Python pandas.core.indexing.IndexingError() Examples

def test_getitem_setitem_boolean_corner(test_data):
    ts = test_data.ts
    mask_shifted = ts.shift(1, freq=BDay()) > ts.median()

    # these used to raise...??

    msg = (r"Unalignable boolean Series provided as indexer \(index of"
           r" the boolean Series and of the indexed object do not match")
    with pytest.raises(IndexingError, match=msg):
        ts[mask_shifted]
    with pytest.raises(IndexingError, match=msg):
        ts[mask_shifted] = 1

    with pytest.raises(IndexingError, match=msg):
        ts.loc[mask_shifted]
    with pytest.raises(IndexingError, match=msg):
        ts.loc[mask_shifted] = 1 

def _parse_tuple(tup):
    """Unpack the user input for getitem and setitem and compute ndim

    loc[a] -> ([a], :), 1D
    loc[[a,b],] -> ([a,b], :),
    loc[a,b] -> ([a], [b]), 0D
    """
    row_loc, col_loc = slice(None), slice(None)

    if is_tuple(tup):
        row_loc = tup[0]
        if len(tup) == 2:
            col_loc = tup[1]
        if len(tup) > 2:
            raise IndexingError("Too many indexers")
    else:
        row_loc = tup

    ndim = _compute_ndim(row_loc, col_loc)
    row_scaler = is_scalar(row_loc)
    col_scaler = is_scalar(col_loc)
    row_loc = [row_loc] if row_scaler else row_loc
    col_loc = [col_loc] if col_scaler else col_loc

    return row_loc, col_loc, ndim, row_scaler, col_scaler 

def test_df_iloc_value_error(self):
        def int_impl(df):
            return df.iloc[11]

        def list_impl(df):
            return df.iloc[[7, 14]]

        def list_bool_impl(df):
            return df.iloc[[True, False]]

        msg1 = 'Index is out of bounds for axis'
        msg2 = 'Item wrong length'
        df = pd.DataFrame({"A": [3.2, 4.4, 7.0, 3.3, 1.0],
                           "B": [5.5, np.nan, 3, 0, 7.7],
                           "C": [3, 4, 1, 0, 222]})

        impls = [(int_impl, msg1), (list_impl, msg1), (list_bool_impl, msg2)]
        for impl, msg in impls:
            with self.subTest(case=impl, msg=msg):
                func = self.jit(impl)
                with self.assertRaises(IndexingError) as raises:
                    func(df)
                self.assertIn(msg, str(raises.exception)) 

def test_getitem_setitem_boolean_corner(test_data):
    ts = test_data.ts
    mask_shifted = ts.shift(1, freq=BDay()) > ts.median()

    # these used to raise...??

    msg = (r"Unalignable boolean Series provided as indexer \(index of"
           r" the boolean Series and of the indexed object do not match")
    with pytest.raises(IndexingError, match=msg):
        ts[mask_shifted]
    with pytest.raises(IndexingError, match=msg):
        ts[mask_shifted] = 1

    with pytest.raises(IndexingError, match=msg):
        ts.loc[mask_shifted]
    with pytest.raises(IndexingError, match=msg):
        ts.loc[mask_shifted] = 1 

def test_getitem_boolean_empty():
    s = Series([], dtype=np.int64)
    s.index.name = 'index_name'
    s = s[s.isna()]
    assert s.index.name == 'index_name'
    assert s.dtype == np.int64

    # GH5877
    # indexing with empty series
    s = Series(['A', 'B'])
    expected = Series(np.nan, index=['C'], dtype=object)
    result = s[Series(['C'], dtype=object)]
    assert_series_equal(result, expected)

    s = Series(['A', 'B'])
    expected = Series(dtype=object, index=Index([], dtype='int64'))
    result = s[Series([], dtype=object)]
    assert_series_equal(result, expected)

    # invalid because of the boolean indexer
    # that's empty or not-aligned
    msg = (r"Unalignable boolean Series provided as indexer \(index of"
           r" the boolean Series and of the indexed object do not match")
    with pytest.raises(IndexingError, match=msg):
        s[Series([], dtype=bool)]

    with pytest.raises(IndexingError, match=msg):
        s[Series([True], dtype=bool)] 

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

        with pytest.raises(IndexingError):
            ix[:, :, :] = 1 

def test_getitem_boolean_empty():
    s = Series([], dtype=np.int64)
    s.index.name = 'index_name'
    s = s[s.isna()]
    assert s.index.name == 'index_name'
    assert s.dtype == np.int64

    # GH5877
    # indexing with empty series
    s = Series(['A', 'B'])
    expected = Series(np.nan, index=['C'], dtype=object)
    result = s[Series(['C'], dtype=object)]
    assert_series_equal(result, expected)

    s = Series(['A', 'B'])
    expected = Series(dtype=object, index=Index([], dtype='int64'))
    result = s[Series([], dtype=object)]
    assert_series_equal(result, expected)

    # invalid because of the boolean indexer
    # that's empty or not-aligned
    def f():
        s[Series([], dtype=bool)]

    pytest.raises(IndexingError, f)

    def f():
        s[Series([True], dtype=bool)]

    pytest.raises(IndexingError, f) 

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

        with pytest.raises(IndexingError):
            ix[:, :, :] = 1 

def test_getitem_boolean_empty(self):
        s = Series([], dtype=np.int64)
        s.index.name = 'index_name'
        s = s[s.isna()]
        assert s.index.name == 'index_name'
        assert s.dtype == np.int64

        # GH5877
        # indexing with empty series
        s = Series(['A', 'B'])
        expected = Series(np.nan, index=['C'], dtype=object)
        result = s[Series(['C'], dtype=object)]
        assert_series_equal(result, expected)

        s = Series(['A', 'B'])
        expected = Series(dtype=object, index=Index([], dtype='int64'))
        result = s[Series([], dtype=object)]
        assert_series_equal(result, expected)

        # invalid because of the boolean indexer
        # that's empty or not-aligned
        def f():
            s[Series([], dtype=bool)]

        pytest.raises(IndexingError, f)

        def f():
            s[Series([True], dtype=bool)]

        pytest.raises(IndexingError, f) 

def df_getitem_list_iloc_codegen(self, idx):
    """
    Example of generated implementation:
        def _df_getitem_list_iloc_impl(self, idx):
            check_idx = False
            for i in idx:
                if -1 < i < len(self._dataframe.index):
                    check_idx = True
            if check_idx == True:
                data_0 = pandas.Series(self._dataframe._data[0][0])
                result_0 = data_0.iloc[numpy.array(idx)]
                data_1 = pandas.Series(self._dataframe._data[1][0])
                result_1 = data_1.iloc[numpy.array(idx)]
                return pandas.DataFrame(data={"A": result_0, "B": result_1}, index=idx)
            raise IndexingError('Index is out of bounds for axis')
    """
    func_lines = ['def _df_getitem_list_iloc_impl(self, idx):',
                  '  check_idx = False',
                  '  for i in idx:',
                  '    if -1 < i < len(self._dataframe.index):',
                  '      check_idx = True',
                  '  if check_idx == True:']
    results = []
    index = '[self._dataframe._index[i] for i in idx]'
    if isinstance(self.index, types.NoneType):
        index = 'idx'
    for i, c in enumerate(self.columns):
        col_loc = self.column_loc[c]
        type_id, col_id = col_loc.type_id, col_loc.col_id
        result_c = f"result_{i}"
        func_lines += [f"    data_{i} = pandas.Series(self._dataframe._data[{type_id}][{col_id}])",
                       f"    {result_c} = data_{i}.iloc[numpy.array(idx)]"]
        results.append((c, result_c))
    data = ', '.join(f'"{col}": {data}' for col, data in results)
    func_lines += [f"    return pandas.DataFrame(data={{{data}}}, index={index})",
                   f"  raise IndexingError('Index is out of bounds for axis')"]

    func_text = '\n'.join(func_lines)
    global_vars = {'pandas': pandas, 'numpy': numpy, 'IndexingError': IndexingError}

    return func_text, global_vars 

def df_getitem_int_iloc_codegen(self, idx):
    """
    Example of generated implementation:
        def _df_getitem_int_iloc_impl(self, idx):
            if -1 < idx < len(self._dataframe.index):
                data_0 = pandas.Series(self._dataframe._data[0][0])
                result_0 = data_0.iat[idx]
                data_1 = pandas.Series(self._dataframe._data[0][1])
                result_1 = data_1.iat[idx]
                return pandas.Series(data=[result_0, result_1], index=['A', 'B'], name=str(idx))
            raise IndexingError('Index is out of bounds for axis')
    """
    func_lines = ['def _df_getitem_int_iloc_impl(self, idx):',
                  '  if -1 < idx < len(self._dataframe.index):']
    results = []
    index = []
    name = 'self._dataframe._index[idx]'
    if isinstance(self.index, types.NoneType):
        name = 'idx'
    for i, c in enumerate(self.columns):
        col_loc = self.column_loc[c]
        type_id, col_id = col_loc.type_id, col_loc.col_id
        result_c = f"result_{i}"
        func_lines += [f"    data_{i} = pandas.Series(self._dataframe._data[{type_id}][{col_id}])",
                       f"    {result_c} = data_{i}.iat[idx]"]
        results.append(result_c)
        index.append(c)
    data = ', '.join(col for col in results)
    func_lines += [f"    return pandas.Series(data=[{data}], index={index}, name=str({name}))",
                   f"  raise IndexingError('Index is out of bounds for axis')"]

    func_text = '\n'.join(func_lines)
    global_vars = {'pandas': pandas, 'numpy': numpy, 'IndexingError': IndexingError}

    return func_text, global_vars 

def df_getitem_bool_series_idx_codegen(self, idx):
    """
    Example of generated implementation with provided index:
        def _df_getitem_bool_series_idx_impl(self, idx):
          length = len(self._data[0][0])
          self_index = range(len(self._data[0][0]))
          if length > len(idx):
            msg = "Unalignable boolean Series provided as indexer " + \
                  "(index of the boolean Series and of the indexed object do not match)."
            raise IndexingError(msg)
          # do not trim idx._data to length as getitem_by_mask handles such case
          res_index = getitem_by_mask(self_index, idx._data)
          # df index is default, same as positions so it can be used in take
          data_0 = self._data[0][0]
          res_data_0 = sdc_take(data_0, res_index)
          data_1 = self._data[1][0]
          res_data_1 = sdc_take(data_1, res_index)
          return pandas.DataFrame({"A": res_data_0, "B": res_data_1}, index=res_index)
    """
    func_lines = ['def _df_getitem_bool_series_idx_impl(self, idx):']
    func_lines += df_getitem_bool_series_idx_main_codelines(self, idx)
    func_text = '\n'.join(func_lines)
    global_vars = {'pandas': pandas, 'numpy': numpy,
                   'getitem_by_mask': getitem_by_mask,
                   'sdc_take': _sdc_take,
                   'sdc_reindex_series': sdc_reindex_series,
                   'IndexingError': IndexingError}

    return func_text, global_vars 

def test_getitem_setitem_fancy_exceptions(self):
        ix = self.frame.iloc
        with pytest.raises(IndexingError, match='Too many indexers'):
            ix[:, :, :]

        with pytest.raises(IndexingError):
            ix[:, :, :] = 1 

def test_getitem_boolean_empty():
    s = Series([], dtype=np.int64)
    s.index.name = 'index_name'
    s = s[s.isna()]
    assert s.index.name == 'index_name'
    assert s.dtype == np.int64

    # GH5877
    # indexing with empty series
    s = Series(['A', 'B'])
    expected = Series(np.nan, index=['C'], dtype=object)
    result = s[Series(['C'], dtype=object)]
    assert_series_equal(result, expected)

    s = Series(['A', 'B'])
    expected = Series(dtype=object, index=Index([], dtype='int64'))
    result = s[Series([], dtype=object)]
    assert_series_equal(result, expected)

    # invalid because of the boolean indexer
    # that's empty or not-aligned
    msg = (r"Unalignable boolean Series provided as indexer \(index of"
           r" the boolean Series and of the indexed object do not match")
    with pytest.raises(IndexingError, match=msg):
        s[Series([], dtype=bool)]

    with pytest.raises(IndexingError, match=msg):
        s[Series([True], dtype=bool)] 

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

        with pytest.raises(IndexingError):
            ix[:, :, :] = 1 

def test_getitem_boolean_empty():
    s = Series([], dtype=np.int64)
    s.index.name = 'index_name'
    s = s[s.isna()]
    assert s.index.name == 'index_name'
    assert s.dtype == np.int64

    # GH5877
    # indexing with empty series
    s = Series(['A', 'B'])
    expected = Series(np.nan, index=['C'], dtype=object)
    result = s[Series(['C'], dtype=object)]
    assert_series_equal(result, expected)

    s = Series(['A', 'B'])
    expected = Series(dtype=object, index=Index([], dtype='int64'))
    result = s[Series([], dtype=object)]
    assert_series_equal(result, expected)

    # invalid because of the boolean indexer
    # that's empty or not-aligned
    def f():
        s[Series([], dtype=bool)]

    pytest.raises(IndexingError, f)

    def f():
        s[Series([True], dtype=bool)]

    pytest.raises(IndexingError, f) 

def process_loc_indexes(inp, indexes):
    ndim = inp.ndim

    if not isinstance(indexes, tuple):
        indexes = (indexes,)
    if len(indexes) < ndim:
        indexes += (slice(None),) * (ndim - len(indexes))
    if len(indexes) > ndim:
        raise IndexingError('Too many indexers')

    new_indexes = []
    for ax, index in enumerate(indexes):
        if isinstance(index, (list, np.ndarray, pd.Series, Base, Entity)):
            if not isinstance(index, (Base, Entity)):
                index = np.asarray(index)
            else:
                index = asarray(index)
                if ax == 1:
                    # do not support tensor index on axis 1
                    # because if so, the dtypes and columns_value would be unknown
                    try:
                        index = index.fetch()
                    except (RuntimeError, ValueError):
                        raise NotImplementedError('indexer on axis columns cannot be '
                                                  'non-executed tensor')
        new_indexes.append(index)

    return new_indexes 

def test_getitem_setitem_fancy_exceptions(self):
        ix = self.frame.iloc
        with pytest.raises(IndexingError, match='Too many indexers'):
            ix[:, :, :]

        with pytest.raises(IndexingError):
            ix[:, :, :] = 1 

def sdc_reindex_series_overload(arr, index, name, by_index):
    """ Reindexes series data by new index following the logic of pandas.core.indexing.check_bool_indexer """

    range_indexes = isinstance(index, RangeIndexType) and isinstance(by_index, RangeIndexType)
    data_dtype, index_dtype = arr.dtype, index.dtype
    data_is_str_arr = isinstance(arr.dtype, types.UnicodeType)

    def sdc_reindex_series_impl(arr, index, name, by_index):

        # no reindexing is needed if indexes are equal
        if range_indexes == True:  # noqa
            equal_indexes = numpy_like.array_equal(index, by_index)
        else:
            equal_indexes = False
        if (index is by_index or equal_indexes):
            return pandas.Series(data=arr, index=by_index, name=name)

        if data_is_str_arr == True:  # noqa
            _res_data = [''] * len(by_index)
            res_data_nan_mask = numpy.zeros(len(by_index), dtype=types.bool_)
        else:
            _res_data = numpy.empty(len(by_index), dtype=data_dtype)

        # build a dict of self.index values to their positions:
        map_index_to_position = Dict.empty(
            key_type=index_dtype,
            value_type=types.int32
        )

        for i, value in enumerate(index):
            if value in map_index_to_position:
                raise ValueError("cannot reindex from a duplicate axis")
            else:
                map_index_to_position[value] = i

        index_mismatch = 0
        # FIXME: TypingError in parfor step (wrong promotion to float64?) if prange is used
        for i in numpy.arange(len(by_index)):
            if by_index[i] in map_index_to_position:
                pos_in_self = map_index_to_position[by_index[i]]
                _res_data[i] = arr[pos_in_self]
                if data_is_str_arr == True:  # noqa
                    res_data_nan_mask[i] = isna(arr, i)
            else:
                index_mismatch += 1
        if index_mismatch:
            msg = "Unalignable boolean Series provided as indexer " + \
                  "(index of the boolean Series and of the indexed object do not match)."
            raise IndexingError(msg)

        if data_is_str_arr == True:  # noqa
            res_data = create_str_arr_from_list(_res_data)
            str_arr_set_na_by_mask(res_data, res_data_nan_mask)
        else:
            res_data = _res_data

        return pandas.Series(data=res_data, index=by_index, name=name)

    return sdc_reindex_series_impl 

def df_getitem_bool_series_idx_main_codelines(self, idx):
    """Generate main code lines for df.getitem"""
    length_expr = df_length_expr(self)

    # optimization for default indexes in df and idx when index alignment is trivial
    if (isinstance(self.index, types.NoneType) and isinstance(idx.index, types.NoneType)):
        func_lines = [f'  length = {length_expr}',
                      f'  self_index = self.index',
                      f'  if length > len(idx):',
                      f'    msg = "Unalignable boolean Series provided as indexer " + \\',
                      f'          "(index of the boolean Series and of the indexed object do not match)."',
                      f'    raise IndexingError(msg)',
                      f'  # do not trim idx._data to length as getitem_by_mask handles such case',
                      f'  res_index = getitem_by_mask(self_index, idx._data)',
                      f'  # df index is default, same as positions so it can be used in take']
        results = []
        for i, col in enumerate(self.columns):
            col_loc = self.column_loc[col]
            type_id, col_id = col_loc.type_id, col_loc.col_id
            res_data = f'res_data_{i}'
            func_lines += [
                f'  data_{i} = self._data[{type_id}][{col_id}]',
                f'  {res_data} = sdc_take(data_{i}, res_index)'
            ]
            results.append((col, res_data))

        data = ', '.join(f'"{col}": {data}' for col, data in results)
        func_lines += [
            f'  return pandas.DataFrame({{{data}}}, index=res_index)'
        ]
    else:
        func_lines = [
            f'  length = {length_expr}',
            f'  self_index = self.index',
            f'  reindexed_idx = sdc_reindex_series(idx._data, idx.index, idx._name, self_index)',
            f'  res_index = getitem_by_mask(self_index, reindexed_idx._data)',
            f'  selected_pos = getitem_by_mask(numpy.arange(length), reindexed_idx._data)'
        ]
        results = []
        for i, col in enumerate(self.columns):
            col_loc = self.column_loc[col]
            type_id, col_id = col_loc.type_id, col_loc.col_id
            res_data = f'res_data_{i}'
            func_lines += [
                f'  data_{i} = self._data[{type_id}][{col_id}]',
                f'  {res_data} = sdc_take(data_{i}, selected_pos)'
            ]
            results.append((col, res_data))

        data = ', '.join(f'"{col}": {data}' for col, data in results)
        func_lines += [
            f'  return pandas.DataFrame({{{data}}}, index=res_index)'
        ]

    return func_lines 

def test_partial_slicing_with_multiindex(self):

        # GH 4758
        # partial string indexing with a multi-index buggy
        df = DataFrame({'ACCOUNT': ["ACCT1", "ACCT1", "ACCT1", "ACCT2"],
                        'TICKER': ["ABC", "MNP", "XYZ", "XYZ"],
                        'val': [1, 2, 3, 4]},
                       index=date_range("2013-06-19 09:30:00",
                                        periods=4, freq='5T'))
        df_multi = df.set_index(['ACCOUNT', 'TICKER'], append=True)

        expected = DataFrame([
            [1]
        ], index=Index(['ABC'], name='TICKER'), columns=['val'])
        result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1')]
        tm.assert_frame_equal(result, expected)

        expected = df_multi.loc[
            (pd.Timestamp('2013-06-19 09:30:00', tz=None), 'ACCT1', 'ABC')]
        result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1', 'ABC')]
        tm.assert_series_equal(result, expected)

        # this is an IndexingError as we don't do partial string selection on
        # multi-levels.
        def f():
            df_multi.loc[('2013-06-19', 'ACCT1', 'ABC')]

        pytest.raises(IndexingError, f)

        # GH 4294
        # partial slice on a series mi
        s = pd.DataFrame(np.random.rand(1000, 1000), index=pd.date_range(
            '2000-1-1', periods=1000)).stack()

        s2 = s[:-1].copy()
        expected = s2['2000-1-4']
        result = s2[pd.Timestamp('2000-1-4')]
        tm.assert_series_equal(result, expected)

        result = s[pd.Timestamp('2000-1-4')]
        expected = s['2000-1-4']
        tm.assert_series_equal(result, expected)

        df2 = pd.DataFrame(s)
        expected = df2.xs('2000-1-4')
        result = df2.loc[pd.Timestamp('2000-1-4')]
        tm.assert_frame_equal(result, expected) 

def process_iloc_indexes(inp, indexes):
    ndim = inp.ndim

    if not isinstance(indexes, tuple):
        indexes = (indexes,)
    if len(indexes) < ndim:
        indexes += (slice(None),) * (ndim - len(indexes))
    if len(indexes) > ndim:
        raise IndexingError('Too many indexers')

    new_indexes = []
    # check each index
    for ax, index in enumerate(indexes):
        if isinstance(index, tuple):
            # a tuple should already have been caught by this point
            # so don't treat a tuple as a valid indexer
            raise IndexingError("Too many indexers")
        elif isinstance(index, slice):
            if any(v is not None for v in [index.start, index.stop, index.step]):
                pd_index = (inp.index_value if ax == 0 else inp.columns_value).to_pandas()
                for val in [index.start, index.stop, index.step]:
                    if val is not None:
                        try:
                            pd_index[val]  # check on the pandas
                        except IndexError:
                            pass
                        except TypeError:
                            raise TypeError(
                                'cannot do slice indexing on {} '
                                'with these indexers [{}] '
                                'of {}'.format(type(pd_index), val, type(val)))
            new_indexes.append(index)
        elif isinstance(index, (list, np.ndarray, pd.Series, Base, Entity)):
            if not isinstance(index, (Base, Entity)):
                index = np.asarray(index)
            else:
                index = asarray(index)
                if ax == 1:
                    # do not support tensor index on axis 1
                    # because if so, the dtypes and columns_value would be unknown
                    try:
                        index = index.fetch()
                    except (RuntimeError, ValueError):
                        raise NotImplementedError('indexer on axis columns cannot be '
                                                  'non-executed tensor')
            if index.dtype != np.bool_:
                index = index.astype(np.int64)
            if index.ndim != 1:
                raise ValueError('Buffer has wrong number of dimensions '
                                 '(expected 1, got {})'.format(index.ndim))
            new_indexes.append(index)
        elif isinstance(index, Integral):
            shape = inp.shape[ax]
            if not np.isnan(shape):
                if index < -shape or index >= shape:
                    raise IndexError('single positional indexer is out-of-bounds')
            new_indexes.append(index)
        else:
            raise ValueError(_ILOC_ERROR_MSG)

    return new_indexes 

def test_partial_slicing_with_multiindex(self):

        # GH 4758
        # partial string indexing with a multi-index buggy
        df = DataFrame({'ACCOUNT': ["ACCT1", "ACCT1", "ACCT1", "ACCT2"],
                        'TICKER': ["ABC", "MNP", "XYZ", "XYZ"],
                        'val': [1, 2, 3, 4]},
                       index=date_range("2013-06-19 09:30:00",
                                        periods=4, freq='5T'))
        df_multi = df.set_index(['ACCOUNT', 'TICKER'], append=True)

        expected = DataFrame([
            [1]
        ], index=Index(['ABC'], name='TICKER'), columns=['val'])
        result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1')]
        tm.assert_frame_equal(result, expected)

        expected = df_multi.loc[
            (pd.Timestamp('2013-06-19 09:30:00', tz=None), 'ACCT1', 'ABC')]
        result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1', 'ABC')]
        tm.assert_series_equal(result, expected)

        # this is an IndexingError as we don't do partial string selection on
        # multi-levels.
        def f():
            df_multi.loc[('2013-06-19', 'ACCT1', 'ABC')]

        pytest.raises(IndexingError, f)

        # GH 4294
        # partial slice on a series mi
        s = pd.DataFrame(np.random.rand(1000, 1000), index=pd.date_range(
            '2000-1-1', periods=1000)).stack()

        s2 = s[:-1].copy()
        expected = s2['2000-1-4']
        result = s2[pd.Timestamp('2000-1-4')]
        tm.assert_series_equal(result, expected)

        result = s[pd.Timestamp('2000-1-4')]
        expected = s['2000-1-4']
        tm.assert_series_equal(result, expected)

        df2 = pd.DataFrame(s)
        expected = df2.xs('2000-1-4')
        result = df2.loc[pd.Timestamp('2000-1-4')]
        tm.assert_frame_equal(result, expected) 

def test_partial_slicing_with_multiindex(self):

        # GH 4758
        # partial string indexing with a multi-index buggy
        df = DataFrame({'ACCOUNT': ["ACCT1", "ACCT1", "ACCT1", "ACCT2"],
                        'TICKER': ["ABC", "MNP", "XYZ", "XYZ"],
                        'val': [1, 2, 3, 4]},
                       index=date_range("2013-06-19 09:30:00",
                                        periods=4, freq='5T'))
        df_multi = df.set_index(['ACCOUNT', 'TICKER'], append=True)

        expected = DataFrame([
            [1]
        ], index=Index(['ABC'], name='TICKER'), columns=['val'])
        result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1')]
        tm.assert_frame_equal(result, expected)

        expected = df_multi.loc[
            (pd.Timestamp('2013-06-19 09:30:00', tz=None), 'ACCT1', 'ABC')]
        result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1', 'ABC')]
        tm.assert_series_equal(result, expected)

        # this is an IndexingError as we don't do partial string selection on
        # multi-levels.
        def f():
            df_multi.loc[('2013-06-19', 'ACCT1', 'ABC')]

        pytest.raises(IndexingError, f)

        # GH 4294
        # partial slice on a series mi
        s = pd.DataFrame(np.random.rand(1000, 1000), index=pd.date_range(
            '2000-1-1', periods=1000)).stack()

        s2 = s[:-1].copy()
        expected = s2['2000-1-4']
        result = s2[pd.Timestamp('2000-1-4')]
        tm.assert_series_equal(result, expected)

        result = s[pd.Timestamp('2000-1-4')]
        expected = s['2000-1-4']
        tm.assert_series_equal(result, expected)

        df2 = pd.DataFrame(s)
        expected = df2.xs('2000-1-4')
        result = df2.loc[pd.Timestamp('2000-1-4')]
        tm.assert_frame_equal(result, expected)