text: """ test get/set & misc """
  
  from datetime import timedelta
  
  import numpy as np
  import pytest
  
  from pandas.core.dtypes.common import is_scalar
  
  import pandas as pd
  from pandas import (
      Categorical,
      DataFrame,
      IndexSlice,
      MultiIndex,
      Series,
      Timedelta,
      Timestamp,
      date_range,
      period_range,
      timedelta_range,
  )
  import pandas._testing as tm
  
  from pandas.tseries.offsets import BDay
  
  
  def test_basic_indexing():
      s = Series(np.random.randn(5), index=["a", "b", "a", "a", "b"])
  
      msg = "index 5 is out of bounds for axis 0 with size 5"
      with pytest.raises(IndexError, match=msg):
          s[5]
      with pytest.raises(IndexError, match=msg):
          s[5] = 0
  
      with pytest.raises(KeyError, match=r"^'c'$"):
          s["c"]
  
      s = s.sort_index()
  
      with pytest.raises(IndexError, match=msg):
          s[5]
      msg = r"index 5 is out of bounds for axis (0|1) with size 5|^5$"
      with pytest.raises(IndexError, match=msg):
          s[5] = 0
  
  
  def test_basic_getitem_with_labels(datetime_series):
      indices = datetime_series.index[[5, 10, 15]]
  
      result = datetime_series[indices]
      expected = datetime_series.reindex(indices)
      tm.assert_series_equal(result, expected)
  
      result = datetime_series[indices[0] : indices[2]]
      expected = datetime_series.loc[indices[0] : indices[2]]
      tm.assert_series_equal(result, expected)
  
      # integer indexes, be careful
      s = Series(np.random.randn(10), index=list(range(0, 20, 2)))
      inds = [0, 2, 5, 7, 8]
      arr_inds = np.array([0, 2, 5, 7, 8])
      with pytest.raises(KeyError, match="with any missing labels"):
          s[inds]
  
      with pytest.raises(KeyError, match="with any missing labels"):
          s[arr_inds]
  
      # GH12089
      # with tz for values
      s = Series(
          pd.date_range("2011-01-01", periods=3, tz="US/Eastern"), index=["a", "b", "c"]
      )
      expected = Timestamp("2011-01-01", tz="US/Eastern")
      result = s.loc["a"]
      assert result == expected
      result = s.iloc[0]
      assert result == expected
      result = s["a"]
      assert result == expected
  
  
  def test_getitem_setitem_ellipsis():
      s = Series(np.random.randn(10))
  
      np.fix(s)
  
      result = s[...]
      tm.assert_series_equal(result, s)
  
      s[...] = 5
      assert (result == 5).all()
  
  
  def test_getitem_get(datetime_series, string_series, object_series):
      idx1 = string_series.index[5]
      idx2 = object_series.index[5]
  
      assert string_series[idx1] == string_series.get(idx1)
      assert object_series[idx2] == object_series.get(idx2)
  
      assert string_series[idx1] == string_series[5]
      assert object_series[idx2] == object_series[5]
  
      assert string_series.get(-1) == string_series.get(string_series.index[-1])
      assert string_series[5] == string_series.get(string_series.index[5])
  
      # missing
      d = datetime_series.index[0] - BDay()
      msg = r"Timestamp\('1999-12-31 00:00:00', freq='B'\)"
      with pytest.raises(KeyError, match=msg):
          datetime_series[d]
  
      # None
      # GH 5652
      s1 = Series(dtype=object)
      s2 = Series(dtype=object, index=list("abc"))
      for s in [s1, s2]:
          result = s.get(None)
          assert result is None
  
  
  def test_getitem_fancy(string_series, object_series):
      slice1 = string_series[[1, 2, 3]]
      slice2 = object_series[[1, 2, 3]]
      assert string_series.index[2] == slice1.index[1]
      assert object_series.index[2] == slice2.index[1]
      assert string_series[2] == slice1[1]
      assert object_series[2] == slice2[1]
  
  
  def test_type_promotion():
      # GH12599
      s = Series(dtype=object)
      s["a"] = Timestamp("2016-01-01")
      s["b"] = 3.0
      s["c"] = "foo"
      expected = Series([Timestamp("2016-01-01"), 3.0, "foo"], index=["a", "b", "c"])
      tm.assert_series_equal(s, expected)
  
  
  @pytest.mark.parametrize(
      "result_1, duplicate_item, expected_1",
      [
          [
              Series({1: 12, 2: [1, 2, 2, 3]}),
              Series({1: 313}),
              Series({1: 12}, dtype=object),
          ],
          [
              Series({1: [1, 2, 3], 2: [1, 2, 2, 3]}),
              Series({1: [1, 2, 3]}),
              Series({1: [1, 2, 3]}),
          ],
      ],
  )
  def test_getitem_with_duplicates_indices(result_1, duplicate_item, expected_1):
      # GH 17610
      result = result_1.append(duplicate_item)
      expected = expected_1.append(duplicate_item)
      tm.assert_series_equal(result[1], expected)
      assert result[2] == result_1[2]
  
  
  def test_getitem_setitem_integers():
      # caused bug without test
      s = Series([1, 2, 3], ["a", "b", "c"])
  
      assert s.iloc[0] == s["a"]
      s.iloc[0] = 5
      tm.assert_almost_equal(s["a"], 5)
  
  
  def test_getitem_box_float64(datetime_series):
      value = datetime_series[5]
      assert isinstance(value, np.float64)
  
  
  def test_series_box_timestamp():
      rng = pd.date_range("20090415", "20090519", freq="B")
      ser = Series(rng)
  
      assert isinstance(ser[5], Timestamp)
  
      rng = pd.date_range("20090415", "20090519", freq="B")
      ser = Series(rng, index=rng)
      assert isinstance(ser[5], Timestamp)
  
      assert isinstance(ser.iat[5], Timestamp)
  
  
  def test_series_box_timedelta():
      rng = pd.timedelta_range("1 day 1 s", periods=5, freq="h")
      ser = Series(rng)
      assert isinstance(ser[0], Timedelta)
      assert isinstance(ser.at[1], Timedelta)
      assert isinstance(ser.iat[2], Timedelta)
      assert isinstance(ser.loc[3], Timedelta)
      assert isinstance(ser.iloc[4], Timedelta)
  
  
  def test_getitem_ambiguous_keyerror():
      s = Series(range(10), index=list(range(0, 20, 2)))
      with pytest.raises(KeyError, match=r"^1$"):
          s[1]
      with pytest.raises(KeyError, match=r"^1$"):
          s.loc[1]
  
  
  def test_getitem_unordered_dup():
      obj = Series(range(5), index=["c", "a", "a", "b", "b"])
      assert is_scalar(obj["c"])
      assert obj["c"] == 0
  
  
  def test_getitem_dups_with_missing():
      # breaks reindex, so need to use .loc internally
      # GH 4246
      s = Series([1, 2, 3, 4], ["foo", "bar", "foo", "bah"])
      with pytest.raises(KeyError, match="with any missing labels"):
          s.loc[["foo", "bar", "bah", "bam"]]
  
      with pytest.raises(KeyError, match="with any missing labels"):
          s[["foo", "bar", "bah", "bam"]]
  
  
  def test_getitem_dups():
      s = Series(range(5), index=["A", "A", "B", "C", "C"], dtype=np.int64)
      expected = Series([3, 4], index=["C", "C"], dtype=np.int64)
      result = s["C"]
      tm.assert_series_equal(result, expected)
  
  
  def test_setitem_ambiguous_keyerror():
      s = Series(range(10), index=list(range(0, 20, 2)))
  
      # equivalent of an append
      s2 = s.copy()
      s2[1] = 5
      expected = s.append(Series([5], index=[1]))
      tm.assert_series_equal(s2, expected)
  
      s2 = s.copy()
      s2.loc[1] = 5
      expected = s.append(Series([5], index=[1]))
      tm.assert_series_equal(s2, expected)
  
  
  def test_setitem(datetime_series, string_series):
      datetime_series[datetime_series.index[5]] = np.NaN
      datetime_series[[1, 2, 17]] = np.NaN
      datetime_series[6] = np.NaN
      assert np.isnan(datetime_series[6])
      assert np.isnan(datetime_series[2])
      datetime_series[np.isnan(datetime_series)] = 5
      assert not np.isnan(datetime_series[2])
  
      # caught this bug when writing tests
      series = Series(tm.makeIntIndex(20).astype(float), index=tm.makeIntIndex(20))
  
      series[::2] = 0
      assert (series[::2] == 0).all()
  
      # set item that's not contained
      s = string_series.copy()
      s["foobar"] = 1
  
      app = Series([1], index=["foobar"], name="series")
      expected = string_series.append(app)
      tm.assert_series_equal(s, expected)
  
  
  def test_setitem_dtypes():
      # change dtypes
      # GH 4463
      expected = Series([np.nan, 2, 3])
  
      s = Series([1, 2, 3])
      s.iloc[0] = np.nan
      tm.assert_series_equal(s, expected)
  
      s = Series([1, 2, 3])
      s.loc[0] = np.nan
      tm.assert_series_equal(s, expected)
  
      s = Series([1, 2, 3])
      s[0] = np.nan
      tm.assert_series_equal(s, expected)
  
      s = Series([False])
      s.loc[0] = np.nan
      tm.assert_series_equal(s, Series([np.nan]))
  
      s = Series([False, True])
      s.loc[0] = np.nan
      tm.assert_series_equal(s, Series([np.nan, 1.0]))
  
  
  def test_setslice(datetime_series):
      sl = datetime_series[5:20]
      assert len(sl) == len(sl.index)
      assert sl.index.is_unique is True
  
  
  def test_loc_setitem_2d_to_1d_raises():
      x = np.random.randn(2, 2)
      y = Series(range(2))
  
      msg = "|".join(
          [
              r"shape mismatch: value array of shape \(2,2\) could not be "
              r"broadcast to indexing result of shape \(2,\)",
              r"cannot reshape array of size 4 into shape \(2,\)",
          ]
      )
      with pytest.raises(ValueError, match=msg):
          y.loc[range(2)] = x
  
      msg = r"could not broadcast input array from shape \(2,2\) into shape \(2,?\)"
      with pytest.raises(ValueError, match=msg):
          y.loc[:] = x
  
  
  # FutureWarning from NumPy about [slice(None, 5).
  @pytest.mark.filterwarnings("ignore:Using a non-tuple:FutureWarning")
  def test_basic_getitem_setitem_corner(datetime_series):
      # invalid tuples, e.g. td.ts[:, None] vs. td.ts[:, 2]
      msg = "key of type tuple not found and not a MultiIndex"
      with pytest.raises(KeyError, match=msg):
          datetime_series[:, 2]
      with pytest.raises(KeyError, match=msg):
          datetime_series[:, 2] = 2
  
      # weird lists. [slice(0, 5)] will work but not two slices
      with tm.assert_produces_warning(FutureWarning):
          # GH#31299
          result = datetime_series[[slice(None, 5)]]
      expected = datetime_series[:5]
      tm.assert_series_equal(result, expected)
  
      # OK
      msg = r"unhashable type(: 'slice')?"
      with pytest.raises(TypeError, match=msg):
          datetime_series[[5, slice(None, None)]]
      with pytest.raises(TypeError, match=msg):
          datetime_series[[5, slice(None, None)]] = 2
  
  
  @pytest.mark.parametrize("tz", ["US/Eastern", "UTC", "Asia/Tokyo"])
  def test_setitem_with_tz(tz):
      orig = Series(pd.date_range("2016-01-01", freq="H", periods=3, tz=tz))
      assert orig.dtype == f"datetime64[ns, {tz}]"
  
      # scalar
      s = orig.copy()
      s[1] = Timestamp("2011-01-01", tz=tz)
      exp = Series(
          [
              Timestamp("2016-01-01 00:00", tz=tz),
              Timestamp("2011-01-01 00:00", tz=tz),
              Timestamp("2016-01-01 02:00", tz=tz),
          ]
      )
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.loc[1] = Timestamp("2011-01-01", tz=tz)
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.iloc[1] = Timestamp("2011-01-01", tz=tz)
      tm.assert_series_equal(s, exp)
  
      # vector
      vals = Series(
          [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)],
          index=[1, 2],
      )
      assert vals.dtype == f"datetime64[ns, {tz}]"
  
      s[[1, 2]] = vals
      exp = Series(
          [
              Timestamp("2016-01-01 00:00", tz=tz),
              Timestamp("2011-01-01 00:00", tz=tz),
              Timestamp("2012-01-01 00:00", tz=tz),
          ]
      )
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.loc[[1, 2]] = vals
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.iloc[[1, 2]] = vals
      tm.assert_series_equal(s, exp)
  
  
  def test_setitem_with_tz_dst():
      # GH XXX TODO: fill in GH ref
      tz = "US/Eastern"
      orig = Series(pd.date_range("2016-11-06", freq="H", periods=3, tz=tz))
      assert orig.dtype == f"datetime64[ns, {tz}]"
  
      # scalar
      s = orig.copy()
      s[1] = Timestamp("2011-01-01", tz=tz)
      exp = Series(
          [
              Timestamp("2016-11-06 00:00-04:00", tz=tz),
              Timestamp("2011-01-01 00:00-05:00", tz=tz),
              Timestamp("2016-11-06 01:00-05:00", tz=tz),
          ]
      )
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.loc[1] = Timestamp("2011-01-01", tz=tz)
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.iloc[1] = Timestamp("2011-01-01", tz=tz)
      tm.assert_series_equal(s, exp)
  
      # vector
      vals = Series(
          [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)],
          index=[1, 2],
      )
      assert vals.dtype == f"datetime64[ns, {tz}]"
  
      s[[1, 2]] = vals
      exp = Series(
          [
              Timestamp("2016-11-06 00:00", tz=tz),
              Timestamp("2011-01-01 00:00", tz=tz),
              Timestamp("2012-01-01 00:00", tz=tz),
          ]
      )
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.loc[[1, 2]] = vals
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.iloc[[1, 2]] = vals
      tm.assert_series_equal(s, exp)
  
  
  def test_categorical_assigning_ops():
      orig = Series(Categorical(["b", "b"], categories=["a", "b"]))
      s = orig.copy()
      s[:] = "a"
      exp = Series(Categorical(["a", "a"], categories=["a", "b"]))
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s[1] = "a"
      exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s[s.index > 0] = "a"
      exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s[[False, True]] = "a"
      exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
      tm.assert_series_equal(s, exp)
  
      s = orig.copy()
      s.index = ["x", "y"]
      s["y"] = "a"
      exp = Series(Categorical(["b", "a"], categories=["a", "b"]), index=["x", "y"])
      tm.assert_series_equal(s, exp)
  
      # ensure that one can set something to np.nan
      s = Series(Categorical([1, 2, 3]))
      exp = Series(Categorical([1, np.nan, 3], categories=[1, 2, 3]))
      s[1] = np.nan
      tm.assert_series_equal(s, exp)
  
  
  def test_getitem_categorical_str():
      # GH#31765
      ser = Series(range(5), index=Categorical(["a", "b", "c", "a", "b"]))
      result = ser["a"]
      expected = ser.iloc[[0, 3]]
      tm.assert_series_equal(result, expected)
  
      # Check the intermediate steps work as expected
      with tm.assert_produces_warning(FutureWarning):
          result = ser.index.get_value(ser, "a")
      tm.assert_series_equal(result, expected)
  
  
  def test_slice(string_series, object_series):
      numSlice = string_series[10:20]
      numSliceEnd = string_series[-10:]
      objSlice = object_series[10:20]
  
      assert string_series.index[9] not in numSlice.index
      assert object_series.index[9] not in objSlice.index
  
      assert len(numSlice) == len(numSlice.index)
      assert string_series[numSlice.index[0]] == numSlice[numSlice.index[0]]
  
      assert numSlice.index[1] == string_series.index[11]
      assert tm.equalContents(numSliceEnd, np.array(string_series)[-10:])
  
      # Test return view.
      sl = string_series[10:20]
      sl[:] = 0
  
      assert (string_series[10:20] == 0).all()
  
  
  def test_slice_can_reorder_not_uniquely_indexed():
      s = Series(1, index=["a", "a", "b", "b", "c"])
      s[::-1]  # it works!
  
  
  def test_loc_setitem(string_series):
      inds = string_series.index[[3, 4, 7]]
  
      result = string_series.copy()
      result.loc[inds] = 5
  
      expected = string_series.copy()
      expected[[3, 4, 7]] = 5
      tm.assert_series_equal(result, expected)
  
      result.iloc[5:10] = 10
      expected[5:10] = 10
      tm.assert_series_equal(result, expected)
  
      # set slice with indices
      d1, d2 = string_series.index[[5, 15]]
      result.loc[d1:d2] = 6
      expected[5:16] = 6  # because it's inclusive
      tm.assert_series_equal(result, expected)
  
      # set index value
      string_series.loc[d1] = 4
      string_series.loc[d2] = 6
      assert string_series[d1] == 4
      assert string_series[d2] == 6
  
  
  def test_timedelta_assignment():
      # GH 8209
      s = Series([], dtype=object)
      s.loc["B"] = timedelta(1)
      tm.assert_series_equal(s, Series(Timedelta("1 days"), index=["B"]))
  
      s = s.reindex(s.index.insert(0, "A"))
      tm.assert_series_equal(s, Series([np.nan, Timedelta("1 days")], index=["A", "B"]))
  
      s.loc["A"] = timedelta(1)
      expected = Series(Timedelta("1 days"), index=["A", "B"])
      tm.assert_series_equal(s, expected)
  
      # GH 14155
      s = Series(10 * [np.timedelta64(10, "m")])
      s.loc[[1, 2, 3]] = np.timedelta64(20, "m")
      expected = Series(10 * [np.timedelta64(10, "m")])
      expected.loc[[1, 2, 3]] = Timedelta(np.timedelta64(20, "m"))
      tm.assert_series_equal(s, expected)
  
  
  @pytest.mark.parametrize(
      "nat_val,should_cast",
      [
          (pd.NaT, True),
          (np.timedelta64("NaT", "ns"), False),
          (np.datetime64("NaT", "ns"), True),
      ],
  )
  @pytest.mark.parametrize("tz", [None, "UTC"])
  def test_dt64_series_assign_nat(nat_val, should_cast, tz):
      # some nat-like values should be cast to datetime64 when inserting
      #  into a datetime64 series.  Others should coerce to object
      #  and retain their dtypes.
      dti = pd.date_range("2016-01-01", periods=3, tz=tz)
      base = Series(dti)
      expected = Series([pd.NaT] + list(dti[1:]), dtype=dti.dtype)
      if not should_cast:
          expected = expected.astype(object)
  
      ser = base.copy(deep=True)
      ser[0] = nat_val
      tm.assert_series_equal(ser, expected)
  
      ser = base.copy(deep=True)
      ser.loc[0] = nat_val
      tm.assert_series_equal(ser, expected)
  
      ser = base.copy(deep=True)
      ser.iloc[0] = nat_val
      tm.assert_series_equal(ser, expected)
  
  
  @pytest.mark.parametrize(
      "nat_val,should_cast",
      [
          (pd.NaT, True),
          (np.timedelta64("NaT", "ns"), True),
          (np.datetime64("NaT", "ns"), False),
      ],
  )
  def test_td64_series_assign_nat(nat_val, should_cast):
      # some nat-like values should be cast to timedelta64 when inserting
      #  into a timedelta64 series.  Others should coerce to object
      #  and retain their dtypes.
      base = Series([0, 1, 2], dtype="m8[ns]")
      expected = Series([pd.NaT, 1, 2], dtype="m8[ns]")
      if not should_cast:
          expected = expected.astype(object)
  
      ser = base.copy(deep=True)
      ser[0] = nat_val
      tm.assert_series_equal(ser, expected)
  
      ser = base.copy(deep=True)
      ser.loc[0] = nat_val
      tm.assert_series_equal(ser, expected)
  
      ser = base.copy(deep=True)
      ser.iloc[0] = nat_val
      tm.assert_series_equal(ser, expected)
  
  
  @pytest.mark.parametrize(
      "td",
      [
          Timedelta("9 days"),
          Timedelta("9 days").to_timedelta64(),
          Timedelta("9 days").to_pytimedelta(),
      ],
  )
  def test_append_timedelta_does_not_cast(td):
      # GH#22717 inserting a Timedelta should _not_ cast to int64
      expected = Series(["x", td], index=[0, "td"], dtype=object)
  
      ser = Series(["x"])
      ser["td"] = td
      tm.assert_series_equal(ser, expected)
      assert isinstance(ser["td"], Timedelta)
  
      ser = Series(["x"])
      ser.loc["td"] = Timedelta("9 days")
      tm.assert_series_equal(ser, expected)
      assert isinstance(ser["td"], Timedelta)
  
  
  def test_underlying_data_conversion():
      # GH 4080
      df = DataFrame({c: [1, 2, 3] for c in ["a", "b", "c"]})
      return_value = df.set_index(["a", "b", "c"], inplace=True)
      assert return_value is None
      s = Series([1], index=[(2, 2, 2)])
      df["val"] = 0
      df
      df["val"].update(s)
  
      expected = DataFrame(
          {"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3], "val": [0, 1, 0]}
      )
      return_value = expected.set_index(["a", "b", "c"], inplace=True)
      assert return_value is None
      tm.assert_frame_equal(df, expected)
  
      # GH 3970
      # these are chained assignments as well
      pd.set_option("chained_assignment", None)
      df = DataFrame({"aa": range(5), "bb": [2.2] * 5})
      df["cc"] = 0.0
  
      ck = [True] * len(df)
  
      df["bb"].iloc[0] = 0.13
  
      # TODO: unused
      df_tmp = df.iloc[ck]  # noqa
  
      df["bb"].iloc[0] = 0.15
      assert df["bb"].iloc[0] == 0.15
      pd.set_option("chained_assignment", "raise")
  
      # GH 3217
      df = DataFrame({"a": [1, 3], "b": [np.nan, 2]})
      df["c"] = np.nan
      df["c"].update(Series(["foo"], index=[0]))
  
      expected = DataFrame({"a": [1, 3], "b": [np.nan, 2], "c": ["foo", np.nan]})
      tm.assert_frame_equal(df, expected)
  
  
  def test_preserve_refs(datetime_series):
      seq = datetime_series[[5, 10, 15]]
      seq[1] = np.NaN
      assert not np.isnan(datetime_series[10])
  
  
  def test_cast_on_putmask():
      # GH 2746
  
      # need to upcast
      s = Series([1, 2], index=[1, 2], dtype="int64")
      s[[True, False]] = Series([0], index=[1], dtype="int64")
      expected = Series([0, 2], index=[1, 2], dtype="int64")
  
      tm.assert_series_equal(s, expected)
  
  
  def test_type_promote_putmask():
      # GH8387: test that changing types does not break alignment
      ts = Series(np.random.randn(100), index=np.arange(100, 0, -1)).round(5)
      left, mask = ts.copy(), ts > 0
      right = ts[mask].copy().map(str)
      left[mask] = right
      tm.assert_series_equal(left, ts.map(lambda t: str(t) if t > 0 else t))
  
      s = Series([0, 1, 2, 0])
      mask = s > 0
      s2 = s[mask].map(str)
      s[mask] = s2
      tm.assert_series_equal(s, Series([0, "1", "2", 0]))
  
      s = Series([0, "foo", "bar", 0])
      mask = Series([False, True, True, False])
      s2 = s[mask]
      s[mask] = s2
      tm.assert_series_equal(s, Series([0, "foo", "bar", 0]))
  
  
  def test_multilevel_preserve_name():
      index = MultiIndex(
          levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
          codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
          names=["first", "second"],
      )
      s = Series(np.random.randn(len(index)), index=index, name="sth")
  
      result = s["foo"]
      result2 = s.loc["foo"]
      assert result.name == s.name
      assert result2.name == s.name
  
  
  """
  miscellaneous methods
  """
  
  
  def test_uint_drop(any_int_dtype):
      # see GH18311
      # assigning series.loc[0] = 4 changed series.dtype to int
      series = Series([1, 2, 3], dtype=any_int_dtype)
      series.loc[0] = 4
      expected = Series([4, 2, 3], dtype=any_int_dtype)
      tm.assert_series_equal(series, expected)
  
  
  def test_getitem_unrecognized_scalar():
      # GH#32684 a scalar key that is not recognized by lib.is_scalar
  
      # a series that might be produced via `frame.dtypes`
      ser = Series([1, 2], index=[np.dtype("O"), np.dtype("i8")])
  
      key = ser.index[1]
  
      result = ser[key]
      assert result == 2
  
  
  @pytest.mark.parametrize(
      "index",
      [
          date_range("2014-01-01", periods=20, freq="MS"),
          period_range("2014-01", periods=20, freq="M"),
          timedelta_range("0", periods=20, freq="H"),
      ],
  )
  def test_slice_with_zero_step_raises(index):
      ts = Series(np.arange(20), index)
  
      with pytest.raises(ValueError, match="slice step cannot be zero"):
          ts[::0]
      with pytest.raises(ValueError, match="slice step cannot be zero"):
          ts.loc[::0]
      with pytest.raises(ValueError, match="slice step cannot be zero"):
          ts.iloc[::0]
  
  
  @pytest.mark.parametrize(
      "index",
      [
          date_range("2014-01-01", periods=20, freq="MS"),
          period_range("2014-01", periods=20, freq="M"),
          timedelta_range("0", periods=20, freq="H"),
      ],
  )
  def test_slice_with_negative_step(index):
      def assert_slices_equivalent(l_slc, i_slc):
          expected = ts.iloc[i_slc]
  
          tm.assert_series_equal(ts[l_slc], expected)
          tm.assert_series_equal(ts.loc[l_slc], expected)
          tm.assert_series_equal(ts.loc[l_slc], expected)
  
      keystr1 = str(index[9])
      keystr2 = str(index[13])
      box = type(index[0])
  
      ts = Series(np.arange(20), index)
      SLC = IndexSlice
  
      for key in [keystr1, box(keystr1)]:
          assert_slices_equivalent(SLC[key::-1], SLC[9::-1])
          assert_slices_equivalent(SLC[:key:-1], SLC[:8:-1])
  
          for key2 in [keystr2, box(keystr2)]:
              assert_slices_equivalent(SLC[key2:key:-1], SLC[13:8:-1])
              assert_slices_equivalent(SLC[key:key2:-1], SLC[0:0:-1])
  
  
  def test_tuple_index():
      # GH 35534 - Selecting values when a Series has an Index of tuples
      s = Series([1, 2], index=[("a",), ("b",)])
      assert s[("a",)] == 1
      assert s[("b",)] == 2
      s[("b",)] = 3
      assert s[("b",)] == 3
  
  
  def test_frozenset_index():
      # GH35747 - Selecting values when a Series has an Index of frozenset
      idx0, idx1 = frozenset("a"), frozenset("b")
      s = Series([1, 2], index=[idx0, idx1])
      assert s[idx0] == 1
      assert s[idx1] == 2
      s[idx1] = 3
      assert s[idx1] == 3
  