code: """ test fancy indexing & misc """
  
  from datetime import datetime
  import re
  import weakref
  
  import numpy as np
  import pytest
  
  from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype
  
  import pandas as pd
  from pandas import DataFrame, Index, NaT, Series
  import pandas._testing as tm
  from pandas.core.indexing import maybe_numeric_slice, non_reducing_slice
  from pandas.tests.indexing.common import _mklbl
  
  from .test_floats import gen_obj
  
  
  def getitem(x):
      return x
  
  
  def setitem(x):
      return x
  
  
  def loc(x):
      return x.loc
  
  
  def iloc(x):
      return x.iloc
  
  
  # ------------------------------------------------------------------------
  # Indexing test cases
  
  
  class TestFancy:
      """ pure get/set item & fancy indexing """
  
      def test_setitem_ndarray_1d(self):
          # GH5508
  
          # len of indexer vs length of the 1d ndarray
          df = DataFrame(index=Index(np.arange(1, 11)))
          df["foo"] = np.zeros(10, dtype=np.float64)
          df["bar"] = np.zeros(10, dtype=complex)
  
          # invalid
          msg = "Must have equal len keys and value when setting with an iterable"
          with pytest.raises(ValueError, match=msg):
              df.loc[df.index[2:5], "bar"] = np.array([2.33j, 1.23 + 0.1j, 2.2, 1.0])
  
          # valid
          df.loc[df.index[2:6], "bar"] = np.array([2.33j, 1.23 + 0.1j, 2.2, 1.0])
  
          result = df.loc[df.index[2:6], "bar"]
          expected = Series(
              [2.33j, 1.23 + 0.1j, 2.2, 1.0], index=[3, 4, 5, 6], name="bar"
          )
          tm.assert_series_equal(result, expected)
  
          # dtype getting changed?
          df = DataFrame(index=Index(np.arange(1, 11)))
          df["foo"] = np.zeros(10, dtype=np.float64)
          df["bar"] = np.zeros(10, dtype=complex)
  
          msg = "Must have equal len keys and value when setting with an iterable"
          with pytest.raises(ValueError, match=msg):
              df[2:5] = np.arange(1, 4) * 1j
  
      @pytest.mark.parametrize("idxr", [getitem, loc, iloc])
      def test_getitem_ndarray_3d(self, index, frame_or_series, idxr):
          # GH 25567
          obj = gen_obj(frame_or_series, index)
          idxr = idxr(obj)
          nd3 = np.random.randint(5, size=(2, 2, 2))
  
          msg = "|".join(
              [
                  r"Buffer has wrong number of dimensions \(expected 1, got 3\)",
                  "Cannot index with multidimensional key",
                  r"Wrong number of dimensions. values.ndim != ndim \[3 != 1\]",
                  "Index data must be 1-dimensional",
                  "positional indexers are out-of-bounds",
                  "Indexing a MultiIndex with a multidimensional key is not implemented",
              ]
          )
  
          potential_errors = (IndexError, ValueError, NotImplementedError)
          with pytest.raises(potential_errors, match=msg):
              with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
                  idxr[nd3]
  
      @pytest.mark.parametrize("indexer", [setitem, loc, iloc])
      def test_setitem_ndarray_3d(self, index, frame_or_series, indexer):
          # GH 25567
          obj = gen_obj(frame_or_series, index)
          idxr = indexer(obj)
          nd3 = np.random.randint(5, size=(2, 2, 2))
  
          if indexer.__name__ == "iloc":
              err = ValueError
              msg = f"Cannot set values with ndim > {obj.ndim}"
          elif (
              isinstance(index, pd.IntervalIndex)
              and indexer.__name__ == "setitem"
              and obj.ndim == 1
          ):
              err = AttributeError
              msg = (
                  "'pandas._libs.interval.IntervalTree' object has no attribute 'get_loc'"
              )
          else:
              err = ValueError
              msg = r"Buffer has wrong number of dimensions \(expected 1, got 3\)|"
  
          with pytest.raises(err, match=msg):
              idxr[nd3] = 0
  
      def test_inf_upcast(self):
          # GH 16957
          # We should be able to use np.inf as a key
          # np.inf should cause an index to convert to float
  
          # Test with np.inf in rows
          df = DataFrame(columns=[0])
          df.loc[1] = 1
          df.loc[2] = 2
          df.loc[np.inf] = 3
  
          # make sure we can look up the value
          assert df.loc[np.inf, 0] == 3
  
          result = df.index
          expected = pd.Float64Index([1, 2, np.inf])
          tm.assert_index_equal(result, expected)
  
          # Test with np.inf in columns
          df = DataFrame()
          df.loc[0, 0] = 1
          df.loc[1, 1] = 2
          df.loc[0, np.inf] = 3
  
          result = df.columns
          expected = pd.Float64Index([0, 1, np.inf])
          tm.assert_index_equal(result, expected)
  
      def test_setitem_dtype_upcast(self):
  
          # GH3216
          df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])
          df["c"] = np.nan
          assert df["c"].dtype == np.float64
  
          df.loc[0, "c"] = "foo"
          expected = DataFrame(
              [{"a": 1, "b": np.nan, "c": "foo"}, {"a": 3, "b": 2, "c": np.nan}]
          )
          tm.assert_frame_equal(df, expected)
  
          # GH10280
          df = DataFrame(
              np.arange(6, dtype="int64").reshape(2, 3),
              index=list("ab"),
              columns=["foo", "bar", "baz"],
          )
  
          for val in [3.14, "wxyz"]:
              left = df.copy()
              left.loc["a", "bar"] = val
              right = DataFrame(
                  [[0, val, 2], [3, 4, 5]],
                  index=list("ab"),
                  columns=["foo", "bar", "baz"],
              )
  
              tm.assert_frame_equal(left, right)
              assert is_integer_dtype(left["foo"])
              assert is_integer_dtype(left["baz"])
  
          left = DataFrame(
              np.arange(6, dtype="int64").reshape(2, 3) / 10.0,
              index=list("ab"),
              columns=["foo", "bar", "baz"],
          )
          left.loc["a", "bar"] = "wxyz"
  
          right = DataFrame(
              [[0, "wxyz", 0.2], [0.3, 0.4, 0.5]],
              index=list("ab"),
              columns=["foo", "bar", "baz"],
          )
  
          tm.assert_frame_equal(left, right)
          assert is_float_dtype(left["foo"])
          assert is_float_dtype(left["baz"])
  
      def test_dups_fancy_indexing(self):
  
          # GH 3455
  
          df = tm.makeCustomDataframe(10, 3)
          df.columns = ["a", "a", "b"]
          result = df[["b", "a"]].columns
          expected = Index(["b", "a", "a"])
          tm.assert_index_equal(result, expected)
  
          # across dtypes
          df = DataFrame([[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], columns=list("aaaaaaa"))
          df.head()
          str(df)
          result = DataFrame([[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]])
          result.columns = list("aaaaaaa")
  
          # TODO(wesm): unused?
          df_v = df.iloc[:, 4]  # noqa
          res_v = result.iloc[:, 4]  # noqa
  
          tm.assert_frame_equal(df, result)
  
          # GH 3561, dups not in selected order
          df = DataFrame(
              {"test": [5, 7, 9, 11], "test1": [4.0, 5, 6, 7], "other": list("abcd")},
              index=["A", "A", "B", "C"],
          )
          rows = ["C", "B"]
          expected = DataFrame(
              {"test": [11, 9], "test1": [7.0, 6], "other": ["d", "c"]}, index=rows
          )
          result = df.loc[rows]
          tm.assert_frame_equal(result, expected)
  
          result = df.loc[Index(rows)]
          tm.assert_frame_equal(result, expected)
  
          rows = ["C", "B", "E"]
          with pytest.raises(KeyError, match="with any missing labels"):
              df.loc[rows]
  
          # see GH5553, make sure we use the right indexer
          rows = ["F", "G", "H", "C", "B", "E"]
          with pytest.raises(KeyError, match="with any missing labels"):
              df.loc[rows]
  
          # List containing only missing label
          dfnu = DataFrame(np.random.randn(5, 3), index=list("AABCD"))
          with pytest.raises(
              KeyError,
              match=re.escape(
                  "\"None of [Index(['E'], dtype='object')] are in the [index]\""
              ),
          ):
              dfnu.loc[["E"]]
  
          # ToDo: check_index_type can be True after GH 11497
  
          # GH 4619; duplicate indexer with missing label
          df = DataFrame({"A": [0, 1, 2]})
          with pytest.raises(KeyError, match="with any missing labels"):
              df.loc[[0, 8, 0]]
  
          df = DataFrame({"A": list("abc")})
          with pytest.raises(KeyError, match="with any missing labels"):
              df.loc[[0, 8, 0]]
  
          # non unique with non unique selector
          df = DataFrame({"test": [5, 7, 9, 11]}, index=["A", "A", "B", "C"])
          with pytest.raises(KeyError, match="with any missing labels"):
              df.loc[["A", "A", "E"]]
  
      def test_dups_fancy_indexing2(self):
          # GH 5835
          # dups on index and missing values
          df = DataFrame(np.random.randn(5, 5), columns=["A", "B", "B", "B", "A"])
  
          with pytest.raises(KeyError, match="with any missing labels"):
              df.loc[:, ["A", "B", "C"]]
  
          # GH 6504, multi-axis indexing
          df = DataFrame(
              np.random.randn(9, 2), index=[1, 1, 1, 2, 2, 2, 3, 3, 3], columns=["a", "b"]
          )
  
          expected = df.iloc[0:6]
          result = df.loc[[1, 2]]
          tm.assert_frame_equal(result, expected)
  
          expected = df
          result = df.loc[:, ["a", "b"]]
          tm.assert_frame_equal(result, expected)
  
          expected = df.iloc[0:6, :]
          result = df.loc[[1, 2], ["a", "b"]]
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("case", [getitem, loc])
      def test_duplicate_int_indexing(self, case):
          # GH 17347
          s = Series(range(3), index=[1, 1, 3])
          expected = s[1]
          result = case(s)[[1]]
          tm.assert_series_equal(result, expected)
  
      def test_indexing_mixed_frame_bug(self):
  
          # GH3492
          df = DataFrame(
              {"a": {1: "aaa", 2: "bbb", 3: "ccc"}, "b": {1: 111, 2: 222, 3: 333}}
          )
  
          # this works, new column is created correctly
          df["test"] = df["a"].apply(lambda x: "_" if x == "aaa" else x)
  
          # this does not work, ie column test is not changed
          idx = df["test"] == "_"
          temp = df.loc[idx, "a"].apply(lambda x: "-----" if x == "aaa" else x)
          df.loc[idx, "test"] = temp
          assert df.iloc[0, 2] == "-----"
  
      def test_multitype_list_index_access(self):
          # GH 10610
          df = DataFrame(np.random.random((10, 5)), columns=["a"] + [20, 21, 22, 23])
  
          with pytest.raises(KeyError, match=re.escape("'[-8, 26] not in index'")):
              df[[22, 26, -8]]
          assert df[21].shape[0] == df.shape[0]
  
      def test_set_index_nan(self):
  
          # GH 3586
          df = DataFrame(
              {
                  "PRuid": {
                      17: "nonQC",
                      18: "nonQC",
                      19: "nonQC",
                      20: "10",
                      21: "11",
                      22: "12",
                      23: "13",
                      24: "24",
                      25: "35",
                      26: "46",
                      27: "47",
                      28: "48",
                      29: "59",
                      30: "10",
                  },
                  "QC": {
                      17: 0.0,
                      18: 0.0,
                      19: 0.0,
                      20: np.nan,
                      21: np.nan,
                      22: np.nan,
                      23: np.nan,
                      24: 1.0,
                      25: np.nan,
                      26: np.nan,
                      27: np.nan,
                      28: np.nan,
                      29: np.nan,
                      30: np.nan,
                  },
                  "data": {
                      17: 7.9544899999999998,
                      18: 8.0142609999999994,
                      19: 7.8591520000000008,
                      20: 0.86140349999999999,
                      21: 0.87853110000000001,
                      22: 0.8427041999999999,
                      23: 0.78587700000000005,
                      24: 0.73062459999999996,
                      25: 0.81668560000000001,
                      26: 0.81927080000000008,
                      27: 0.80705009999999999,
                      28: 0.81440240000000008,
                      29: 0.80140849999999997,
                      30: 0.81307740000000006,
                  },
                  "year": {
                      17: 2006,
                      18: 2007,
                      19: 2008,
                      20: 1985,
                      21: 1985,
                      22: 1985,
                      23: 1985,
                      24: 1985,
                      25: 1985,
                      26: 1985,
                      27: 1985,
                      28: 1985,
                      29: 1985,
                      30: 1986,
                  },
              }
          ).reset_index()
  
          result = (
              df.set_index(["year", "PRuid", "QC"])
              .reset_index()
              .reindex(columns=df.columns)
          )
          tm.assert_frame_equal(result, df)
  
      def test_multi_assign(self):
  
          # GH 3626, an assignment of a sub-df to a df
          df = DataFrame(
              {
                  "FC": ["a", "b", "a", "b", "a", "b"],
                  "PF": [0, 0, 0, 0, 1, 1],
                  "col1": list(range(6)),
                  "col2": list(range(6, 12)),
              }
          )
          df.iloc[1, 0] = np.nan
          df2 = df.copy()
  
          mask = ~df2.FC.isna()
          cols = ["col1", "col2"]
  
          dft = df2 * 2
          dft.iloc[3, 3] = np.nan
  
          expected = DataFrame(
              {
                  "FC": ["a", np.nan, "a", "b", "a", "b"],
                  "PF": [0, 0, 0, 0, 1, 1],
                  "col1": Series([0, 1, 4, 6, 8, 10]),
                  "col2": [12, 7, 16, np.nan, 20, 22],
              }
          )
  
          # frame on rhs
          df2.loc[mask, cols] = dft.loc[mask, cols]
          tm.assert_frame_equal(df2, expected)
  
          df2.loc[mask, cols] = dft.loc[mask, cols]
          tm.assert_frame_equal(df2, expected)
  
          # with an ndarray on rhs
          # coerces to float64 because values has float64 dtype
          # GH 14001
          expected = DataFrame(
              {
                  "FC": ["a", np.nan, "a", "b", "a", "b"],
                  "PF": [0, 0, 0, 0, 1, 1],
                  "col1": [0.0, 1.0, 4.0, 6.0, 8.0, 10.0],
                  "col2": [12, 7, 16, np.nan, 20, 22],
              }
          )
          df2 = df.copy()
          df2.loc[mask, cols] = dft.loc[mask, cols].values
          tm.assert_frame_equal(df2, expected)
          df2.loc[mask, cols] = dft.loc[mask, cols].values
          tm.assert_frame_equal(df2, expected)
  
          # broadcasting on the rhs is required
          df = DataFrame(
              {
                  "A": [1, 2, 0, 0, 0],
                  "B": [0, 0, 0, 10, 11],
                  "C": [0, 0, 0, 10, 11],
                  "D": [3, 4, 5, 6, 7],
              }
          )
  
          expected = df.copy()
          mask = expected["A"] == 0
          for col in ["A", "B"]:
              expected.loc[mask, col] = df["D"]
  
          df.loc[df["A"] == 0, ["A", "B"]] = df["D"]
          tm.assert_frame_equal(df, expected)
  
      def test_setitem_list(self):
  
          # GH 6043
          # iloc with a list
          df = DataFrame(index=[0, 1], columns=[0])
          df.iloc[1, 0] = [1, 2, 3]
          df.iloc[1, 0] = [1, 2]
  
          result = DataFrame(index=[0, 1], columns=[0])
          result.iloc[1, 0] = [1, 2]
  
          tm.assert_frame_equal(result, df)
  
          # iloc with an object
          class TO:
              def __init__(self, value):
                  self.value = value
  
              def __str__(self) -> str:
                  return f"[{self.value}]"
  
              __repr__ = __str__
  
              def __eq__(self, other) -> bool:
                  return self.value == other.value
  
              def view(self):
                  return self
  
          df = DataFrame(index=[0, 1], columns=[0])
          df.iloc[1, 0] = TO(1)
          df.iloc[1, 0] = TO(2)
  
          result = DataFrame(index=[0, 1], columns=[0])
          result.iloc[1, 0] = TO(2)
  
          tm.assert_frame_equal(result, df)
  
          # remains object dtype even after setting it back
          df = DataFrame(index=[0, 1], columns=[0])
          df.iloc[1, 0] = TO(1)
          df.iloc[1, 0] = np.nan
          result = DataFrame(index=[0, 1], columns=[0])
  
          tm.assert_frame_equal(result, df)
  
      def test_string_slice(self):
          # GH 14424
          # string indexing against datetimelike with object
          # dtype should properly raises KeyError
          df = DataFrame([1], Index([pd.Timestamp("2011-01-01")], dtype=object))
          assert df.index._is_all_dates
          with pytest.raises(KeyError, match="'2011'"):
              df["2011"]
  
          with pytest.raises(KeyError, match="'2011'"):
              with tm.assert_produces_warning(FutureWarning):
                  # This does an is_all_dates check
                  df.loc["2011", 0]
  
          df = DataFrame()
          assert not df.index._is_all_dates
          with pytest.raises(KeyError, match="'2011'"):
              df["2011"]
  
          with pytest.raises(KeyError, match="'2011'"):
              df.loc["2011", 0]
  
      def test_astype_assignment(self):
  
          # GH4312 (iloc)
          df_orig = DataFrame(
              [["1", "2", "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG")
          )
  
          df = df_orig.copy()
          df.iloc[:, 0:2] = df.iloc[:, 0:2].astype(np.int64)
          expected = DataFrame(
              [[1, 2, "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG")
          )
          tm.assert_frame_equal(df, expected)
  
          df = df_orig.copy()
          df.iloc[:, 0:2] = df.iloc[:, 0:2]._convert(datetime=True, numeric=True)
          expected = DataFrame(
              [[1, 2, "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG")
          )
          tm.assert_frame_equal(df, expected)
  
          # GH5702 (loc)
          df = df_orig.copy()
          df.loc[:, "A"] = df.loc[:, "A"].astype(np.int64)
          expected = DataFrame(
              [[1, "2", "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG")
          )
          tm.assert_frame_equal(df, expected)
  
          df = df_orig.copy()
          df.loc[:, ["B", "C"]] = df.loc[:, ["B", "C"]].astype(np.int64)
          expected = DataFrame(
              [["1", 2, 3, ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG")
          )
          tm.assert_frame_equal(df, expected)
  
          # full replacements / no nans
          df = DataFrame({"A": [1.0, 2.0, 3.0, 4.0]})
          df.iloc[:, 0] = df["A"].astype(np.int64)
          expected = DataFrame({"A": [1, 2, 3, 4]})
          tm.assert_frame_equal(df, expected)
  
          df = DataFrame({"A": [1.0, 2.0, 3.0, 4.0]})
          df.loc[:, "A"] = df["A"].astype(np.int64)
          expected = DataFrame({"A": [1, 2, 3, 4]})
          tm.assert_frame_equal(df, expected)
  
      @pytest.mark.parametrize("indexer", [getitem, loc])
      def test_index_type_coercion(self, indexer):
  
          # GH 11836
          # if we have an index type and set it with something that looks
          # to numpy like the same, but is actually, not
          # (e.g. setting with a float or string '0')
          # then we need to coerce to object
  
          # integer indexes
          for s in [Series(range(5)), Series(range(5), index=range(1, 6))]:
  
              assert s.index.is_integer()
  
              s2 = s.copy()
              indexer(s2)[0.1] = 0
              assert s2.index.is_floating()
              assert indexer(s2)[0.1] == 0
  
              s2 = s.copy()
              indexer(s2)[0.0] = 0
              exp = s.index
              if 0 not in s:
                  exp = Index(s.index.tolist() + [0])
              tm.assert_index_equal(s2.index, exp)
  
              s2 = s.copy()
              indexer(s2)["0"] = 0
              assert s2.index.is_object()
  
          for s in [Series(range(5), index=np.arange(5.0))]:
  
              assert s.index.is_floating()
  
              s2 = s.copy()
              indexer(s2)[0.1] = 0
              assert s2.index.is_floating()
              assert indexer(s2)[0.1] == 0
  
              s2 = s.copy()
              indexer(s2)[0.0] = 0
              tm.assert_index_equal(s2.index, s.index)
  
              s2 = s.copy()
              indexer(s2)["0"] = 0
              assert s2.index.is_object()
  
  
  class TestMisc:
      def test_float_index_to_mixed(self):
          df = DataFrame({0.0: np.random.rand(10), 1.0: np.random.rand(10)})
          df["a"] = 10
          tm.assert_frame_equal(
              DataFrame({0.0: df[0.0], 1.0: df[1.0], "a": [10] * 10}), df
          )
  
      def test_float_index_non_scalar_assignment(self):
          df = DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]}, index=[1.0, 2.0, 3.0])
          df.loc[df.index[:2]] = 1
          expected = DataFrame({"a": [1, 1, 3], "b": [1, 1, 5]}, index=df.index)
          tm.assert_frame_equal(expected, df)
  
          df = DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]}, index=[1.0, 2.0, 3.0])
          df2 = df.copy()
          df.loc[df.index] = df.loc[df.index]
          tm.assert_frame_equal(df, df2)
  
      def test_float_index_at_iat(self):
          s = Series([1, 2, 3], index=[0.1, 0.2, 0.3])
          for el, item in s.items():
              assert s.at[el] == item
          for i in range(len(s)):
              assert s.iat[i] == i + 1
  
      def test_rhs_alignment(self):
          # GH8258, tests that both rows & columns are aligned to what is
          # assigned to. covers both uniform data-type & multi-type cases
          def run_tests(df, rhs, right_loc, right_iloc):
              # label, index, slice
              lbl_one, idx_one, slice_one = list("bcd"), [1, 2, 3], slice(1, 4)
              lbl_two, idx_two, slice_two = ["joe", "jolie"], [1, 2], slice(1, 3)
  
              left = df.copy()
              left.loc[lbl_one, lbl_two] = rhs
              tm.assert_frame_equal(left, right_loc)
  
              left = df.copy()
              left.iloc[idx_one, idx_two] = rhs
              tm.assert_frame_equal(left, right_iloc)
  
              left = df.copy()
              left.iloc[slice_one, slice_two] = rhs
              tm.assert_frame_equal(left, right_iloc)
  
          xs = np.arange(20).reshape(5, 4)
          cols = ["jim", "joe", "jolie", "joline"]
          df = DataFrame(xs, columns=cols, index=list("abcde"), dtype="int64")
  
          # right hand side; permute the indices and multiplpy by -2
          rhs = -2 * df.iloc[3:0:-1, 2:0:-1]
  
          # expected `right` result; just multiply by -2
          right_iloc = df.copy()
          right_iloc["joe"] = [1, 14, 10, 6, 17]
          right_iloc["jolie"] = [2, 13, 9, 5, 18]
          right_iloc.iloc[1:4, 1:3] *= -2
          right_loc = df.copy()
          right_loc.iloc[1:4, 1:3] *= -2
  
          # run tests with uniform dtypes
          run_tests(df, rhs, right_loc, right_iloc)
  
          # make frames multi-type & re-run tests
          for frame in [df, rhs, right_loc, right_iloc]:
              frame["joe"] = frame["joe"].astype("float64")
              frame["jolie"] = frame["jolie"].map("@{}".format)
          right_iloc["joe"] = [1.0, "@-28", "@-20", "@-12", 17.0]
          right_iloc["jolie"] = ["@2", -26.0, -18.0, -10.0, "@18"]
          run_tests(df, rhs, right_loc, right_iloc)
  
      def test_str_label_slicing_with_negative_step(self):
          SLC = pd.IndexSlice
  
          def assert_slices_equivalent(l_slc, i_slc):
              tm.assert_series_equal(s.loc[l_slc], s.iloc[i_slc])
  
              if not idx.is_integer:
                  # For integer indices, .loc and plain getitem are position-based.
                  tm.assert_series_equal(s[l_slc], s.iloc[i_slc])
                  tm.assert_series_equal(s.loc[l_slc], s.iloc[i_slc])
  
          for idx in [_mklbl("A", 20), np.arange(20) + 100, np.linspace(100, 150, 20)]:
              idx = Index(idx)
              s = Series(np.arange(20), index=idx)
              assert_slices_equivalent(SLC[idx[9] :: -1], SLC[9::-1])
              assert_slices_equivalent(SLC[: idx[9] : -1], SLC[:8:-1])
              assert_slices_equivalent(SLC[idx[13] : idx[9] : -1], SLC[13:8:-1])
              assert_slices_equivalent(SLC[idx[9] : idx[13] : -1], SLC[:0])
  
      def test_slice_with_zero_step_raises(self):
          s = Series(np.arange(20), index=_mklbl("A", 20))
          with pytest.raises(ValueError, match="slice step cannot be zero"):
              s[::0]
          with pytest.raises(ValueError, match="slice step cannot be zero"):
              s.loc[::0]
  
      def test_indexing_assignment_dict_already_exists(self):
          df = DataFrame({"x": [1, 2, 6], "y": [2, 2, 8], "z": [-5, 0, 5]}).set_index("z")
          expected = df.copy()
          rhs = {"x": 9, "y": 99}
          df.loc[5] = rhs
          expected.loc[5] = [9, 99]
          tm.assert_frame_equal(df, expected)
  
      def test_indexing_dtypes_on_empty(self):
          # Check that .iloc returns correct dtypes GH9983
          df = DataFrame({"a": [1, 2, 3], "b": ["b", "b2", "b3"]})
          df2 = df.iloc[[], :]
  
          assert df2.loc[:, "a"].dtype == np.int64
          tm.assert_series_equal(df2.loc[:, "a"], df2.iloc[:, 0])
  
      @pytest.mark.parametrize("size", [5, 999999, 1000000])
      def test_range_in_series_indexing(self, size):
          # range can cause an indexing error
          # GH 11652
          s = Series(index=range(size), dtype=np.float64)
          s.loc[range(1)] = 42
          tm.assert_series_equal(s.loc[range(1)], Series(42.0, index=[0]))
  
          s.loc[range(2)] = 43
          tm.assert_series_equal(s.loc[range(2)], Series(43.0, index=[0, 1]))
  
      @pytest.mark.parametrize(
          "slc",
          [
              pd.IndexSlice[:, :],
              pd.IndexSlice[:, 1],
              pd.IndexSlice[1, :],
              pd.IndexSlice[[1], [1]],
              pd.IndexSlice[1, [1]],
              pd.IndexSlice[[1], 1],
              pd.IndexSlice[1],
              pd.IndexSlice[1, 1],
              slice(None, None, None),
              [0, 1],
              np.array([0, 1]),
              Series([0, 1]),
          ],
      )
      def test_non_reducing_slice(self, slc):
          df = DataFrame([[0, 1], [2, 3]])
  
          tslice_ = non_reducing_slice(slc)
          assert isinstance(df.loc[tslice_], DataFrame)
  
      def test_list_slice(self):
          # like dataframe getitem
          slices = [["A"], Series(["A"]), np.array(["A"])]
          df = DataFrame({"A": [1, 2], "B": [3, 4]}, index=["A", "B"])
          expected = pd.IndexSlice[:, ["A"]]
          for subset in slices:
              result = non_reducing_slice(subset)
              tm.assert_frame_equal(df.loc[result], df.loc[expected])
  
      def test_maybe_numeric_slice(self):
          df = DataFrame({"A": [1, 2], "B": ["c", "d"], "C": [True, False]})
          result = maybe_numeric_slice(df, slice_=None)
          expected = pd.IndexSlice[:, ["A"]]
          assert result == expected
  
          result = maybe_numeric_slice(df, None, include_bool=True)
          expected = pd.IndexSlice[:, ["A", "C"]]
          assert all(result[1] == expected[1])
          result = maybe_numeric_slice(df, [1])
          expected = [1]
          assert result == expected
  
      def test_partial_boolean_frame_indexing(self):
          # GH 17170
          df = DataFrame(
              np.arange(9.0).reshape(3, 3), index=list("abc"), columns=list("ABC")
          )
          index_df = DataFrame(1, index=list("ab"), columns=list("AB"))
          result = df[index_df.notnull()]
          expected = DataFrame(
              np.array([[0.0, 1.0, np.nan], [3.0, 4.0, np.nan], [np.nan] * 3]),
              index=list("abc"),
              columns=list("ABC"),
          )
          tm.assert_frame_equal(result, expected)
  
      def test_no_reference_cycle(self):
          df = DataFrame({"a": [0, 1], "b": [2, 3]})
          for name in ("loc", "iloc", "at", "iat"):
              getattr(df, name)
          wr = weakref.ref(df)
          del df
          assert wr() is None
  
      def test_label_indexing_on_nan(self):
          # GH 32431
          df = Series([1, "{1,2}", 1, None])
          vc = df.value_counts(dropna=False)
          result1 = vc.loc[np.nan]
          result2 = vc[np.nan]
  
          expected = 1
          assert result1 == expected
          assert result2 == expected
  
  
  class TestSeriesNoneCoercion:
      EXPECTED_RESULTS = [
          # For numeric series, we should coerce to NaN.
          ([1, 2, 3], [np.nan, 2, 3]),
          ([1.0, 2.0, 3.0], [np.nan, 2.0, 3.0]),
          # For datetime series, we should coerce to NaT.
          (
              [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
              [NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)],
          ),
          # For objects, we should preserve the None value.
          (["foo", "bar", "baz"], [None, "bar", "baz"]),
      ]
  
      @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
      def test_coercion_with_setitem(self, start_data, expected_result):
          start_series = Series(start_data)
          start_series[0] = None
  
          expected_series = Series(expected_result)
          tm.assert_series_equal(start_series, expected_series)
  
      @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
      def test_coercion_with_loc_setitem(self, start_data, expected_result):
          start_series = Series(start_data)
          start_series.loc[0] = None
  
          expected_series = Series(expected_result)
          tm.assert_series_equal(start_series, expected_series)
  
      @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
      def test_coercion_with_setitem_and_series(self, start_data, expected_result):
          start_series = Series(start_data)
          start_series[start_series == start_series[0]] = None
  
          expected_series = Series(expected_result)
          tm.assert_series_equal(start_series, expected_series)
  
      @pytest.mark.parametrize("start_data,expected_result", EXPECTED_RESULTS)
      def test_coercion_with_loc_and_series(self, start_data, expected_result):
          start_series = Series(start_data)
          start_series.loc[start_series == start_series[0]] = None
  
          expected_series = Series(expected_result)
          tm.assert_series_equal(start_series, expected_series)
  
  
  class TestDataframeNoneCoercion:
      EXPECTED_SINGLE_ROW_RESULTS = [
          # For numeric series, we should coerce to NaN.
          ([1, 2, 3], [np.nan, 2, 3]),
          ([1.0, 2.0, 3.0], [np.nan, 2.0, 3.0]),
          # For datetime series, we should coerce to NaT.
          (
              [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
              [NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)],
          ),
          # For objects, we should preserve the None value.
          (["foo", "bar", "baz"], [None, "bar", "baz"]),
      ]
  
      @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS)
      def test_coercion_with_loc(self, expected):
          start_data, expected_result = expected
  
          start_dataframe = DataFrame({"foo": start_data})
          start_dataframe.loc[0, ["foo"]] = None
  
          expected_dataframe = DataFrame({"foo": expected_result})
          tm.assert_frame_equal(start_dataframe, expected_dataframe)
  
      @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS)
      def test_coercion_with_setitem_and_dataframe(self, expected):
          start_data, expected_result = expected
  
          start_dataframe = DataFrame({"foo": start_data})
          start_dataframe[start_dataframe["foo"] == start_dataframe["foo"][0]] = None
  
          expected_dataframe = DataFrame({"foo": expected_result})
          tm.assert_frame_equal(start_dataframe, expected_dataframe)
  
      @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS)
      def test_none_coercion_loc_and_dataframe(self, expected):
          start_data, expected_result = expected
  
          start_dataframe = DataFrame({"foo": start_data})
          start_dataframe.loc[start_dataframe["foo"] == start_dataframe["foo"][0]] = None
  
          expected_dataframe = DataFrame({"foo": expected_result})
          tm.assert_frame_equal(start_dataframe, expected_dataframe)
  
      def test_none_coercion_mixed_dtypes(self):
          start_dataframe = DataFrame(
              {
                  "a": [1, 2, 3],
                  "b": [1.0, 2.0, 3.0],
                  "c": [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
                  "d": ["a", "b", "c"],
              }
          )
          start_dataframe.iloc[0] = None
  
          exp = DataFrame(
              {
                  "a": [np.nan, 2, 3],
                  "b": [np.nan, 2.0, 3.0],
                  "c": [NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)],
                  "d": [None, "b", "c"],
              }
          )
          tm.assert_frame_equal(start_dataframe, exp)
  
  
  def test_extension_array_cross_section():
      # A cross-section of a homogeneous EA should be an EA
      df = DataFrame(
          {
              "A": pd.core.arrays.integer_array([1, 2]),
              "B": pd.core.arrays.integer_array([3, 4]),
          },
          index=["a", "b"],
      )
      expected = Series(pd.core.arrays.integer_array([1, 3]), index=["A", "B"], name="a")
      result = df.loc["a"]
      tm.assert_series_equal(result, expected)
  
      result = df.iloc[0]
      tm.assert_series_equal(result, expected)
  
  
  def test_extension_array_cross_section_converts():
      # all numeric columns -> numeric series
      df = DataFrame(
          {"A": pd.array([1, 2], dtype="Int64"), "B": np.array([1, 2])}, index=["a", "b"]
      )
      result = df.loc["a"]
      expected = Series([1, 1], dtype="Int64", index=["A", "B"], name="a")
      tm.assert_series_equal(result, expected)
  
      result = df.iloc[0]
      tm.assert_series_equal(result, expected)
  
      # mixed columns -> object series
      df = DataFrame(
          {"A": pd.array([1, 2], dtype="Int64"), "B": np.array(["a", "b"])},
          index=["a", "b"],
      )
      result = df.loc["a"]
      expected = Series([1, "a"], dtype=object, index=["A", "B"], name="a")
      tm.assert_series_equal(result, expected)
  
      result = df.iloc[0]
      tm.assert_series_equal(result, expected)
  
  
  def test_setitem_with_bool_mask_and_values_matching_n_trues_in_length():
      # GH 30567
      ser = Series([None] * 10)
      mask = [False] * 3 + [True] * 5 + [False] * 2
      ser[mask] = range(5)
      result = ser
      expected = Series([None] * 3 + list(range(5)) + [None] * 2).astype("object")
      tm.assert_series_equal(result, expected)
  
  
  def test_missing_labels_inside_loc_matched_in_error_message():
      # GH34272
      s = Series({"a": 1, "b": 2, "c": 3})
      error_message_regex = "missing_0.*missing_1.*missing_2"
      with pytest.raises(KeyError, match=error_message_regex):
          s.loc[["a", "b", "missing_0", "c", "missing_1", "missing_2"]]
  
  
  def test_many_missing_labels_inside_loc_error_message_limited():
      # GH34272
      n = 10000
      missing_labels = [f"missing_{label}" for label in range(n)]
      s = Series({"a": 1, "b": 2, "c": 3})
      # regex checks labels between 4 and 9995 are replaced with ellipses
      error_message_regex = "missing_4.*\\.\\.\\..*missing_9995"
      with pytest.raises(KeyError, match=error_message_regex):
          s.loc[["a", "c"] + missing_labels]
  
  
  def test_long_text_missing_labels_inside_loc_error_message_limited():
      # GH34272
      s = Series({"a": 1, "b": 2, "c": 3})
      missing_labels = [f"long_missing_label_text_{i}" * 5 for i in range(3)]
      # regex checks for very long labels there are new lines between each
      error_message_regex = "long_missing_label_text_0.*\\\\n.*long_missing_label_text_1"
      with pytest.raises(KeyError, match=error_message_regex):
          s.loc[["a", "c"] + missing_labels]
  
  
  def test_setitem_categorical():
      # https://github.com/pandas-dev/pandas/issues/35369
      df = DataFrame({"h": Series(list("mn")).astype("category")})
      df.h = df.h.cat.reorder_categories(["n", "m"])
      expected = DataFrame(
          {"h": pd.Categorical(["m", "n"]).reorder_categories(["n", "m"])}
      )
      tm.assert_frame_equal(df, expected)
  
apis: pandas.Series pandas.array pandas._testing.assert_frame_equal pandas.Float64Index pandas.core.dtypes.common.is_float_dtype pandas.Categorical pandas._testing.assert_series_equal pandas.core.indexing.maybe_numeric_slice pandas._testing.assert_produces_warning numpy.random.rand numpy.linspace pandas.Timestamp numpy.zeros pandas.core.arrays.integer_array numpy.arange pandas.tests.indexing.common._mklbl pandas.core.indexing.non_reducing_slice pandas.Index pandas._testing.makeCustomDataframe pandas.DataFrame numpy.random.randn numpy.random.random pandas._testing.assert_index_equal numpy.array numpy.random.randint pandas.core.dtypes.common.is_integer_dtype