frame_equal(result, expected)

        # for dups
        df.columns = list('aaaa')
        result = df.iloc[3:5, 0:2]
        str(result)
        result.dtypes

        expected = DataFrame(arr[3:5, 0:2], index=index[3:5],
                             columns=list('aa'))
        tm.assert_frame_equal(result, expected)

        # related
        arr = np.random.randn(6, 4)
        index = list(range(0, 12, 2))
        columns = list(range(0, 8, 2))
        df = DataFrame(arr, index=index, columns=columns)

        df._data.blocks[0].mgr_locs
        result = df.iloc[1:5, 2:4]
        str(result)
        result.dtypes
        expected = DataFrame(arr[1:5, 2:4], index=index[1:5],
                             columns=columns[2:4])
        tm.assert_frame_equal(result, expected)

    def test_iloc_setitem_series(self):
        df = DataFrame(np.random.randn(10, 4), index=list('abcdefghij'),
                       columns=list('ABCD'))

        df.iloc[1, 1] = 1
        result = df.iloc[1, 1]
        assert result == 1

        df.iloc[:, 2:3] = 0
        expected = df.iloc[:, 2:3]
        result = df.iloc[:, 2:3]
        tm.assert_frame_equal(result, expected)

        s = Series(np.random.randn(10), index=lrange(0, 20, 2))

        s.iloc[1] = 1
        result = s.iloc[1]
        assert result == 1

        s.iloc[:4] = 0
        expected = s.iloc[:4]
        result = s.iloc[:4]
        tm.assert_series_equal(result, expected)

        s = Series([-1] * 6)
        s.iloc[0::2] = [0, 2, 4]
        s.iloc[1::2] = [1, 3, 5]
        result = s
        expected = Series([0, 1, 2, 3, 4, 5])
        tm.assert_series_equal(result, expected)

    def test_iloc_setitem_list_of_lists(self):

        # GH 7551
        # list-of-list is set incorrectly in mixed vs. single dtyped frames
        df = DataFrame(dict(A=np.arange(5, dtype='int64'),
                            B=np.arange(5, 10, dtype='int64')))
        df.iloc[2:4] = [[10, 11], [12, 13]]
        expected = DataFrame(dict(A=[0, 1, 10, 12, 4], B=[5, 6, 11, 13, 9]))
        tm.assert_frame_equal(df, expected)

        df = DataFrame(
            dict(A=list('abcde'), B=np.arange(5, 10, dtype='int64')))
        df.iloc[2:4] = [['x', 11], ['y', 13]]
        expected = DataFrame(dict(A=['a', 'b', 'x', 'y', 'e'],
                                  B=[5, 6, 11, 13, 9]))
        tm.assert_frame_equal(df, expected)

    @pytest.mark.parametrize(
        'indexer', [[0], slice(None, 1, None), np.array([0])])
    @pytest.mark.parametrize(
        'value', [['Z'], np.array(['Z'])])
    def test_iloc_setitem_with_scalar_index(self, indexer, value):
        # GH #19474
        # assigning like "df.iloc[0, [0]] = ['Z']" should be evaluated
        # elementwisely, not using "setter('A', ['Z'])".

        df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        df.iloc[0, indexer] = value
        result = df.iloc[0, 0]

        assert is_scalar(result) and result == 'Z'

    def test_iloc_mask(self):

        # GH 3631, iloc with a mask (of a series) should raise
        df = DataFrame(lrange(5), list('ABCDE'), columns=['a'])
        mask = (df.a % 2 == 0)
        pytest.raises(ValueError, df.iloc.__getitem__, tuple([mask]))
        mask.index = lrange(len(mask))
        pytest.raises(NotImplementedError, df.iloc.__getitem__,
                      tuple([mask]))

        # ndarray ok
        result = df.iloc[np.array([True] * len(mask), dtype=bool)]
        tm.assert_frame_equal(result, df)

        # the possibilities
        locs = np.arange(4)
        nums = 2 ** locs
        reps = lmap(bin, nums)
        df = DataFrame({'locs': locs, 'nums': nums}, reps)

        expected = {
            (None, ''): '0b1100',
            (None, '.loc'): '0b1100',
            (None, '.iloc'): '0b1100',
            ('index', ''): '0b11',
            ('index', '.loc'): '0b11',
            ('index', '.iloc'): ('iLocation based boolean indexing '
                                 'cannot use an indexable as a mask'),
            ('locs', ''): 'Unalignable boolean Series provided as indexer '
                          '(index of the boolean Series and of the indexed '
                          'object do not match',
            ('locs', '.loc'): 'Unalignable boolean Series provided as indexer '
                              '(index of the boolean Series and of the '
                              'indexed object do not match',
            ('locs', '.iloc'): ('iLocation based boolean indexing on an '
                                'integer type is not available'),
        }

        # UserWarnings from reindex of a boolean mask
        with catch_warnings(record=True):
            simplefilter("ignore", UserWarning)
            result = dict()
            for idx in [None, 'index', 'locs']:
                mask = (df.nums > 2).values
                if idx:
                    mask = Series(mask, list(reversed(getattr(df, idx))))
                for method in ['', '.loc', '.iloc']:
                    try:
                        if method:
                            accessor = getattr(df, method[1:])
                        else:
                            accessor = df
                        ans = str(bin(accessor[mask]['nums'].sum()))
                    except Exception as e:
                        ans = str(e)

                    key = tuple([idx, method])
                    r = expected.get(key)
                    if r != ans:
                        raise AssertionError(
                            "[%s] does not match [%s], received [%s]"
                            % (key, ans, r))

    def test_iloc_non_unique_indexing(self):

        # GH 4017, non-unique indexing (on the axis)
        df = DataFrame({'A': [0.1] * 3000, 'B': [1] * 3000})
        idx = np.array(lrange(30)) * 99
        expected = df.iloc[idx]

        df3 = concat([df, 2 * df, 3 * df])
        result = df3.iloc[idx]

        tm.assert_frame_equal(result, expected)

        df2 = DataFrame({'A': [0.1] * 1000, 'B': [1] * 1000})
        df2 = concat([df2, 2 * df2, 3 * df2])

        sidx = df2.index.to_series()
        expected = df2.iloc[idx[idx <= sidx.max()]]

        new_list = []
        for r, s in expected.iterrows():
            new_list.append(s)
            new_list.append(s * 2)
            new_list.append(s * 3)

        expected = DataFrame(new_list)
        expected = concat([expected, DataFrame(index=idx[idx > sidx.max()])],
                          sort=True)
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            result = df2.loc[idx]
        tm.assert_frame_equal(result, expected, check_index_type=False)

    def test_iloc_empty_list_indexer_is_ok(self):
        from pandas.util.testing import makeCustomDataframe as mkdf
        df = mkdf(5, 2)
        # vertical empty
        tm.assert_frame_equal(df.iloc[:, []], df.iloc[:, :0],
                              check_index_type=True, check_column_type=True)
        # horizontal empty
        tm.assert_frame_equal(df.iloc[[], :], df.iloc[:0, :],
                              check_index_type=True, check_column_type=True)
        # horizontal empty
        tm.assert_frame_equal(df.iloc[[]], df.iloc[:0, :],
                              check_index_type=True,
                              check_column_type=True)

    def test_identity_slice_returns_new_object(self):
        # GH13873
        original_df = DataFrame({'a': [1, 2, 3]})
        sliced_df = original_df.iloc[:]
        assert sliced_df is not original_df

        # should be a shallow copy
        original_df['a'] = [4, 4, 4]
        assert (sliced_df['a'] == 4).all()

        original_series = Series([1, 2, 3, 4, 5, 6])
        sliced_series = original_series.iloc[:]
        assert sliced_series is not original_series

        # should also be a shallow copy
        original_series[:3] = [7, 8, 9]
        assert all(sliced_series[:3] == [7, 8, 9])

    def test_indexing_zero_dim_np_array(self):
        # GH24919
        df = DataFrame([[1, 2], [3, 4]])

        # should not raise an error
        df.iloc[np.array(0)]