text: from datetime import date, datetime, time, timedelta
  from warnings import catch_warnings, simplefilter
  
  import numpy as np
  import pytest
  
  from pandas._libs.tslib import iNaT
  
  from pandas.core.dtypes.common import is_float_dtype, is_integer, is_scalar
  from pandas.core.dtypes.dtypes import CategoricalDtype
  
  import pandas as pd
  from pandas import (
      Categorical, DataFrame, DatetimeIndex, Index, MultiIndex, Series,
      Timestamp, date_range, isna, notna)
  import pandas.core.common as com
  from pandas.core.indexing import IndexingError
  from pandas.tests.frame.common import TestData
  import pandas.util.testing as tm
  from pandas.util.testing import (
      assert_almost_equal, assert_frame_equal, assert_series_equal)
  
  from pandas.tseries.offsets import BDay
  
  
  class TestDataFrameIndexing(TestData):
  
      def test_getitem(self):
          # Slicing
          sl = self.frame[:20]
          assert len(sl.index) == 20
  
          # Column access
          for _, series in sl.items():
              assert len(series.index) == 20
              assert tm.equalContents(series.index, sl.index)
  
          for key, _ in self.frame._series.items():
              assert self.frame[key] is not None
  
          assert 'random' not in self.frame
          with pytest.raises(KeyError, match='random'):
              self.frame['random']
  
          df = self.frame.copy()
          df['$10'] = np.random.randn(len(df))
  
          ad = np.random.randn(len(df))
          df['@awesome_domain'] = ad
  
          with pytest.raises(KeyError):
              df.__getitem__('df["$10"]')
  
          res = df['@awesome_domain']
          tm.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'])
          with pytest.raises(KeyError):
              df[['baf']]
  
      def test_get(self):
          b = self.frame.get('B')
          assert_series_equal(b, self.frame['B'])
  
          assert self.frame.get('foo') is None
          assert_series_equal(self.frame.get('foo', self.frame['B']),
                              self.frame['B'])
  
      @pytest.mark.parametrize("df", [
          DataFrame(),
          DataFrame(columns=list("AB")),
          DataFrame(columns=list("AB"), index=range(3))
      ])
      def test_get_none(self, df):
          # see gh-5652
          assert df.get(None) is None
  
      def test_loc_iterable(self):
          idx = iter(['A', 'B', 'C'])
          result = self.frame.loc[:, idx]
          expected = self.frame.loc[:, ['A', 'B', 'C']]
          assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize(
          "idx_type",
          [list, iter, Index, set,
           lambda l: dict(zip(l, range(len(l)))),
           lambda l: dict(zip(l, range(len(l)))).keys()],
          ids=["list", "iter", "Index", "set", "dict", "dict_keys"])
      @pytest.mark.parametrize("levels", [1, 2])
      def test_getitem_listlike(self, idx_type, levels):
          # GH 21294
  
          if levels == 1:
              frame, missing = self.frame, 'food'
          else:
              # MultiIndex columns
              frame = DataFrame(np.random.randn(8, 3),
                                columns=Index([('foo', 'bar'), ('baz', 'qux'),
                                               ('peek', 'aboo')],
                                              name=('sth', 'sth2')))
              missing = ('good', 'food')
  
          keys = [frame.columns[1], frame.columns[0]]
          idx = idx_type(keys)
          idx_check = list(idx_type(keys))
  
          result = frame[idx]
  
          expected = frame.loc[:, idx_check]
          expected.columns.names = frame.columns.names
  
          assert_frame_equal(result, expected)
  
          idx = idx_type(keys + [missing])
          with pytest.raises(KeyError, match='not in index'):
              frame[idx]
  
      @pytest.mark.parametrize("val,expected", [
          (2**63 - 1, Series([1])),
          (2**63, Series([2])),
      ])
      def test_loc_uint64(self, val, expected):
          # see gh-19399
          df = DataFrame([1, 2], index=[2**63 - 1, 2**63])
          result = df.loc[val]
  
          expected.name = val
          tm.assert_series_equal(result, expected)
  
      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)
  
          msg = 'Columns must be same length as key'
          with pytest.raises(ValueError, match=msg):
              data[['A']] = self.frame[['A', 'B']]
  
          msg = 'Length of values does not match length of index'
          with pytest.raises(ValueError, match=msg):
              data['A'] = range(len(data.index) - 1)
  
          df = DataFrame(0, index=range(3), columns=['tt1', 'tt2'],
                         dtype=np.int_)
          df.loc[1, ['tt1', 'tt2']] = [1, 2]
  
          result = df.loc[df.index[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.loc[df.index[1], ['tt1', 'tt2']] = ['1', '2']
          result = df.loc[df.index[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 = list(zip(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
          def inc(x):
              return 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]
  
          tm.assert_index_equal(subindex, subframe.index)
          with pytest.raises(ValueError, match='Item wrong length'):
              self.tsframe[indexer[:-1]]
  
          subframe_obj = self.tsframe[indexer_obj]
          assert_frame_equal(subframe_obj, subframe)
  
          with pytest.raises(ValueError, match='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]:
              if df is self.mixed_frame:
                  continue
  
              data = df._get_numeric_data()
              bif = df[df > 0]
              bifw = DataFrame({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:
                      assert 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.loc[df.index[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 = isna(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.loc[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 = np.random.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.iloc[:-1]
          expected = df.loc[df.index[:-1]]
          assert_frame_equal(result, expected)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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):
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = self.frame.ix[:, -1]
          assert_series_equal(result, self.frame['D'])
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = self.frame.ix[:, [-1]]
          assert_frame_equal(result, self.frame[['D']])
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = self.frame.ix[:, [-1, -2]]
          assert_frame_equal(result, self.frame[['D', 'C']])
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              self.frame.ix[:, [-1]] = 0
          assert (self.frame['D'] == 0).all()
  
          df = DataFrame(np.random.randn(8, 4))
          # ix does label-based indexing when having an integer index
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              with pytest.raises(KeyError):
                  df.ix[[-1]]
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              with pytest.raises(KeyError):
                  df.ix[:, [-1]]
  
          # #1942
          a = DataFrame(np.random.randn(20, 2),
                        index=[chr(x + 65) for x in range(20)])
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              a.ix[-1] = a.ix[-2]
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              assert_series_equal(a.ix[-1], a.ix[-2], check_names=False)
              assert a.ix[-1].name == 'T'
              assert a.ix[-2].name == 'S'
  
      def test_getattr(self):
          assert_series_equal(self.frame.A, self.frame['A'])
          msg = "'DataFrame' object has no attribute 'NONEXISTENT_NAME'"
          with pytest.raises(AttributeError, match=msg):
              self.frame.NONEXISTENT_NAME
  
      def test_setattr_column(self):
          df = DataFrame({'foobar': 1}, index=range(10))
  
          df.foobar = 5
          assert (df.foobar == 5).all()
  
      def test_setitem(self):
          # not sure what else to do here
          series = self.frame['A'][::2]
          self.frame['col5'] = series
          assert 'col5' in self.frame
  
          assert len(series) == 15
          assert 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 pytest.raises(KeyError):
              self.frame[np.random.randn(len(self.frame) + 1)] = 1
  
          # set ndarray
          arr = np.random.randn(len(self.frame))
          self.frame['col9'] = arr
          assert (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]
  
          with pytest.raises(com.SettingWithCopyError):
              smaller['col10'] = ['1', '2']
  
          assert smaller['col10'].dtype == np.object_
          assert (smaller['col10'] == ['1', '2']).all()
  
          # 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)
  
      @pytest.mark.parametrize("dtype", ["int32", "int64", "float32", "float64"])
      def test_setitem_dtype(self, dtype):
          arr = np.random.randn(len(self.frame))
  
          self.frame[dtype] = np.array(arr, dtype=dtype)
          assert self.frame[dtype].dtype.name == dtype
  
      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] = np.nan
          assert notna(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)
  
          msg = "Must pass DataFrame or 2-d ndarray with boolean values only"
          with pytest.raises(TypeError, match=msg):
              df[df * 0] = 2
  
          # index with DataFrame
          mask = df > np.abs(df)
          expected = df.copy()
          df[df > np.abs(df)] = np.nan
          expected.values[mask.values] = np.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)
  
      @pytest.mark.parametrize(
          "mask_type",
          [lambda df: df > np.abs(df) / 2,
           lambda df: (df > np.abs(df) / 2).values],
          ids=['dataframe', 'array'])
      def test_setitem_boolean_mask(self, mask_type):
  
          # Test for issue #18582
          df = self.frame.copy()
          mask = mask_type(df)
  
          # index with boolean mask
          result = df.copy()
          result[mask] = np.nan
  
          expected = df.copy()
          expected.values[np.array(mask)] = np.nan
          assert_frame_equal(result, expected)
  
      def test_setitem_cast(self):
          self.frame['D'] = self.frame['D'].astype('i8')
          assert 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
          assert self.frame['B'].dtype == np.int64
  
          # cast if pass array of course
          self.frame['B'] = np.arange(len(self.frame))
          assert issubclass(self.frame['B'].dtype.type, np.integer)
  
          self.frame['foo'] = 'bar'
          self.frame['foo'] = 0
          assert self.frame['foo'].dtype == np.int64
  
          self.frame['foo'] = 'bar'
          self.frame['foo'] = 2.5
          assert self.frame['foo'].dtype == np.float64
  
          self.frame['something'] = 0
          assert self.frame['something'].dtype == np.int64
          self.frame['something'] = 2
          assert self.frame['something'].dtype == np.int64
          self.frame['something'] = 2.5
          assert 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
          assert df.dtypes.one == np.dtype(np.int8)
          df.one = np.int8(7)
          assert df.dtypes.one == np.dtype(np.int8)
  
      def test_setitem_boolean_column(self):
          expected = self.frame.copy()
          mask = self.frame['A'] > 0
  
          self.frame.loc[mask, 'B'] = 0
          expected.values[mask.values, 1] = 0
  
          assert_frame_equal(self.frame, expected)
  
      def test_frame_setitem_timestamp(self):
          # GH#2155
          columns = date_range(start='1/1/2012', end='2/1/2012', freq=BDay())
          data = DataFrame(columns=columns, index=range(10))
          t = datetime(2012, 11, 1)
          ts = Timestamp(t)
          data[ts] = np.nan  # works, mostly a smoke-test
          assert np.isnan(data[ts]).all()
  
      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.]
          assert 'B' in df
          assert 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'
          assert len(dm.columns) == 2
          assert dm.values.dtype == np.object_
  
          # upcast
          dm['C'] = 1
          assert dm['C'].dtype == np.int64
  
          dm['E'] = 1.
          assert dm['E'].dtype == np.float64
  
          # set existing column
          dm['A'] = 'bar'
          assert 'bar' == dm['A'][0]
  
          dm = DataFrame(index=np.arange(3))
          dm['A'] = 1
          dm['foo'] = 'bar'
          del dm['foo']
          dm['foo'] = 'bar'
          assert dm['foo'].dtype == np.object_
  
          dm['coercable'] = ['1', '2', '3']
          assert 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.loc[ix, ['title']] = 'foobar'
          df.loc[ix, ['cruft']] = 0
  
          assert df.loc[1, 'title'] == 'foobar'
          assert df.loc[1, 'cruft'] == 0
  
      def test_setitem_ambig(self):
          # Difficulties with mixed-type data
          from decimal import Decimal
  
          # Created as float type
          dm = DataFrame(index=range(3), columns=range(3))
  
          coercable_series = Series([Decimal(1) for _ in range(3)],
                                    index=range(3))
          uncoercable_series = Series(['foo', 'bzr', 'baz'], index=range(3))
  
          dm[0] = np.ones(3)
          assert len(dm.columns) == 3
  
          dm[1] = coercable_series
          assert len(dm.columns) == 3
  
          dm[2] = uncoercable_series
          assert len(dm.columns) == 3
          assert dm[2].dtype == np.object_
  
      def test_setitem_clear_caches(self):
          # see 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.loc[df.index[2:], 'z'] = 42
  
          expected = Series([np.nan, np.nan, 42, 42], index=df.index, name='z')
  
          assert df['z'] is not foo
          tm.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.isna(), 'a'] = result.a
          assert_frame_equal(result, df)
  
      @pytest.mark.parametrize("dtype", ["float", "int64"])
      @pytest.mark.parametrize("kwargs", [
          dict(),
          dict(index=[1]),
          dict(columns=["A"])
      ])
      def test_setitem_empty_frame_with_boolean(self, dtype, kwargs):
          # see gh-10126
          kwargs["dtype"] = dtype
          df = DataFrame(**kwargs)
  
          df2 = df.copy()
          df[df > df2] = 47
          assert_frame_equal(df, df2)
  
      def test_setitem_scalars_no_index(self):
          # GH16823 / 17894
          df = DataFrame()
          df['foo'] = 1
          expected = DataFrame(columns=['foo']).astype(np.int64)
          assert_frame_equal(df, expected)
  
      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']
          assert len(f.columns) == 3
          with pytest.raises(KeyError, match=r"^'D'$"):
              del f['D']
          del f['B']
          assert len(f.columns) == 2
  
      def test_getitem_fancy_2d(self):
          f = self.frame
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              assert_frame_equal(f.ix[:, ['B', 'A']],
                                 f.reindex(columns=['B', 'A']))
  
          subidx = self.frame.index[[5, 4, 1]]
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              assert_frame_equal(f.ix[subidx, ['B', 'A']],
                                 f.reindex(index=subidx, columns=['B', 'A']))
  
          # slicing rows, etc.
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              assert_frame_equal(f.ix[5:10], f[5:10])
              assert_frame_equal(f.ix[5:10, :], f[5:10])
              assert_frame_equal(f.ix[:5, ['A', 'B']],
                                 f.reindex(index=f.index[:5],
                                           columns=['A', 'B']))
  
          # slice rows with labels, inclusive!
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              expected = f.ix[5:11]
              result = f.ix[f.index[5]:f.index[10]]
          assert_frame_equal(expected, result)
  
          # slice columns
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              assert_frame_equal(f.ix[:, :2], f.reindex(columns=['A', 'B']))
  
          # get view
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              exp = f.copy()
              f.ix[5:10].values[:] = 5
              exp.values[5:10] = 5
              assert_frame_equal(f, exp)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              msg = "Cannot index with multidimensional key"
              with pytest.raises(ValueError, match=msg):
                  f.ix[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.loc[52195.1:52196.5]
          assert len(s1) == 2
  
          s1 = df.loc[52195.1:52196.6]
          assert len(s1) == 2
  
          s1 = df.loc[52195.1:52198.9]
          assert len(s1) == 3
  
      def test_getitem_fancy_slice_integers_step(self):
          df = DataFrame(np.random.randn(10, 5))
  
          # this is OK
          result = df.iloc[:8:2]  # noqa
          df.iloc[:8:2] = np.nan
          assert isna(df.iloc[:8:2]).values.all()
  
      def test_getitem_setitem_integer_slice_keyerrors(self):
          df = DataFrame(np.random.randn(10, 5), index=range(0, 20, 2))
  
          # this is OK
          cp = df.copy()
          cp.iloc[4:10] = 0
          assert (cp.iloc[4:10] == 0).values.all()
  
          # so is this
          cp = df.copy()
          cp.iloc[3:11] = 0
          assert (cp.iloc[3:11] == 0).values.all()
  
          result = df.iloc[2:6]
          result2 = df.loc[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[list(range(5)) + list(range(5, 10))[::-1]]
          with pytest.raises(KeyError, match=r"^3$"):
              df2.loc[3:11]
          with pytest.raises(KeyError, match=r"^3$"):
              df2.loc[3:11] = 0
  
      def test_setitem_fancy_2d(self):
  
          # case 1
          frame = self.frame.copy()
          expected = frame.copy()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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 = np.random.randn(3, 2)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              expected1 = self.frame.copy()
              frame.ix[5:10] = 1.
              expected1.values[5:10] = 1.
          assert_frame_equal(frame, expected1)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              expected2 = self.frame.copy()
              arr = np.random.randn(5, len(frame.columns))
              frame.ix[5:10] = arr
              expected2.values[5:10] = arr
          assert_frame_equal(frame, expected2)
  
          # case 4
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              frame = self.frame.copy()
              frame2 = self.frame.copy()
  
              expected = self.frame.copy()
              values = np.random.randn(5, 2)
  
              frame.ix[:5, ['A', 'B']] = values
              expected['A'][:5] = values[:, 0]
              expected['B'][:5] = values[:, 1]
          assert_frame_equal(frame, expected)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              frame2.ix[:5, [0, 1]] = values
          assert_frame_equal(frame2, expected)
  
          # case 6: slice rows with labels, inclusive!
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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(zip([2, 3, 9, 6, 7], [np.nan] * 5),
                            columns=['a', 'b'])
          lst = [100]
          lst.extend([np.nan] * 4)
          expected = DataFrame(zip([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.iloc[:, -3:]
          assert sliced['D'].dtype == np.float64
  
          # get view with single block
          # setting it triggers setting with copy
          sliced = self.frame.iloc[:, -3:]
  
          with pytest.raises(com.SettingWithCopyError):
              sliced['C'] = 4.
  
          assert (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))
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              tmp = df.copy()
              exp = df.copy()
              tmp.ix[[0, 2, 4]] = 5
              exp.values[:3] = 5
          assert_frame_equal(tmp, exp)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              tmp = df.copy()
              exp = df.copy()
              tmp.ix[6] = 5
              exp.values[3] = 5
          assert_frame_equal(tmp, exp)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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))
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = df.ix[[4, 2, 0], [2, 0]]
              expected = df.reindex(index=[4, 2, 0], columns=[2, 0])
          assert_frame_equal(result, expected)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = df.ix[[4, 2, 0]]
              expected = df.reindex(index=[4, 2, 0])
          assert_frame_equal(result, expected)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = df.ix[4]
              expected = df.xs(4)
          assert_series_equal(result, expected)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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))
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
  
              # labels that aren't contained
              with pytest.raises(KeyError, match=r"\[1\] not in index"):
                  df.ix[[0, 1, 2], [2, 3, 4]] = 5
  
              # try to set indices not contained in frame
              msg = (r"None of \[Index\(\['foo', 'bar', 'baz'\],"
                     r" dtype='object'\)\] are in the \[index\]")
              with pytest.raises(KeyError, match=msg):
                  self.frame.ix[['foo', 'bar', 'baz']] = 1
              msg = (r"None of \[Index\(\['E'\], dtype='object'\)\] are in the"
                     r" \[columns\]")
              with pytest.raises(KeyError, match=msg):
                  self.frame.ix[:, ['E']] = 1
  
              # partial setting now allows this GH2578
              # pytest.raises(KeyError, self.frame.ix.__setitem__,
              #               (slice(None, None), 'E'), 1)
  
      def test_setitem_fancy_mixed_2d(self):
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              self.mixed_frame.ix[:5, ['C', 'B', 'A']] = 5
              result = self.mixed_frame.ix[:5, ['C', 'B', 'A']]
              assert (result.values == 5).all()
  
              self.mixed_frame.ix[5] = np.nan
              assert isna(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
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              df = DataFrame({1: [1., 2., 3.],
                              2: [3, 4, 5]})
              assert 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(np.random.randn(10), name=0).sort_values()
          df_orig = DataFrame(np.random.randn(10, 4))
          df = df_orig.copy()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              df.ix[:, 0] = b
              assert_series_equal(df.ix[:, 0].reindex(b.index), b)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              dft = df_orig.T
              dft.ix[0, :] = b
              assert_series_equal(dft.ix[0, :].reindex(b.index), b)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              df = df_orig.copy()
              df.ix[:5, 0] = b
              s = df.ix[:5, 0]
              assert_series_equal(s, b.reindex(s.index))
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              dft = df_orig.T
              dft.ix[0, :5] = b
              s = dft.ix[0, :5]
              assert_series_equal(s, b.reindex(s.index))
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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))
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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(np.random.randn(10, 4))
          df = df_orig.copy()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              df.ix[:3] = b
              out = b.ix[:3]
              assert_frame_equal(out, b)
  
          b.sort_index(inplace=True)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              df = df_orig.copy()
              df.ix[[0, 1, 2]] = b
              out = df.ix[[0, 1, 2]].reindex(b.index)
              assert_frame_equal(out, b)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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.loc[start:end]
          result2 = df[start:end]
          expected = df[5:11]
          assert_frame_equal(result, expected)
          assert_frame_equal(result2, expected)
  
          result = df.copy()
          result.loc[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.loc[df.index == 0, :]
          xp = df.reindex([0])
          assert_frame_equal(rs, xp)
  
          """ #1321
          df = DataFrame(np.random.randn(3, 2))
          rs = df.loc[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'])
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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']])
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              sub = df.ix[:'d']
              assert_frame_equal(sub, df)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              sub = df.ix['a':'c']
              assert_frame_equal(sub, df.ix[0:4])
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              sub = df.ix['b':'d']
              assert_frame_equal(sub, df.ix[2:])
  
      def test_getitem_fancy_1d(self):
          f = self.frame
  
          # return self if no slicing...for now
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              assert f.ix[:, :] is f
  
          # low dimensional slice
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              xs1 = f.ix[2, ['C', 'B', 'A']]
          xs2 = f.xs(f.index[2]).reindex(['C', 'B', 'A'])
          tm.assert_series_equal(xs1, xs2)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              ts1 = f.ix[5:10, 2]
          ts2 = f[f.columns[2]][5:10]
          tm.assert_series_equal(ts1, ts2)
  
          # positional xs
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              xs1 = f.ix[0]
          xs2 = f.xs(f.index[0])
          tm.assert_series_equal(xs1, xs2)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              xs1 = f.ix[f.index[5]]
          xs2 = f.xs(f.index[5])
          tm.assert_series_equal(xs1, xs2)
  
          # single column
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              assert_series_equal(f.ix[:, 'A'], f['A'])
  
          # return view
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              exp = f.copy()
              exp.values[5] = 4
              f.ix[5][:] = 4
          tm.assert_frame_equal(exp, f)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              exp.values[:, 1] = 6
              f.ix[:, 1][:] = 6
          tm.assert_frame_equal(exp, f)
  
          # slice of mixed-frame
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              xs = self.mixed_frame.ix[5]
          exp = self.mixed_frame.xs(self.mixed_frame.index[5])
          tm.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()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              vals = np.random.randn(5)
              expected.values[5:10, 2] = vals
              frame.ix[5:10, 2] = vals
          assert_frame_equal(frame, expected)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              frame.ix[4] = 5.
              expected.values[4] = 5.
          assert_frame_equal(frame, expected)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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()
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              frame.ix[:, 'A'] = 7.
              expected['A'] = 7.
          assert_frame_equal(frame, expected)
  
      def test_getitem_fancy_scalar(self):
          f = self.frame
          ix = f.loc
  
          # individual value
          for col in f.columns:
              ts = f[col]
              for idx in f.index[::5]:
                  assert ix[idx, col] == ts[idx]
  
      def test_setitem_fancy_scalar(self):
          f = self.frame
          expected = self.frame.copy()
          ix = f.loc
  
          # 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 = np.random.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.loc
  
          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[f.index[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, f.columns[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.loc[mask] = 0.
          expected.values[mask.values] = 0.
          assert_frame_equal(frame, expected)
  
          frame = self.frame.copy()
          expected = self.frame.copy()
          frame.loc[mask, ['A', 'B']] = 0.
          expected.values[mask.values, :2] = 0.
          assert_frame_equal(frame, expected)
  
      def test_getitem_fancy_ints(self):
          result = self.frame.iloc[[1, 4, 7]]
          expected = self.frame.loc[self.frame.index[[1, 4, 7]]]
          assert_frame_equal(result, expected)
  
          result = self.frame.iloc[:, [2, 0, 1]]
          expected = self.frame.loc[:, self.frame.columns[[2, 0, 1]]]
          assert_frame_equal(result, expected)
  
      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_setitem_boolean_misaligned(self):
          # boolean index misaligned labels
          mask = self.frame['A'][::-1] > 1
  
          result = self.frame.loc[mask]
          expected = self.frame.loc[mask[::-1]]
          assert_frame_equal(result, expected)
  
          cp = self.frame.copy()
          expected = self.frame.copy()
          cp.loc[mask] = 0
          expected.loc[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.loc[k1, k2]
          expected = df.loc[[0, 2], [1]]
          assert_frame_equal(result, expected)
  
          expected = df.copy()
          df.loc[np.array([True, False, True]),
                 np.array([False, True])] = 5
          expected.loc[[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.loc[1.5:4]
          expected = df.reindex([1.5, 2, 3, 4])
          assert_frame_equal(result, expected)
          assert len(result) == 4
  
          result = df.loc[4:5]
          expected = df.reindex([4, 5])  # reindex with int
          assert_frame_equal(result, expected, check_index_type=False)
          assert len(result) == 2
  
          result = df.loc[4:5]
          expected = df.reindex([4.0, 5.0])  # reindex with float
          assert_frame_equal(result, expected)
          assert len(result) == 2
  
          # loc_float changes this to work properly
          result = df.loc[1:2]
          expected = df.iloc[0:2]
          assert_frame_equal(result, expected)
  
          df.loc[1:2] = 0
          result = df[1:2]
          assert (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!
          msg = ("cannot do slice indexing on"
                 r" <class 'pandas\.core\.indexes\.numeric\.Float64Index'> with"
                 r" these indexers \[1.0\] of <class 'float'>")
          with pytest.raises(TypeError, match=msg):
              df.iloc[1.0:5]
  
          result = df.iloc[4:5]
          expected = df.reindex([5.0])
          assert_frame_equal(result, expected)
          assert len(result) == 1
  
          cp = df.copy()
  
          with pytest.raises(TypeError):
              cp.iloc[1.0:5] = 0
  
          with pytest.raises(TypeError):
              result = cp.iloc[1.0:5] == 0  # noqa
  
          assert result.values.all()
          assert (cp.iloc[0:1] == df.iloc[0:1]).values.all()
  
          cp = df.copy()
          cp.iloc[4:5] = 0
          assert (cp.iloc[4:5] == 0).values.all()
          assert (cp.iloc[0:4] == df.iloc[0:4]).values.all()
  
          # float slicing
          result = df.loc[1.0:5]
          expected = df
          assert_frame_equal(result, expected)
          assert len(result) == 5
  
          result = df.loc[1.1:5]
          expected = df.reindex([2.5, 3.5, 4.5, 5.0])
          assert_frame_equal(result, expected)
          assert len(result) == 4
  
          result = df.loc[4.51:5]
          expected = df.reindex([5.0])
          assert_frame_equal(result, expected)
          assert len(result) == 1
  
          result = df.loc[1.0:5.0]
          expected = df.reindex([1.0, 2.5, 3.5, 4.5, 5.0])
          assert_frame_equal(result, expected)
          assert len(result) == 5
  
          cp = df.copy()
          cp.loc[1.0:5.0] = 0
          result = cp.loc[1.0:5.0]
          assert (result == 0).values.all()
  
      def test_setitem_single_column_mixed(self):
          df = DataFrame(np.random.randn(5, 3), index=['a', 'b', 'c', 'd', 'e'],
                         columns=['foo', 'bar', 'baz'])
          df['str'] = 'qux'
          df.loc[df.index[::2], 'str'] = np.nan
          expected = np.array([np.nan, 'qux', np.nan, 'qux', np.nan],
                              dtype=object)
          assert_almost_equal(df['str'].values, expected)
  
      def test_setitem_single_column_mixed_datetime(self):
          df = DataFrame(np.random.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
          df.loc['b', 'timestamp'] = iNaT
          assert isna(df.loc['b', 'timestamp'])
  
          # allow this syntax
          df.loc['c', 'timestamp'] = np.nan
          assert isna(df.loc['c', 'timestamp'])
  
          # allow this syntax
          df.loc['d', :] = np.nan
          assert not isna(df.loc['c', :]).all()
  
          # as of GH 3216 this will now work!
          # try to set with a list like item
          # pytest.raises(
          #    Exception, df.loc.__setitem__, ('d', 'timestamp'), [np.nan])
  
      def test_setitem_mixed_datetime(self):
          # GH 9336
          expected = DataFrame({'a': [0, 0, 0, 0, 13, 14],
                                'b': [pd.datetime(2012, 1, 1),
                                      1,
                                      'x',
                                      'y',
                                      pd.datetime(2013, 1, 1),
                                      pd.datetime(2014, 1, 1)]})
          df = pd.DataFrame(0, columns=list('ab'), index=range(6))
          df['b'] = pd.NaT
          df.loc[0, 'b'] = pd.datetime(2012, 1, 1)
          df.loc[1, 'b'] = 1
          df.loc[[2, 3], 'b'] = 'x', 'y'
          A = np.array([[13, np.datetime64('2013-01-01T00:00:00')],
                        [14, np.datetime64('2014-01-01T00:00:00')]])
          df.loc[[4, 5], ['a', 'b']] = A
          assert_frame_equal(df, expected)
  
      def test_setitem_frame(self):
          piece = self.frame.loc[self.frame.index[:2], ['A', 'B']]
          self.frame.loc[self.frame.index[-2]:, ['A', 'B']] = piece.values
          result = self.frame.loc[self.frame.index[-2:], ['A', 'B']].values
          expected = piece.values
          assert_almost_equal(result, expected)
  
          # 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.loc[key] = piece
          assert_almost_equal(f.loc[f.index[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.loc[key] = piece
          assert_almost_equal(f.loc[f.index[0:2:], ['A', 'B']].values,
                              piece.values[0:2])
  
          # key is unaligned with values
          f = self.mixed_frame.copy()
          piece = f.loc[f.index[:2], ['A']]
          piece.index = f.index[-2:]
          key = (slice(-2, None), ['A', 'B'])
          f.loc[key] = piece
          piece['B'] = np.nan
          assert_almost_equal(f.loc[f.index[-2:], ['A', 'B']].values,
                              piece.values)
  
          # ndarray
          f = self.mixed_frame.copy()
          piece = self.mixed_frame.loc[f.index[:2], ['A', 'B']]
          key = (slice(-2, None), ['A', 'B'])
          f.loc[key] = piece.values
          assert_almost_equal(f.loc[f.index[-2:], ['A', 'B']].values,
                              piece.values)
  
          # needs upcasting
          df = DataFrame([[1, 2, 'foo'], [3, 4, 'bar']], columns=['A', 'B', 'C'])
          df2 = df.copy()
          df2.loc[:, ['A', 'B']] = df.loc[:, ['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.loc[self.frame.index[:2], ['A', 'B']]
          piece.index = self.frame.index[-2:]
          piece.columns = ['A', 'B']
          self.frame.loc[self.frame.index[-2:], ['A', 'B']] = piece
          result = self.frame.loc[self.frame.index[-2:], ['A', 'B']].values
          expected = piece.values
          assert_almost_equal(result, expected)
  
      def test_getitem_setitem_ix_duplicates(self):
          # #1201
          df = DataFrame(np.random.randn(5, 3),
                         index=['foo', 'foo', 'bar', 'baz', 'bar'])
  
          result = df.loc['foo']
          expected = df[:2]
          assert_frame_equal(result, expected)
  
          result = df.loc['bar']
          expected = df.iloc[[2, 4]]
          assert_frame_equal(result, expected)
  
          result = df.loc['baz']
          expected = df.iloc[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.loc[['bar']]
          exp = df.iloc[[2, 4]]
          assert_frame_equal(result, exp)
  
          result = df.loc[df[1] > 0]
          exp = df[df[1] > 0]
          assert_frame_equal(result, exp)
  
          result = df.loc[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]})
  
          with pytest.raises(KeyError, match=r"^False$"):
              df.loc[False]
          with pytest.raises(KeyError, match=r"^True$"):
              df.loc[True]
  
          msg = "cannot use a single bool to index into setitem"
          with pytest.raises(KeyError, match=msg):
              df.loc[False] = 0
          with pytest.raises(KeyError, match=msg):
              df.loc[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']]
          assert result.columns.name == 'foo'
  
          expected = df.iloc[:, 2:]
          assert_frame_equal(result, expected)
  
      def test_get_value(self):
          for idx in self.frame.index:
              for col in self.frame.columns:
                  with tm.assert_produces_warning(FutureWarning,
                                                  check_stacklevel=False):
                      result = self.frame.get_value(idx, col)
                  expected = self.frame[col][idx]
                  assert result == expected
  
      def test_lookup(self):
          def alt(df, rows, cols, dtype):
              with tm.assert_produces_warning(FutureWarning,
                                              check_stacklevel=False):
                  result = [df.get_value(r, c) for r, c in zip(rows, cols)]
              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'))
          assert df['mask'].dtype == np.bool_
  
          with pytest.raises(KeyError):
              self.frame.lookup(['xyz'], ['A'])
  
          with pytest.raises(KeyError):
              self.frame.lookup([self.frame.index[0]], ['xyz'])
  
          with pytest.raises(ValueError, match='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:
                  with tm.assert_produces_warning(FutureWarning,
                                                  check_stacklevel=False):
                      self.frame.set_value(idx, col, 1)
                  assert self.frame[col][idx] == 1
  
      def test_set_value_resize(self):
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              res = self.frame.set_value('foobar', 'B', 0)
          assert res is self.frame
          assert res.index[-1] == 'foobar'
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              assert res.get_value('foobar', 'B') == 0
  
          self.frame.loc['foobar', 'qux'] = 0
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              assert self.frame.get_value('foobar', 'qux') == 0
  
          res = self.frame.copy()
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              res3 = res.set_value('foobar', 'baz', 'sam')
          assert res3['baz'].dtype == np.object_
  
          res = self.frame.copy()
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              res3 = res.set_value('foobar', 'baz', True)
          assert res3['baz'].dtype == np.object_
  
          res = self.frame.copy()
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              res3 = res.set_value('foobar', 'baz', 5)
          assert is_float_dtype(res3['baz'])
          assert isna(res3['baz'].drop(['foobar'])).all()
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              msg = "could not convert string to float: 'sam'"
              with pytest.raises(ValueError, match=msg):
                  res3.set_value('foobar', 'baz', 'sam')
  
      def test_set_value_with_index_dtype_change(self):
          df_orig = DataFrame(np.random.randn(3, 3),
                              index=range(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()
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              df.set_value('C', 2, 1.0)
          assert list(df.index) == list(df_orig.index) + ['C']
          # assert list(df.columns) == list(df_orig.columns) + [2]
  
          df = df_orig.copy()
          df.loc['C', 2] = 1.0
          assert list(df.index) == list(df_orig.index) + ['C']
          # assert list(df.columns) == list(df_orig.columns) + [2]
  
          # create both new
          df = df_orig.copy()
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              df.set_value('C', 'D', 1.0)
          assert list(df.index) == list(df_orig.index) + ['C']
          assert list(df.columns) == list(df_orig.columns) + ['D']
  
          df = df_orig.copy()
          df.loc['C', 'D'] = 1.0
          assert list(df.index) == list(df_orig.index) + ['C']
          assert 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=range(4))
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              with pytest.raises(KeyError, match=r"^0$"):
                  df.get_value(0, 1)
  
      def test_single_element_ix_dont_upcast(self):
          self.frame['E'] = 1
          assert issubclass(self.frame['E'].dtype.type, (int, np.integer))
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = self.frame.ix[self.frame.index[5], 'E']
              assert is_integer(result)
  
          result = self.frame.loc[self.frame.index[5], 'E']
          assert is_integer(result)
  
          # GH 11617
          df = pd.DataFrame(dict(a=[1.23]))
          df["b"] = 666
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = df.ix[0, "b"]
          assert is_integer(result)
          result = df.loc[0, "b"]
          assert is_integer(result)
  
          expected = Series([666], [0], name='b')
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = df.ix[[0], "b"]
          assert_series_equal(result, expected)
          result = df.loc[[0], "b"]
          assert_series_equal(result, expected)
  
      def test_iloc_row(self):
          df = DataFrame(np.random.randn(10, 4), index=range(0, 20, 2))
  
          result = df.iloc[1]
          exp = df.loc[2]
          assert_series_equal(result, exp)
  
          result = df.iloc[2]
          exp = df.loc[4]
          assert_series_equal(result, exp)
  
          # slice
          result = df.iloc[slice(4, 8)]
          expected = df.loc[8:14]
          assert_frame_equal(result, expected)
  
          # verify slice is view
          # setting it makes it raise/warn
          with pytest.raises(com.SettingWithCopyError):
              result[2] = 0.
  
          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_iloc_col(self):
  
          df = DataFrame(np.random.randn(4, 10), columns=range(0, 20, 2))
  
          result = df.iloc[:, 1]
          exp = df.loc[:, 2]
          assert_series_equal(result, exp)
  
          result = df.iloc[:, 2]
          exp = df.loc[:, 4]
          assert_series_equal(result, exp)
  
          # slice
          result = df.iloc[:, slice(4, 8)]
          expected = df.loc[:, 8:14]
          assert_frame_equal(result, expected)
  
          # verify slice is view
          # and that we are setting a copy
          with pytest.raises(com.SettingWithCopyError):
              result[8] = 0.
  
          assert (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_iloc_duplicates(self):
  
          df = DataFrame(np.random.rand(3, 3), columns=list('ABC'),
                         index=list('aab'))
  
          result = df.iloc[0]
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result2 = df.ix[0]
          assert isinstance(result, Series)
          assert_almost_equal(result.values, df.values[0])
          assert_series_equal(result, result2)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = df.T.iloc[:, 0]
              result2 = df.T.ix[:, 0]
          assert isinstance(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']])
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              rs = df.iloc[0]
              xp = df.ix[0]
          assert_series_equal(rs, xp)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              rs = df.iloc[:, 0]
              xp = df.T.ix[0]
          assert_series_equal(rs, xp)
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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_loc_duplicates(self):
          # gh-17105
  
          # insert a duplicate element to the index
          trange = pd.date_range(start=pd.Timestamp(year=2017, month=1, day=1),
                                 end=pd.Timestamp(year=2017, month=1, day=5))
  
          trange = trange.insert(loc=5,
                                 item=pd.Timestamp(year=2017, month=1, day=5))
  
          df = pd.DataFrame(0, index=trange, columns=["A", "B"])
          bool_idx = np.array([False, False, False, False, False, True])
  
          # assignment
          df.loc[trange[bool_idx], "A"] = 6
  
          expected = pd.DataFrame({'A': [0, 0, 0, 0, 6, 6],
                                   'B': [0, 0, 0, 0, 0, 0]},
                                  index=trange)
          tm.assert_frame_equal(df, expected)
  
          # in-place
          df = pd.DataFrame(0, index=trange, columns=["A", "B"])
          df.loc[trange[bool_idx], "A"] += 6
          tm.assert_frame_equal(df, expected)
  
      @pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
      def test_iloc_sparse_propegate_fill_value(self):
          from pandas.core.sparse.api import SparseDataFrame
          df = SparseDataFrame({'A': [999, 1]}, default_fill_value=999)
          assert len(df['A'].sp_values) == len(df.iloc[:, 0].sp_values)
  
      def test_iat(self):
  
          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]
                  assert 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"])
          index = list(df.index)
          index[0] = ["a", "b"]
          df.index = index
  
          try:
              repr(df)
          except Exception as e:
              assert type(e) != UnboundLocalError
  
      @pytest.mark.parametrize("method,expected_values", [
          ("nearest", [0, 1, 1, 2]),
          ("pad", [np.nan, 0, 1, 1]),
          ("backfill", [0, 1, 2, 2])
      ])
      def test_reindex_methods(self, method, expected_values):
          df = pd.DataFrame({"x": list(range(5))})
          target = np.array([-0.1, 0.9, 1.1, 1.5])
  
          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_like(df, method=method, tolerance=[0, 0, 0, 0])
          assert_frame_equal(df, actual)
  
          actual = df.reindex(target, method=method, tolerance=1)
          assert_frame_equal(expected, actual)
          actual = df.reindex(target, method=method, tolerance=[1, 1, 1, 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)
  
      def test_reindex_methods_nearest_special(self):
          df = pd.DataFrame({"x": list(range(5))})
          target = np.array([-0.1, 0.9, 1.1, 1.5])
  
          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)
  
          expected = pd.DataFrame({"x": [0, np.nan, 1, np.nan]}, index=target)
          actual = df.reindex(target, method="nearest",
                              tolerance=[0.5, 0.01, 0.4, 0.1])
          assert_frame_equal(expected, actual)
  
      def test_reindex_frame_add_nat(self):
          rng = date_range('1/1/2000 00:00:00', periods=10, freq='10s')
          df = DataFrame({'A': np.random.randn(len(rng)), 'B': rng})
  
          result = df.reindex(range(15))
          assert np.issubdtype(result['B'].dtype, np.dtype('M8[ns]'))
  
          mask = com.isna(result)['B']
          assert mask[-5:].all()
          assert not mask[:-5].any()
  
      def test_set_dataframe_column_ns_dtype(self):
          x = DataFrame([datetime.now(), datetime.now()])
          assert x[0].dtype == np.dtype('M8[ns]')
  
      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
          msg = "index must be monotonic increasing or decreasing"
          with pytest.raises(ValueError, match=msg):
              df_rev.reindex(df.index, method='pad')
          with pytest.raises(ValueError, match=msg):
              df_rev.reindex(df.index, method='ffill')
          with pytest.raises(ValueError, match=msg):
              df_rev.reindex(df.index, method='bfill')
          with pytest.raises(ValueError, match=msg):
              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):
              def f(val):
                  return np.nonzero((df[level] == val).to_numpy())[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)
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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)
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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)
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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)
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              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_getitem_sparse_column(self):
          # https://github.com/pandas-dev/pandas/issues/23559
          data = pd.SparseArray([0, 1])
          df = pd.DataFrame({"A": data})
          expected = pd.Series(data, name="A")
          result = df['A']
          tm.assert_series_equal(result, expected)
  
          result = df.iloc[:, 0]
          tm.assert_series_equal(result, expected)
  
          result = df.loc[:, 'A']
          tm.assert_series_equal(result, expected)
  
      def test_setitem_with_sparse_value(self):
          # GH8131
          df = pd.DataFrame({'c_1': ['a', 'b', 'c'], 'n_1': [1., 2., 3.]})
          sp_array = pd.SparseArray([0, 0, 1])
          df['new_column'] = sp_array
          assert_series_equal(df['new_column'],
                              pd.Series(sp_array, name='new_column'),
                              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(pd.SparseArray([0, 0, 1]), index=[2, 1, 0])
          df['new_column'] = sp_series
          exp = pd.Series(pd.SparseArray([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')
          assert pd.Timestamp('2008-08-08') == df.loc[0, 'c']
          assert pd.Timestamp('2008-08-08') == df.loc[1, 'c']
          df.loc[2, 'c'] = date(2005, 5, 5)
          assert 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.loc[:, 'D'] = np.array([4 * one_hour] * 4, dtype='m8[ns]')
          df.loc[df.index[:3], 'E'] = np.array([5 * one_hour] * 3,
                                               dtype='m8[ns]')
          df['F'] = np.timedelta64('NaT')
          df.loc[df.index[:-1], 'F'] = np.array([6 * one_hour] * 3,
                                                dtype='m8[ns]')
          df.loc[df.index[-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)
  
      @pytest.mark.parametrize('idxer', ['var', ['var']])
      def test_setitem_datetimeindex_tz(self, idxer, tz_naive_fixture):
          # GH 11365
          tz = tz_naive_fixture
          idx = date_range(start='2015-07-12', periods=3, freq='H', tz=tz)
          expected = DataFrame(1.2, index=idx, columns=['var'])
          result = DataFrame(index=idx, columns=['var'])
          result.loc[:, idxer] = expected
          tm.assert_frame_equal(result, expected)
  
      def test_at_time_between_time_datetimeindex(self):
          index = date_range("2012-01-01", "2012-01-05", freq='30min')
          df = DataFrame(np.random.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.loc[akey]
          expected2 = df.iloc[ainds]
          assert_frame_equal(result, expected)
          assert_frame_equal(result, expected2)
          assert len(result) == 4
  
          result = df.between_time(bkey.start, bkey.stop)
          expected = df.loc[bkey]
          expected2 = df.iloc[binds]
          assert_frame_equal(result, expected)
          assert_frame_equal(result, expected2)
          assert len(result) == 12
  
          result = df.copy()
          result.loc[akey] = 0
          result = result.loc[akey]
          expected = df.loc[akey].copy()
          expected.loc[:] = 0
          assert_frame_equal(result, expected)
  
          result = df.copy()
          result.loc[akey] = 0
          result.loc[akey] = df.iloc[ainds]
          assert_frame_equal(result, df)
  
          result = df.copy()
          result.loc[bkey] = 0
          result = result.loc[bkey]
          expected = df.loc[bkey].copy()
          expected.loc[:] = 0
          assert_frame_equal(result, expected)
  
          result = df.copy()
          result.loc[bkey] = 0
          result.loc[bkey] = df.iloc[binds]
          assert_frame_equal(result, df)
  
      def test_xs(self):
          idx = self.frame.index[5]
          xs = self.frame.xs(idx)
          for item, value in xs.items():
              if np.isnan(value):
                  assert np.isnan(self.frame[item][idx])
              else:
                  assert 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')
          assert xs.dtype == np.object_
          assert xs['A'] == 1
          assert xs['B'] == '1'
  
          with pytest.raises(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
          assert (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(np.random.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=range(4), columns=range(5))
  
          dm.xs(2)[:] = 10
          assert (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"])
  
          with catch_warnings(record=True):
              simplefilter("ignore", FutureWarning)
              result = df.ix[IndexType("foo", "bar")]["A"]
          assert result == 1
  
          result = df.loc[IndexType("foo", "bar")]["A"]
          assert result == 1
  
      def test_boolean_indexing(self):
          idx = list(range(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 pytest.raises(ValueError, match='Item wrong length'):
              df1[df1.index[:-1] > 2] = -1
  
      def test_boolean_indexing_mixed(self):
          df = DataFrame({
              0: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan},
              1: {35: np.nan, 40: 0.32632316859446198, 43: np.nan,
                  49: 0.32632316859446198, 50: 0.39114724480578139},
              2: {35: np.nan, 40: np.nan, 43: 0.29012581014105987, 49: np.nan,
                  50: np.nan},
              3: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan},
              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'
          msg = "not supported between instances|unorderable types"
  
          with pytest.raises(TypeError, match=msg):
              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 df.items()))
  
          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:
                  assert (rs.dtypes == df.dtypes).all()
  
          # check getting
          for df in [default_frame, self.mixed_frame,
                     self.mixed_float, self.mixed_int]:
              if df is self.mixed_frame:
                  with pytest.raises(TypeError):
                      df > 0
                  continue
              cond = df > 0
              _check_get(df, cond)
  
          # upcasting case (GH # 2794)
          df = DataFrame({c: Series([1] * 3, dtype=c)
                          for c in ['float32', 'float64',
                                    'int32', 'int64']})
          df.iloc[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):
                  assert (rs.dtypes == df.dtypes).all()
  
          for df in [self.mixed_frame, self.mixed_float, self.mixed_int]:
              if df is self.mixed_frame:
                  with pytest.raises(TypeError):
                      df > 0
                  continue
  
              # 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, :]
          msg = "other must be the same shape as self when an ndarray"
          with pytest.raises(ValueError, match=msg):
              df.where(cond, err1)
  
          err2 = cond.iloc[:2, :].values
          other1 = _safe_add(df)
          msg = "Array conditional must be same shape as self"
          with pytest.raises(ValueError, match=msg):
              df.where(err2, other1)
  
          with pytest.raises(ValueError, match=msg):
              df.mask(True)
          with pytest.raises(ValueError, match=msg):
              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 df.dtypes.items():
                      if issubclass(v.type, np.integer) and not cond[k].all():
                          v = np.dtype('float64')
                      assert dfi[k].dtype == v
  
          for df in [default_frame, self.mixed_frame, self.mixed_float,
                     self.mixed_int]:
              if df is self.mixed_frame:
                  with pytest.raises(TypeError):
                      df > 0
                  continue
  
              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)
  
      @pytest.mark.parametrize("klass", [list, tuple, np.array])
      def test_where_array_like(self, klass):
          # see gh-15414
          df = DataFrame({"a": [1, 2, 3]})
          cond = [[False], [True], [True]]
          expected = DataFrame({"a": [np.nan, 2, 3]})
  
          result = df.where(klass(cond))
          assert_frame_equal(result, expected)
  
          df["b"] = 2
          expected["b"] = [2, np.nan, 2]
          cond = [[False, True], [True, False], [True, True]]
  
          result = df.where(klass(cond))
          assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("cond", [
          [[1], [0], [1]],
          Series([[2], [5], [7]]),
          DataFrame({"a": [2, 5, 7]}),
          [["True"], ["False"], ["True"]],
          [[Timestamp("2017-01-01")],
           [pd.NaT], [Timestamp("2017-01-02")]]
      ])
      def test_where_invalid_input_single(self, cond):
          # see gh-15414: only boolean arrays accepted
          df = DataFrame({"a": [1, 2, 3]})
          msg = "Boolean array expected for the condition"
  
          with pytest.raises(ValueError, match=msg):
              df.where(cond)
  
      @pytest.mark.parametrize("cond", [
          [[0, 1], [1, 0], [1, 1]],
          Series([[0, 2], [5, 0], [4, 7]]),
          [["False", "True"], ["True", "False"],
           ["True", "True"]],
          DataFrame({"a": [2, 5, 7], "b": [4, 8, 9]}),
          [[pd.NaT, Timestamp("2017-01-01")],
           [Timestamp("2017-01-02"), pd.NaT],
           [Timestamp("2017-01-03"), Timestamp("2017-01-03")]]
      ])
      def test_where_invalid_input_multiple(self, cond):
          # see gh-15414: only boolean arrays accepted
          df = DataFrame({"a": [1, 2, 3], "b": [2, 2, 2]})
          msg = "Boolean array expected for the condition"
  
          with pytest.raises(ValueError, match=msg):
              df.where(cond)
  
      def test_where_dataframe_col_match(self):
          df = DataFrame([[1, 2, 3], [4, 5, 6]])
          cond = DataFrame([[True, False, True], [False, False, True]])
  
          result = df.where(cond)
          expected = DataFrame([[1.0, np.nan, 3], [np.nan, np.nan, 6]])
          tm.assert_frame_equal(result, expected)
  
          # this *does* align, though has no matching columns
          cond.columns = ["a", "b", "c"]
          result = df.where(cond)
          expected = DataFrame(np.nan, index=df.index, columns=df.columns)
          tm.assert_frame_equal(result, expected)
  
      def test_where_ndframe_align(self):
          msg = "Array conditional must be same shape as self"
          df = DataFrame([[1, 2, 3], [4, 5, 6]])
  
          cond = [True]
          with pytest.raises(ValueError, match=msg):
              df.where(cond)
  
          expected = DataFrame([[1, 2, 3], [np.nan, np.nan, np.nan]])
  
          out = df.where(Series(cond))
          tm.assert_frame_equal(out, expected)
  
          cond = np.array([False, True, False, True])
          with pytest.raises(ValueError, match=msg):
              df.where(cond)
  
          expected = DataFrame([[np.nan, np.nan, np.nan], [4, 5, 6]])
  
          out = df.where(Series(cond))
          tm.assert_frame_equal(out, expected)
  
      def test_where_bug(self):
          # see 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)
  
      def test_where_bug_mixed(self, sint_dtype):
          # see gh-2793
          df = DataFrame({"a": np.array([1, 2, 3, 4], dtype=sint_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)
  
      def test_where_bug_transposition(self):
          # see gh-7506
          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.isna() | (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.isna() | (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)
          with pytest.raises(TypeError):
              df > stamp
  
          result = df[df.iloc[:, :-1] > stamp]
  
          expected = df.copy()
          expected.loc[[0, 1], 'A'] = np.nan
          expected.loc[:, 'C'] = 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}])
          msg = 'boolean setting on mixed-type'
  
          with pytest.raises(TypeError, match=msg):
              df.where(~isna(df), None, inplace=True)
  
      def test_where_empty_df_and_empty_cond_having_non_bool_dtypes(self):
          # see gh-21947
          df = pd.DataFrame(columns=["a"])
          cond = df.applymap(lambda x: x > 0)
  
          result = df.where(cond)
          tm.assert_frame_equal(result, df)
  
      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.notna(df), df.mean(), axis='columns')
          assert_frame_equal(result, expected)
  
          df.where(pd.notna(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.notna(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]])
          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)), dtype='int64')],
              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('int64')
          expected[3] = expected[3].astype('int64')
          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('int64')
          expected[3] = expected[3].astype('int64')
          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_where_tz_values(self, tz_naive_fixture):
          df1 = DataFrame(DatetimeIndex(['20150101', '20150102', '20150103'],
                                        tz=tz_naive_fixture),
                          columns=['date'])
          df2 = DataFrame(DatetimeIndex(['20150103', '20150104', '20150105'],
                                        tz=tz_naive_fixture),
                          columns=['date'])
          mask = DataFrame([True, True, False], columns=['date'])
          exp = DataFrame(DatetimeIndex(['20150101', '20150102', '20150105'],
                                        tz=tz_naive_fixture),
                          columns=['date'])
          result = df1.where(mask, df2)
          assert_frame_equal(exp, result)
  
      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))
  
          # see gh-21891
          df = DataFrame([1, 2])
          res = df.mask([[True], [False]])
  
          exp = DataFrame([np.nan, 2])
          tm.assert_frame_equal(res, exp)
  
      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([[np.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 pytest.raises(TypeError, match="is an invalid key"):
              str(dg[:, 0])
  
          index = Index(range(2), name='i')
          columns = MultiIndex(levels=[['x', 'y'], [0, 1]],
                               codes=[[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)
  
      def test_interval_index(self):
          # GH 19977
          index = pd.interval_range(start=0, periods=3)
          df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
                            index=index,
                            columns=['A', 'B', 'C'])
  
          expected = 1
          result = df.loc[0.5, 'A']
          assert_almost_equal(result, expected)
  
          index = pd.interval_range(start=0, periods=3, closed='both')
          df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
                            index=index,
                            columns=['A', 'B', 'C'])
  
          index_exp = pd.interval_range(start=0, periods=2,
                                        freq=1, closed='both')
          expected = pd.Series([1, 4], index=index_exp, name='A')
          result = df.loc[1, 'A']
          assert_series_equal(result, expected)
  
  
  class TestDataFrameIndexingDatetimeWithTZ(TestData):
  
      def setup_method(self, method):
          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]
          tm.assert_extension_array_equal(b1.values, b2.values)
          assert id(b1.values._data.base) != id(b2.values._data.base)
  
          # with nan
          df2 = df.copy()
          df2.iloc[1, 1] = pd.NaT
          df2.iloc[1, 2] = pd.NaT
          result = df2['B']
          assert_series_equal(notna(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()
          assert result['foo'].dtype, 'M8[ns, US/Eastern'
  
          df = 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)
  
      def test_scalar_assignment(self):
          # issue #19843
          df = pd.DataFrame(index=(0, 1, 2))
          df['now'] = pd.Timestamp('20130101', tz='UTC')
          expected = pd.DataFrame(
              {'now': pd.Timestamp('20130101', tz='UTC')}, index=[0, 1, 2])
          tm.assert_frame_equal(df, expected)
  
  
  class TestDataFrameIndexingUInt64(TestData):
  
      def setup_method(self, method):
          self.ir = Index(np.arange(3), dtype=np.uint64)
          self.idx = Index([2**63, 2**63 + 5, 2**63 + 10], name='foo')
  
          self.df = DataFrame({'A': self.idx, 'B': self.ir})
  
      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']
  
          # With NaN: because uint64 has no NaN element,
          # the column should be cast to object.
          df2 = df.copy()
          df2.iloc[1, 1] = pd.NaT
          df2.iloc[1, 2] = pd.NaT
          result = df2['B']
          assert_series_equal(notna(result), Series(
              [True, False, True], name='B'))
          assert_series_equal(df2.dtypes, Series([np.dtype('uint64'),
                                                  np.dtype('O'), np.dtype('O')],
                                                 index=['A', 'B', 'C']))
  
      def test_set_reset(self):
  
          idx = self.idx
  
          # set/reset
          df = DataFrame({'A': [0, 1, 2]}, index=idx)
          result = df.reset_index()
          assert result['foo'].dtype == np.dtype('uint64')
  
          df = 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)
  
  
  class TestDataFrameIndexingCategorical:
  
      def test_assignment(self):
          # assignment
          df = DataFrame({'value': np.array(
              np.random.randint(0, 10000, 100), dtype='int32')})
          labels = Categorical(["{0} - {1}".format(i, i + 499)
                                for i in range(0, 10000, 500)])
  
          df = df.sort_values(by=['value'], ascending=True)
          s = pd.cut(df.value, range(0, 10500, 500), right=False, labels=labels)
          d = s.values
          df['D'] = d
          str(df)
  
          result = df.dtypes
          expected = Series(
              [np.dtype('int32'), CategoricalDtype(categories=labels,
                                                   ordered=False)],
              index=['value', 'D'])
          tm.assert_series_equal(result, expected)
  
          df['E'] = s
          str(df)
  
          result = df.dtypes
          expected = Series([np.dtype('int32'),
                             CategoricalDtype(categories=labels, ordered=False),
                             CategoricalDtype(categories=labels, ordered=False)],
                            index=['value', 'D', 'E'])
          tm.assert_series_equal(result, expected)
  
          result1 = df['D']
          result2 = df['E']
          tm.assert_categorical_equal(result1._data._block.values, d)
  
          # sorting
          s.name = 'E'
          tm.assert_series_equal(result2.sort_index(), s.sort_index())
  
          cat = Categorical([1, 2, 3, 10], categories=[1, 2, 3, 4, 10])
          df = DataFrame(Series(cat))
  
      def test_assigning_ops(self):
          # systematically test the assigning operations:
          # for all slicing ops:
          #  for value in categories and value not in categories:
  
          #   - assign a single value -> exp_single_cats_value
  
          #   - assign a complete row (mixed values) -> exp_single_row
  
          # assign multiple rows (mixed values) (-> array) -> exp_multi_row
  
          # assign a part of a column with dtype == categorical ->
          # exp_parts_cats_col
  
          # assign a part of a column with dtype != categorical ->
          # exp_parts_cats_col
  
          cats = Categorical(["a", "a", "a", "a", "a", "a", "a"],
                             categories=["a", "b"])
          idx = Index(["h", "i", "j", "k", "l", "m", "n"])
          values = [1, 1, 1, 1, 1, 1, 1]
          orig = DataFrame({"cats": cats, "values": values}, index=idx)
  
          # the expected values
          # changed single row
          cats1 = Categorical(["a", "a", "b", "a", "a", "a", "a"],
                              categories=["a", "b"])
          idx1 = Index(["h", "i", "j", "k", "l", "m", "n"])
          values1 = [1, 1, 2, 1, 1, 1, 1]
          exp_single_row = DataFrame({"cats": cats1,
                                      "values": values1}, index=idx1)
  
          # changed multiple rows
          cats2 = Categorical(["a", "a", "b", "b", "a", "a", "a"],
                              categories=["a", "b"])
          idx2 = Index(["h", "i", "j", "k", "l", "m", "n"])
          values2 = [1, 1, 2, 2, 1, 1, 1]
          exp_multi_row = DataFrame({"cats": cats2,
                                     "values": values2}, index=idx2)
  
          # changed part of the cats column
          cats3 = Categorical(
              ["a", "a", "b", "b", "a", "a", "a"], categories=["a", "b"])
          idx3 = Index(["h", "i", "j", "k", "l", "m", "n"])
          values3 = [1, 1, 1, 1, 1, 1, 1]
          exp_parts_cats_col = DataFrame({"cats": cats3,
                                          "values": values3}, index=idx3)
  
          # changed single value in cats col
          cats4 = Categorical(
              ["a", "a", "b", "a", "a", "a", "a"], categories=["a", "b"])
          idx4 = Index(["h", "i", "j", "k", "l", "m", "n"])
          values4 = [1, 1, 1, 1, 1, 1, 1]
          exp_single_cats_value = DataFrame({"cats": cats4,
                                             "values": values4}, index=idx4)
  
          #  iloc
          # ###############
          #   - assign a single value -> exp_single_cats_value
          df = orig.copy()
          df.iloc[2, 0] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          df = orig.copy()
          df.iloc[df.index == "j", 0] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          #   - assign a single value not in the current categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.iloc[2, 0] = "c"
  
          #   - assign a complete row (mixed values) -> exp_single_row
          df = orig.copy()
          df.iloc[2, :] = ["b", 2]
          tm.assert_frame_equal(df, exp_single_row)
  
          #   - assign a complete row (mixed values) not in categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.iloc[2, :] = ["c", 2]
  
          #   - assign multiple rows (mixed values) -> exp_multi_row
          df = orig.copy()
          df.iloc[2:4, :] = [["b", 2], ["b", 2]]
          tm.assert_frame_equal(df, exp_multi_row)
  
          with pytest.raises(ValueError):
              df = orig.copy()
              df.iloc[2:4, :] = [["c", 2], ["c", 2]]
  
          # assign a part of a column with dtype == categorical ->
          # exp_parts_cats_col
          df = orig.copy()
          df.iloc[2:4, 0] = Categorical(["b", "b"], categories=["a", "b"])
          tm.assert_frame_equal(df, exp_parts_cats_col)
  
          with pytest.raises(ValueError):
              # different categories -> not sure if this should fail or pass
              df = orig.copy()
              df.iloc[2:4, 0] = Categorical(list('bb'), categories=list('abc'))
  
          with pytest.raises(ValueError):
              # different values
              df = orig.copy()
              df.iloc[2:4, 0] = Categorical(list('cc'), categories=list('abc'))
  
          # assign a part of a column with dtype != categorical ->
          # exp_parts_cats_col
          df = orig.copy()
          df.iloc[2:4, 0] = ["b", "b"]
          tm.assert_frame_equal(df, exp_parts_cats_col)
  
          with pytest.raises(ValueError):
              df.iloc[2:4, 0] = ["c", "c"]
  
          #  loc
          # ##############
          #   - assign a single value -> exp_single_cats_value
          df = orig.copy()
          df.loc["j", "cats"] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          df = orig.copy()
          df.loc[df.index == "j", "cats"] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          #   - assign a single value not in the current categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.loc["j", "cats"] = "c"
  
          #   - assign a complete row (mixed values) -> exp_single_row
          df = orig.copy()
          df.loc["j", :] = ["b", 2]
          tm.assert_frame_equal(df, exp_single_row)
  
          #   - assign a complete row (mixed values) not in categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.loc["j", :] = ["c", 2]
  
          #   - assign multiple rows (mixed values) -> exp_multi_row
          df = orig.copy()
          df.loc["j":"k", :] = [["b", 2], ["b", 2]]
          tm.assert_frame_equal(df, exp_multi_row)
  
          with pytest.raises(ValueError):
              df = orig.copy()
              df.loc["j":"k", :] = [["c", 2], ["c", 2]]
  
          # assign a part of a column with dtype == categorical ->
          # exp_parts_cats_col
          df = orig.copy()
          df.loc["j":"k", "cats"] = Categorical(
              ["b", "b"], categories=["a", "b"])
          tm.assert_frame_equal(df, exp_parts_cats_col)
  
          with pytest.raises(ValueError):
              # different categories -> not sure if this should fail or pass
              df = orig.copy()
              df.loc["j":"k", "cats"] = Categorical(
                  ["b", "b"], categories=["a", "b", "c"])
  
          with pytest.raises(ValueError):
              # different values
              df = orig.copy()
              df.loc["j":"k", "cats"] = Categorical(
                  ["c", "c"], categories=["a", "b", "c"])
  
          # assign a part of a column with dtype != categorical ->
          # exp_parts_cats_col
          df = orig.copy()
          df.loc["j":"k", "cats"] = ["b", "b"]
          tm.assert_frame_equal(df, exp_parts_cats_col)
  
          with pytest.raises(ValueError):
              df.loc["j":"k", "cats"] = ["c", "c"]
  
          #  loc
          # ##############
          #   - assign a single value -> exp_single_cats_value
          df = orig.copy()
          df.loc["j", df.columns[0]] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          df = orig.copy()
          df.loc[df.index == "j", df.columns[0]] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          #   - assign a single value not in the current categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.loc["j", df.columns[0]] = "c"
  
          #   - assign a complete row (mixed values) -> exp_single_row
          df = orig.copy()
          df.loc["j", :] = ["b", 2]
          tm.assert_frame_equal(df, exp_single_row)
  
          #   - assign a complete row (mixed values) not in categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.loc["j", :] = ["c", 2]
  
          #   - assign multiple rows (mixed values) -> exp_multi_row
          df = orig.copy()
          df.loc["j":"k", :] = [["b", 2], ["b", 2]]
          tm.assert_frame_equal(df, exp_multi_row)
  
          with pytest.raises(ValueError):
              df = orig.copy()
              df.loc["j":"k", :] = [["c", 2], ["c", 2]]
  
          # assign a part of a column with dtype == categorical ->
          # exp_parts_cats_col
          df = orig.copy()
          df.loc["j":"k", df.columns[0]] = Categorical(
              ["b", "b"], categories=["a", "b"])
          tm.assert_frame_equal(df, exp_parts_cats_col)
  
          with pytest.raises(ValueError):
              # different categories -> not sure if this should fail or pass
              df = orig.copy()
              df.loc["j":"k", df.columns[0]] = Categorical(
                  ["b", "b"], categories=["a", "b", "c"])
  
          with pytest.raises(ValueError):
              # different values
              df = orig.copy()
              df.loc["j":"k", df.columns[0]] = Categorical(
                  ["c", "c"], categories=["a", "b", "c"])
  
          # assign a part of a column with dtype != categorical ->
          # exp_parts_cats_col
          df = orig.copy()
          df.loc["j":"k", df.columns[0]] = ["b", "b"]
          tm.assert_frame_equal(df, exp_parts_cats_col)
  
          with pytest.raises(ValueError):
              df.loc["j":"k", df.columns[0]] = ["c", "c"]
  
          # iat
          df = orig.copy()
          df.iat[2, 0] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          #   - assign a single value not in the current categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.iat[2, 0] = "c"
  
          # at
          #   - assign a single value -> exp_single_cats_value
          df = orig.copy()
          df.at["j", "cats"] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          #   - assign a single value not in the current categories set
          with pytest.raises(ValueError):
              df = orig.copy()
              df.at["j", "cats"] = "c"
  
          # fancy indexing
          catsf = Categorical(["a", "a", "c", "c", "a", "a", "a"],
                              categories=["a", "b", "c"])
          idxf = Index(["h", "i", "j", "k", "l", "m", "n"])
          valuesf = [1, 1, 3, 3, 1, 1, 1]
          df = DataFrame({"cats": catsf, "values": valuesf}, index=idxf)
  
          exp_fancy = exp_multi_row.copy()
          exp_fancy["cats"].cat.set_categories(["a", "b", "c"], inplace=True)
  
          df[df["cats"] == "c"] = ["b", 2]
          # category c is kept in .categories
          tm.assert_frame_equal(df, exp_fancy)
  
          # set_value
          df = orig.copy()
          df.at["j", "cats"] = "b"
          tm.assert_frame_equal(df, exp_single_cats_value)
  
          with pytest.raises(ValueError):
              df = orig.copy()
              df.at["j", "cats"] = "c"
  
          # Assigning a Category to parts of a int/... column uses the values of
          # the Catgorical
          df = DataFrame({"a": [1, 1, 1, 1, 1], "b": list("aaaaa")})
          exp = DataFrame({"a": [1, "b", "b", 1, 1], "b": list("aabba")})
          df.loc[1:2, "a"] = Categorical(["b", "b"], categories=["a", "b"])
          df.loc[2:3, "b"] = Categorical(["b", "b"], categories=["a", "b"])
          tm.assert_frame_equal(df, exp)
  
      def test_functions_no_warnings(self):
          df = DataFrame({'value': np.random.randint(0, 100, 20)})
          labels = ["{0} - {1}".format(i, i + 9) for i in range(0, 100, 10)]
          with tm.assert_produces_warning(False):
              df['group'] = pd.cut(df.value, range(0, 105, 10), right=False,
                                   labels=labels)
  